Imagine a team of AI agents building, deploying, and running software at machine speed—yet unable to see what’s happening in production. This is the new reality for enterprise technology leaders. As one Fortune 500 CTO told us, “Speed is now the primary driver of innovation, forcing organizations to rethink processes, compliance, and roles; it’s a necessity for innovation teams.”

Observability—real-time visibility into how software behaves in production—has become the critical enabler for both human-led and agent-led teams. Without it, AI agents are powerful but blind.

Key executive insights

1. Software production is being redefined by AI agents. This transformation is a structural shift, not a trend.
2. The world is bimodal again. Human-led and agent-led environments coexist.
3. AI agents are powerful but blind. Without rich context from production, they cannot deliver reliably.
4. Observability is a crucial enabler to gradually transform from human-led to agent-led operations. Observability is what allows organizations to industrialize software delivery with confidence.
5. The new KPI for agent-led teams is the percentage of human intervention required. The lower the number, the better the AI is working.

The market reality: A bimodal world

Organizations are accelerating AI adoption not because it is trendy, but because it is existential. Companies that fail to transform risk being outpaced by competitors that can deliver software faster, cheaper, and at higher quality. CTOs and CIOs are making statements like “speed over compliance” not out of recklessness, but because they recognize that without radical acceleration, their businesses face disruption.

At the frontier of this shift is a fundamentally new way of building software: AI-first development. In these environments, 100% of coding, testing, deployment, operations, bug fixing, and optimization are performed by AI agents. The human role shifts to specification, goal setting, supervision, and correction. Intellectual property moves from the code to the specification—code becomes a generated artifact, not the source of truth. With a complete, well-architected spec, agents can fully rebuild the software from it again.

This creates a bimodal operating environment:

• Human-led teams—the majority today—are existing operations, SREs, and developers augmenting their workflows with AI. They follow the traditional SDLC, increasingly supported by AI agents that auto-prevent, auto-remediate, and auto-optimize, which reduces manual effort and achieves more with the same resources.
• Agent-led teams—growing fast—are innovation groups operating in full AI development life cycle (AIDLC) mode. Swarms of AI agents build, deploy, and run software end-to-end. Humans write specifications and intent, not code. For these teams, the KPI is no longer “how many story points were solved?” but “what percentage of human intervention is required?”

Observability enables a reliable transition to autonomous operations

In the early 2010s, a similar bimodal pattern emerged with cloud: one team running thousands of servers on-premises, another in stealth mode on AWS. The pattern is repeating now with AI.

Why not switch everything to agent-led right away? Because existing systems follow processes, compliance, and technology stacks that can’t be immediately automated in an AI-first way. Moreover, it’s too risky to move all business-critical systems simultaneously. The safer path: start with an innovation team, build less critical applications first, and only when those are successful and trusted, begin migrating more of the business-critical services.

New foundation models that arrived in early 2026 have accelerated the path to fully autonomous operations, making agent-led teams realistic at small scale today, with large scale within sight. These systems focus on AI-first software generation first, with a clear goal to eventually master operational challenges (resilience, performance, scale, security) entirely with agents as well.

Observability plays a critical role not only in making both modes work reliably, but also in enabling the transformation from the first mode to the second. The context observability provides—understanding existing system behavior, dependencies, and requirements—is exactly what agents need to create the reliable and scalable software. Observability is what makes both modes work, and it is the critical bridge between them.

The core problem: AI agents are blind

AI agents can code, deploy, refactor, and operate software faster than humans ever could. But there is one thing AI cannot do without help: AI has no awareness of what happens in production. It’s blind to the real world: without real-time feedback from running software—in development and production —agents make decisions without context and without understanding their consequences. They operate at speed, but without sight.

77% of IT teams still lack full visibility across hybrid environments (IBM Institute for Business Value, 2025). If you can’t see it, you can’t scale it. Observability is not optional for AI-first operations, it’s a prerequisite.

The Dynatrace response: Real-time observability for both worlds

Dynatrace addresses both sides of this bimodal reality: a complementary response to the two speeds at which enterprises now operate.

For human-led teams: Autonomous operations at scale

This year, Dynatrace launched Dynatrace Intelligence: a full agentic operations system that orchestrates dozens of agents that auto-prevent, auto-remediate, and auto-optimize across site reliability, development, and application security. These AI agents deliver the following value in production:

• SRE Agent: Kubernetes troubleshooting, infrastructure optimization, and automated incident resolution – reducing mean time to resolution at scale.
• Developer Agent: Surfaces production context during deployment, validates changes, and prevents issues before they reach customers.
• Security Agent: Identifies vulnerabilities, triages threats, and accelerates security response, all in real time.

The deterministic foundation underneath: what separates Dynatrace agents from others is its deterministic foundation: real-time, full-stack, and cross-model root-cause analysis, anomaly detection, and forecasting, all grounded by data in a unified, purpose-built data lakehouse that delivers accurate, contextual answers from exabytes of information. This is not AI that guesses; it’s AI that reasons from facts. Benchmarks from internal testing and observed customer use cases: 12× higher success rate in SRE use cases, 3× faster problem resolution, 2.5× lower token cost.

Ecosystem integrations that extend intelligence beyond the platform: Dynatrace Intelligence extends into third-party tools to drive autonomous actions across development, SRE, and ITOps workflows.

For agent-led teams: develop and run software reliably

Dynatrace enables AI-first teams to let swarms of agents to build and run software reliably, providing real-world awareness from observability, run-time context across development, security, and operations, and self-optimization toward SLAs, cost, and resilience. Key capabilities include:

• Agentic observability: Closed-loop autonomous operations where observability agents coordinate with coding and deployment agents to self-heal.
• AI and cloud observability: Full-stack visibility across cloud infrastructure and AI workloads, covering resilience, performance, security, user experience, and LLM evaluations to assess the quality and reliability of agent outputs, helping identify potential inaccuracies, hallucinations, or risks.
• AI data lakehouse (Grail): Real-time context engine that provides long-term memory for agent decisions—sub-second, API-native, at an exabyte scale.

The goal: a closed loop where agents detect issues, resolve them, and ship the fix—autonomously, 24/7.

Dynatrace is on the same bimodal journey – our entire business runs on Dynatrace Intelligence in human-led mode, with agents taking over more tasks continuously, while our AI-first offering and new services are built and operated entirely by agent swarms, using our own observability to close the feedback loop.

Different approaches – unified platform

Across the platform, Dynatrace delivers end-to-end, full-stack visibility across cloud infrastructure, applications, and AI workloads, including agent behavior, decision paths, and cost, along with governance at machine scale. These capabilities serve human-led and agent-led teams differently, but from the same unified platform.

The measure of success in software delivery is shifting from human productivity metrics to a new KPI: the percentage of human intervention required. Observability is what makes that progress possible. The question for every technology leader is no longer whether to adopt AI-first, but how quickly they can close the visibility gap before competitors do to drive massive growth in innovation and productivity.

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Executives are looking for successful ways to run their digital ecosystems with AI as cloud and AI adoption reach unprecedented complexity. Organizations are increasingly recognizing that agentic AI on its own can’t deliver the consistent, trustworthy outcomes they expect. With 65% of enterprises investing in AI‑driven monitoring and automation, leaders now need trustworthy AI‑powered observability to shift from human‑driven operations to human‑supervised, autonomous digital ecosystems.

Key executive insights

• Alongside the rapid adoption of agentic AI, Dynatrace is uniquely architected for powering real‑time autonomous operations across organizations’ digital systems while also integrating seamlessly into broader agentic ecosystems.
• Dynatrace – pioneer of large-scale AI-powered root cause analysis – established predictive operations and now further redefines observability, taking the next step from automation to autonomous action by auto-remediating, auto-preventing and auto-optimizing.
• Dynatrace takes the guesswork out of AI by optimizing the balance between deterministic AI, contextual analytics, and stochastic AI to drive precise agentic answers and reliable actions.
• Dynatrace Intelligence is an agentic operations system in the Dynatrace platform, driving autonomous actions through orchestrating ready-made Dynatrace agents as well as external ecosystem agents.
• AI engineering, AI operations, agentic SRE get enabled by Dynatrace with real-time production feedback-loops from trusted AI.

Minimizing hallucinations and avoiding large language model data processing limits

One of the biggest fears of executives who build agentic frameworks is that generative AI can hallucinate and push their agents off course. CTOs also prioritize ensuring agentic systems have instant access to high‑quality information, so multi‑step agent workflows can execute quickly and reliably.

Hallucinations aren’t minor errors – they can trigger wrong actions leading to outages, security risks, and financial exposure. To make it worse, in agentic processing inaccuracies can accumulate and amplify.

The Dynatrace AI approach reduces the risk of hallucinations by maximizing the use of deterministic AI in its agents, allowing Dynatrace to deliver responses based on real-time data rather than probabilistic guesses. Also, Dynatrace continuously observes, maintains, and enriches this context through real-time dependency graphs, real-world context, and high-performance data lakehouse analytics—fueling precise analytics and real-time answers across the entire digital services and business landscape.

This level of contextual precision is critical because large language models cannot directly process petabytes of heterogeneous observability data. Their context windows are limited, and performance often degrades as input approaches the maximum length.

Dumping large amounts of data into AI requests can reduce quality. Models have finite context windows and can underweight important details in very long prompts. Curating and structuring only the most relevant information generally yield better results than providing everything at once.

To overcome these constraints, it’s essential to rapidly distill vast amounts of data into short, high-quality context—this is where contextual analytics, dependency graphs, and the AI-optimized data lakehouse Grail become critical differentiators.

Real-time analytics with instant visualization of dependencies and interactions

Agentic AI depends not only on the model it runs on but on the quality, context, and immediacy of the data it receives—real‑time, fact‑based inputs are essential to keep pace with agentic decision‑making.

Grail, the Dynatrace data lakehouse, provides this foundation by unifying observability, security, and business data at an exabyte scale. Its schema‑on‑read approach removes indexing overhead and enables any‑question, any‑time analytics of Grail data. Grail processes metrics, logs, traces, user behavior, security events, and business signals alongside directed dependency graphs, delivering deep insights instantly through zero‑latency, always-hydrated storage.

Complementing this, Dynatrace Smartscape continuously refines a real-time dependency graph of real-world dependencies across business, teams, digital services, processes, infrastructure, risks, and more. By dynamically uncovering both vertical and horizontal dependencies, Smartscape enables teams to understand how systems, business processes, and organizational structures interact. The latest generation of Smartscape real-time dependency graph can now also be augmented with custom entities, such as business data types, ownership information, and other meta-data and ownership details, so teams immediately know who to notify for fast, fact-based remediation.

Together, Grail and Smartscape provide the technical foundation for Dynatrace Intelligence to let agentic AI act on facts, not guesses, ensuring that AI-powered decisions are accurate, scalable, and actionable, a pre-requisite for reliable autonomous operations.

Dynatrace Intelligence

Dynatrace Intelligence is an agentic operations system at the core of the Dynatrace platform that fuses deterministic AI with agentic AI to drive a new level of reliability across observability and autonomous operations. It provides a unified intelligence layer where humans define the goals, and AI executes them with precision—guided by policies, context, and guardrails.

At the foundation are deterministic agents that create a highly reliable operational core. The Root Cause Agent, powered by deterministic causal AI, delivers answers with far greater speed and precision than LLM‑only approaches. The Analytics Agent distills petabytes of Grail data lakehouse data into concise, contextual intelligence, while the Forecasting Agent scales predictive capabilities across the environment. An Operator Agent oversees, orchestrates, and coordinates agentic team efforts to ensure optimal outcomes. These foundational agents power every other agent operating on the platform.

Building on this foundation are ready‑made, domain‑specific agents designed to extend and augment the work of Development, SRE, and Security teams. This fusion of deterministic AI, agentic AI, and specialized domain agents enables Dynatrace Intelligence to detect anomalies, predict issues, identify root causes, run complementary investigations, and plan and execute corrective actions—ultimately enabling auto‑prevention, auto‑remediation, and auto‑optimization.

Dynatrace already observes customers’ digital services, end‑user experiences, and AI stacks automatically, making these domain agents immediately impactful. As examples, for developers, Dynatrace detects rising mobile‑app crashes, analyzes the code paths, and produces an immediate fix suggestion—turning what used to take hours into seconds. For security teams, Dynatrace continuously monitors emerging threats and instantly checks the environment for related vulnerabilities or indicators of compromise, helping teams respond proactively before attackers can act.

New Assist Agents simplify the adoption and everyday use of Dynatrace, while Agentic Workflows empower customers to build their own agents.

Dynatrace Intelligence is here not only to remediate symptoms—like a production infrastructure overload—but also to detect the underlying root cause and generate actionable plans to fix the source of the issue.

AI-powered observability from Dynatrace enables the next generation software delivery life cycle process with AI engineering, AI operations, agentic SRE, and AI business analytics, through real-time facts from production systems.

Already today, Dynatrace Intelligence agents collectively power a wide range of real‑world use cases, from mobile app crash inspection and front‑end error explanation to infrastructure optimization, Kubernetes operations, and security‑context insights. They also accelerate tasks like anomaly and log‑pattern analysis, vulnerability validation, timeseries analysis, and even dashboard creation, with extensible workflows that let teams expand these capabilities as their needs grow.

Dynatrace Intelligence also coordinates a bi-directional interaction with the agentic ecosystem like AWS Kiro, GitHub Copilot, ServiceNow, Azure SRE agent, Atlassian Rovo and many others – e.g. submitting tickets, invoking a coding agent, assessing the risk of a new deployment, adjusting infrastructure, informing business workflows. It also responds when other systems request, for example, incident details, performance insights, business impact analysis, or resilience risk assessments.

Along with Dynatrace Intelligence, Dynatrace leads customers on a journey toward fully autonomous operations.

Journey to fully autonomous operations

Each organization goes through their own maturity stages and pace on the journey to autonomous operation; for the majority it can look like this:

Automated – a stage when an organization’s digital system executes pre‑defined workflows and actions automatically (based on AI‑generated answers) to support both reactive and predictive operations. I can say that currently, many organizations are striving to get to this stage or are already in it.

Digital systems must be automatable and observable. To validate this, workflows must be testable:

• Can you automate it?
• Can you observe it?
• Can you understand its behavior in real time?

Organizations don’t need to automate every single workflow. Once it’s possible to confirm that key use cases are both automatable and observable, organizations can progress to the next maturity stage.

Supervised Autonomous – in this stage, AI generates execution‑ready action plans with clear reasoning and acts only after human oversight and approval. In a “crawl‑walk‑run” approach, organizations start with small, repetitive tasks that require agentic AI instead of hard-coded workflows.

Key principles in evaluating ability for supervised autonomous operations:

• Reliability: For reliable decisions and actions, rely as much as possible on deterministic AI and analytics, and leverage generative AI for common sense and learned expertise.
• Transparency: Allow people to set goals and guardrails for AI, validate reasoning, review knowledge graphs, improve real-time feedback loops, and assess explanations to build trust.
• Feedback loop: Agentic AI relies on accurate factual inputs to create actionable plans, as well as precise real-time feedback to investigate plan details, refine them and validate execution.

Once the digital system consistently performs with human‑like review discipline, the organization is ready to move toward fully autonomous operations.

Fully Autonomous – in the future, fully autonomous stage, Dynatrace Intelligence acts independently to fulfill business goals and autonomously operates a wide range of aspects in successfully delivering software that end-users expect, requesting human input only when necessary. As much as Dynatrace uses AI to observe other AI within the cloud- and AI-native services customers run, it also continuously observes itself—to self-optimize, ensure compliance, and provide insights that help people set the goals. People still play a crucial role: they review outcomes, adjust goals, and refine instructions. As a result, organizations deliver and operate software with higher resilience, happier customers, and lower cost.

The fusion of deterministic AI and agentic AI sets Dynatrace apart by providing a reliable agentic AI-powered observability. It is an AI that observes other AI and helps organizations to build more resilient applications and better customer experiences.

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Digital systems will continue to grow in scale and complexity in 2026, driven by the rapid adoption of agentic AI, unified telemetry, and cloud-native delivery models. These shifts will influence how organizations understand system behavior, prepare for autonomy, and maintain reliability in environments that change in real time. The insights that follow highlight the trends executives should monitor most closely, along with the conditions that will determine whether AI-driven operations deliver reliable, transparent, and resilient outcomes.

Key insights for executives

• Complexity will surge with agentic AI: Digital ecosystems are already complex, but agentic systems are introducing an exponential leap. Each new agent brings its own logic, behavior, and interactions, often acting independently, sometimes unpredictably. Without visibility into how agents interact or what decisions they make, organizations risk losing control over their systems. Guardrails, oversight, and end-to-end observability will be essential to avoid chaos and maintain reliability as the complexity of this new AI layer accelerates system behavior and reshapes the digital environment.
• Autonomous operations will depend on maturity, not ambition: Organizations will not move directly to full autonomy. They will progress through preventive operations and recommendation-driven workflows before adopting supervised autonomy, the final step before full autonomy. AI-assisted automation is where the foundation is built, because this stage forces organizations to expose and harden the services, data sources, and contextual signals that AI depends on. Autonomy is only possible when these components are accessible in real time, performant, and understood in context. With this foundation in place, supervised autonomy will reliably prepare the environment for full autonomous operations.
• Resilience will become a primary measure of digital operations: Customers expect systems to remain available and secure even under stress, and leaders will treat reliability and security as a single requirement. Early detection and rapid recovery will be essential, because failures spread faster across these interconnected systems. As a result, organizations will need unified visibility to protect customer experience and revenue.
• Reliable AI requires strong deterministic foundations: AI can only act dependably when its inputs are accurate, contextual, right-sized, and correctly interpreted. High-quality information must be available in real time and understood in a context-aware view of the broader system. Because large language models can’t reason over raw telemetry at scale, enterprises need mechanisms that distill massive data streams into concise, meaningful context and graph-based representations that show how systems and signals relate. Leaders should prioritize data quality, contextual integrity, and correct interpretation to ensure AI decisions remain reliable and useful.
• Human supervision will remain essential in AI-enabled operations: AI will take on more execution, but humans will continue to set goals, define boundaries, and ensure accountability. Leaders should redesign roles so that human judgment guides the system while AI handles repeatable or time-sensitive tasks.
• AI will become a standard component of newly developed digital services: AI workloads, pipelines, and operational practices will merge with existing cloud development processes, and executives should prepare for closer alignment among AI engineering, platform, SRE, and security teams to support consistent reliability and performance.

Prediction 1: Agentic AI triggers a new era of system complexity

Agentic AI is introducing a new level of system interaction. It’s more powerful, but exponentially harder to manage. As agents coordinate tasks, exchange context, and trigger downstream actions, even well-architected digital environments can spiral into unpredictable behavior. Most organizations are not ready for this shift. Without strong observability and consistent governance, these systems will become increasingly difficult to understand and control.

Think of each AI agent acting autonomously based on instructions and input from not only humans but plenty of first-and third-party agents. A single customer interaction might set off hundreds of background conversations among agents, each taking its own initiative. Roles shift depending on the situation, and some agents may direct others.

Common scenarios show how this plays out. When a vehicle detects an issue, task-specialized agents may check customer information, evaluate service options, estimate timelines, and coordinate a resolution. A travel assistance agent might do something similar, reaching out to agents that compare flights, check loyalty benefits, book transportation, and adjust plans in real time. In both cases, many agents work behind the scenes toward a single outcome, and the interactions between them can multiply in unpredictable ways. Every agent still reports to a human or another agent, and accountability remains with human supervision. This exponential growth in agent-to-agent communication can’t be managed without observability.

Organizations that adopt agentic AI without unified context and clear guardrails will face escalating costs, unpredictable behavior, and higher risk. The challenge is not just improving individual models, but managing the web of autonomous interactions that unfold in real time. In this next phase, observability is no longer a support function: It becomes the foundation for safe, scalable, and governable agentic ecosystems.

Prediction 2: The path to autonomous operations requires several maturity steps

Enterprises will take meaningful steps toward autonomous operations. Maturity, not ambition, will determine who succeeds. AI cannot act independently until the underlying systems, automation, and processes are stable, observable, and well-understood. Agentic systems are coming, but first, the groundwork must be solid. Earlier stages of automation are essential, because they surface the gaps in data access, service performance, and contextual signals that AI depend on. Only after those components are reliable and available in real time will supervised and autonomous operations take hold.

Most enterprises will follow a progression: they will start by ensuring their digital systems are fully automated, with runbooks, APIs, and interfaces in place to support reliable execution. This foundation enables predictive operations, where issues can be identified and remediated before they affect end users. From there, organizations can introduce supervised autonomous operations, using agentic automation with human oversight to build confidence and operational trust. As maturity increases and these systems consistently perform as expected, enterprises can progress naturally toward fully autonomous operations.

The journey toward fully autonomous operations will be gradual. Organizations that invest now in preventive workflows and recommendation-driven automation will be best positioned to introduce autonomous capabilities safely and responsibly.

Prediction 3: Resilience becomes the new benchmark for operational excellence

Resilience will become the defining measure of digital performance. As systems become more distributed and interconnected, small faults can spread quickly across applications, cloud regions, payment systems, and third-party services. Leaders won’t treat reliability, availability, security, and observability as separate practices. They will view them as a single requirement: the ability of a system to absorb disruption, recover quickly, and maintain a consistent customer experience under stress.

Independent research we commissioned with FreedomPay shows why this shift is accelerating. The findings reveal how fragile digital ecosystems have become and how quickly technical failures turn into customer disruption and financial loss. In the United Kingdom, payment outages put an estimated £1.6 billion in annual revenue at risk. In France, the figure rises to €1.9 billion. A single service issue can ripple across connected systems and channels, showing how tightly coupled modern operations have become.

Customers feel these failures immediately. Patience begins to drop within the first few minutes, and many customers leave the transaction if the issue persists for more than fifteen minutes. Yet the average outage lasts more than an hour, which means most of the damage has already occurred. Nearly one in three customers say a single incident is enough to reduce their trust in a business, with younger digital native consumers even more likely to leave.

This environment requires a unified approach to resilience. Organizations need shared visibility into how services behave, how failures propagate, and how recovery affects the customer journey. Resilience will be measured by how systems respond under stress, not just how they perform when digital services run as expected.

Prediction 4: Reliability becomes the foundation of AI progress

Organizations will prioritize building foundations that make AI systems consistently reliable. The next phase of AI progress will depend as much on deterministic grounding and factual signals as on the generative power of stochastic models. Enterprises are recognizing that creativity alone is insufficient. Reliable AI requires both structured inputs and mechanisms that ensure outputs remain trustworthy.

Agentic systems add a new layer of complexity. As agents coordinate tasks, exchange context, and initiate downstream actions, even a small misunderstanding can propagate across the system. Greater capability amplifies this effect because a powerful agent can accelerate outcomes while also accelerating an error. This is how hallucination emerges at system scale—not from a single faulty model, but from inaccuracies that compound across agent interactions. Deterministic grounding and end-to-end observability prevent that inaccuracy by ensuring agents act on the same factual signals and remain accountable to the human operator.

A common scenario shows what this looks like. A vehicle detecting a problem may trigger agents that review customer data, vehicle status information, identify service locations, evaluate schedules, estimate travel time, and plan the full resolution workflow. In each case, many agents collaborate behind the scenes to produce a single outcome. Organizations that want transparent and dependable AI outcomes will prioritize deterministic guardrails, enabling agentic systems to behave safely, act predictably, and collaborate with clarity.

Prediction 5: AI will scale, but human supervision will remain essential

In the next year, agentic AI growth will lead to a new operating model where humans define goals, and AI performs well-defined execution. As systems gain more context and become capable of coordinated action, the human role will shift from performing tasks to setting direction, providing instructions, and ensuring oversight. Organizations will rely on AI to analyze relationships, identify risks, and initiate safe actions, while humans remain accountable for outcomes and cross-domain judgment.

Agentic AI will behave much like a high-speed intern. When given clear goals, good tools, and instructions, and the right context, it will deliver results at a speed that is difficult for teams to match manually. But it will still require guidance. Humans will define the aim, interpret trade-offs, and make decisions where intent is unclear or results are ambiguous. If something goes wrong, accountability stays with the human operator, not the system.

This operating model will help teams manage complexity more predictably. AI will take on repetitive or time-sensitive tasks, and humans will focus on strategic decisions and system-level understanding. Growth in the agentic era will come from organizations that combine human judgment with AI-driven execution in a way that is transparent, governed, and aligned to business objectives.

Prediction 6: AI and cloud teams will converge

AI will stop operating as an isolated discipline and will become a normal component of cloud-native software delivery. Teams will integrate AI into digital services the same way they integrate databases or other core systems. As a result, AI engineering, cloud engineering, SRE, and security will converge into a shared operating model with common pipelines, shared SLOs, and unified accountability for the full lifecycle of AI-enabled services.

This shift reflects how modern software already behaves. AI features influence cost, latency, behavior, and compliance, and these effects span the entire stack. They can’t be monitored or governed in isolation. To operate reliably in production, AI must run within the same workflows, guardrails, and delivery pipelines used for the rest of the cloud-native system.

End-to-end observability becomes essential because what matters is the complete outcome for the user. The guidance agents receive, the actions they take, the database calls they trigger, and the costs they incur all contribute to the overall user experience. Observability must follow all of these signals together and treat AI components, application logic, and cloud infrastructure as one interconnected system. This removes the distinction between “AI observability” and traditional telemetry and creates a unified view that aligns to how customers experience the service.

Organizations that adopt this model will treat AI as a first-class software component. Central teams will define use cases, establish common stacks, and ensure compliance, while product teams will build AI directly into their delivery pipelines. This practical convergence will allow enterprises to operate AI-driven services with the same discipline and predictability as any other cloud-native system.

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As AI investment accelerates, a gap is emerging between ambition and execution. IDC projects¹ that by 2028, AI spending will make up 16.4% of total IT expenditures. However, Gartner, Inc.² predicts over 40% of agentic AI projects will be canceled by end of 2027. Likewise, a CIO survey found that 88% of AI pilots fail to reach production due to unclear objectives, insufficient data readiness, and a lack of in-house expertise. These findings place the expected return on research and innovation firmly at risk, as organizations invest in bespoke models and agentic AI that lack a clear, scalable outcome.

Nonetheless, another Gartner, Inc. article³ predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously by agentic AI systems. Consequently, a Forrester blog⁴ predicts that 40% of highly regulated enterprises will combine data and AI governance in a move toward a more integrated, transparent, accountable, and ethically responsible approach to AI.

These trends are not contradictory—they show how the market is searching for the right formula to adopt AI, and specifically agentic AI. Successful agentic AI outcomes are predicated on trust in AI’s reliability, security, and alignment with business goals and strategies to not fall behind competitors. Achieving that trust requires AI-native observability that’s deeply integrated with both data and strategic objectives.

Key insights for executives

• Every modern cloud-native enterprise project will also be an AI-native project – either because of first party AI or through invoking agentic AI services. Preparing for AI adoption is among the top drivers for cloud strategy and investment. Likewise, 63% of top-performing companies increase their cloud budgets to be able to leverage AI.
• Visibility into reliability and governance of AI interactions has emerged as a new responsibility for executives to realize the value of AI investments while managing risks. From analyst firms in the US to regulators in the EU – increased oversight, link to business goals and regulation of AI systems has become mandatory.
• Unifying observability signals with AI-powered analytics provides a strategic advantage for AI transformation. By converging observability and AI, teams can accelerate moving projects from pilot production and advance trust and transparency in AI.
• Dynatrace sets the standard for cloud- and AI-native software, including tracing and logging of AI behavior, predicting and optimizing AI resource utilization, and protecting from unintended AI behavior through runtime security.
• Dynatrace delivers unified, full-stack visibility across cloud infrastructure, AI workloads—from chat interfaces and prompts to models, tools, and GPUs running on Kubernetes—plus customer experiences and the business layer, all in a single pane of glass, to confidently deliver advanced, AI-powered cloud-native services via a rapidly growing number of 40+ technologies and integrations with hyperscalers and major agentic frameworks providers.

The rise of AI comes with a rise in complexity—and executive responsibility

Organizations generally find themselves maturing their AI implementations along five phases with growing complexity and risks:

1. Prompt engineering (generative Al hype). Single step human language prompts a large language model (LLM) for automated text processing and assistance.
2. Retrieval augmented generation (embedding Al in digital services). Multi-step prompt engineering and LLM access for customer support, automation, and decision-making.
3. Fine-tuned models. Additional model(s) put on top of existing ones for increased accuracy and domain-aware responses.
4. Multimodal GenAI. Combination of various modalities beyond text—such as video, audio, imaging and others—that further increase heterogeneity and processing power of services and their interdependences.
5. Agentic Al. Multiple AI agents and cloud native digital services intensively interacting with each other to autonomously fulfill a specific goal. Agentic AI can double the number of deployed digital service instances and massively increase IT complexity.

The necessity of AI observability for agentic AI reliability

As the complexity of AI implementations increases, observability becomes an essential feedback channel to properly orchestrate and moderate reliable agentic AI outcomes.

Even the early phase implementations show the need to observe AI, tune experience, manage cost, provide guardrails and govern AI responsibly. As the complexity grows, the risks also increase, making deep, context-rich observability of AI strictly mandatory.

7 important reasons for continuously observing AI

1. Business value. Validate AI investments against business goals and verify end-user value of AI services. Gain business insights from observability data.
2. Cost and performance control. Monitor and control expenses and sustainability associated with AI operations and investments.
3. Security. Increase awareness of interactions among AI services, reducing the risk of hacking and malicious influence. Leverage converged observability and security offerings to minimize risk and cost.
4. Compliance. Monitor that AI output is ethical, unbiased, and adheres to guardrails for meeting regulatory compliance requirements and providing traceability for audits. Expect high volumes of logs and traces to observe AI behaviors and keep audit trails.
5. Accuracy. Verify that AI agents function properly and precisely, generating quality output. Use observability to deeply check run-time behaviors and
6. Reliability. Provide traceability and root-cause analysis to verify AI agent health, scalability, performance, and availability.
7. Collaboration. Govern communications among agent-to-agent and agent-to-human, and provide the means to keep humans in control to override and take responsibility. Automate events from observability platforms that integrate with enterprise ecosystems.

With Dynatrace, executives can solve one of the biggest challenges of managing return on AI investment: Balancing innovation speed with risk, cost, and value.

Increase AI success with AI Observability from Dynatrace

AI is not a single component. Agentic AI in particular is composed of multiple layers and technologies, each observed within a holistic context. Dynatrace provides complete coverage of all layers that allows teams to observe the complete AI stack of modern cloud- and AI-native applications. The layers consist of the following:

• Business – track outcome: does it create productivity gains, does it deflect support tickets, does it act autonomously and is the investment worth it
• Infrastructure – utilization, saturation, errors
• Models – accuracy, precision/recall, explainability
• Semantic caches and vector databases – volume, distribution
• Orchestration – performance, versions, degradation
• Agentic layer – autonomous agents, MCPs
• Application health – availability, latency, reliability

Dynatrace automatically observes and analyzes complex multicloud and agentic AI systems. By securely unifying and storing all data in context, the Grail® data lakehouse with massively parallel processing unifies all data signals with full context and is continuously updated by Dynatrace Smartscape® real-time dependency mapping technology.

Davis® AI combines predictive, causal, and generative AI to provide deterministic answers and insights, which drive AutomationEngine actions and inform teams with recommendations to optimize productivity, performance, and cost. With these advantages, teams can embrace AI with confidence, make better decisions faster, and innovate at speed—without compromising trust, performance, reliability, or control.

Why Dynatrace for reliable agentic AI projects

Top Fortune 500™ organizations use Dynatrace to not only maximize return on investment (ROI) in AI technologies, but across their cloud- and enterprise stacks. Dynatrace leverages partnerships with hyperscalers and major AI framework providers to provide customers with observability for the latest technologies in this fast-moving space.

The recent announcement of our collaboration with NVIDIA is an example of our commitment to providing differentiated AI observability. Dynatrace AI observability delivers real-time, end-to-end observability into AI and LLM workloads—from infrastructure and applications to model performance and end-user experiences. This empowers enterprises to accelerate innovation, ensure compliance, and confidently scale mission-critical AI, all while maintaining reliability and efficiency across their cloud environments.

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¹ _{IDC Market Forecast, “Worldwide Artificial Intelligence IT Spending Forecast, 2024–2028,” October 2024, https://my.idc.com/getdoc.jsp?containerId=US52635424&pageType=PRINTFRIENDLY.}

² _{Gartner Press Release, “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027,” June 25, 2025, https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027}

_{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.}

³ _{Gartner Article, “Intelligent Agents in AI Really Can Work Alone. Here’s How.,” by Tom Coshow, October 01, 2024, https://www.gartner.com/en/articles/intelligent-agent-in-ai.}

⁴ _{“Predictions 2025: An AI Reality Check Paves The Path For Long-Term Success,” Forrester Research, Inc., by Jayesh Chaurasia and Sudha Maheshwari, October 22, 2024, https://www.forrester.com/blogs/predictions-2025-artificial-intelligence/.}

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In my frequent interactions with customers implementing agentic AI, the expectations of two key audiences—executives and developers—quickly become apparent.

Executives are actively exploring how to implement agentic AI, with a strong focus on unlocking significant productivity gains. They expect AI automation to free up engineering time, fix software automatically, prevent outages, and take over the majority of 80% of non-feature tasks.

Developers are rapidly adopting AI for convenience and efficiency in their day-to-day work; it’s becoming as essential to them as internet access. For example, GitHub Copilot usage among developers rose from 17% in 2023 to 45% in 2024. They want AI to bring context and suggest precise error repairs, generate tests automatically, auto-collect information to fix vulnerabilities, and recommend optimizations based on real production insights.

The market is embracing agentic AI with growing excitement. KPMG’s AI Pulse Survey, 68% of business leaders plan to invest between $50 million and $250 million in generative and agentic AI technologies this year alone, up from 45% in 2024. Enterprises see it as a strategic priority and as an enabler for smarter automation. While the potential is real, the requirements to make the use of agentic AI robust and secure need a solid foundation.

Context as foundation for reliable agentic AI

Imagine your car won’t start, and you ask an online car assistant for help. Most would start by asking you vague questions or suggesting generic fixes (“Try a new battery”) because they don’t understand or know the context of the problem. The next one might tell you: “Your engine is entirely broken. You need a new one.” Now, imagine instead you bring the car to an automotive expert who not only sees the reason for not starting but also instantly analyzes the entire build of your car down to the exact configuration of parts, how they interact, and even what parts were installed in what order. They don’t just know that the motor and screw exist: they know the screw holds the ignition coil to the engine block, and not the other way around.

This is how agentic AI works with Dynatrace. Agentic AI works like a team of experts who know your car inside out: every screw and why and how the vehicle was built. It’s not guessing but rather operating with architectural clarity, automatically pinpointing the root cause because it understands how everything is connected. With Davis® AI Root Cause Analysis, Dynatrace analyzes more than three million problems accurately and at scale every 24 hours, every day.

Instead of fumbling through 100,000 parts, it navigates a precise causal (say, the 50 services that actually influence the outcome) thanks to Dynatrace Smartscape. It doesn’t reach for every tool in the shed, but instead picks the right one for your specific digital system, every time.

So, similarly in IT: instead of general comments (“Your system seems slow, maybe scale your servers”), engineering teams get granular insights: “User slowdown originates from a failed API call in payment service, due to a misconfigured feature flag introduced in deployment of branch ‘calculation update in payment service’.” That’s how Dynatrace delivers context in action.

Today’s AI-powered automation in Dynatrace already shows agentic behavior

Dynatrace has long been operating at the intersection of data, intelligence, and automation. In fact, many capabilities typically associated with agentic AI, such as autonomous root cause detection, preventive operations, causal inference (which today has become causal AI), and self-healing production environments, have already been running across our platform for a decade.

Take this example: Dynatrace automatically detects a capacity issue, anticipates seasonal fluctuations, rates it by customer and business impact, and recalibrates the production environment across a customer’s hyperscaler setup, all end-to-end. It carries out full analytical and planning steps, creates reconfiguration plans, and only then notifies a human for final governance. This isn’t hypothetical: thousands of customers around the world are already leveraging our trusted causal and predictive AI in production workloads that run their businesses. And hundreds are taking the next step, adopting preventive operations by carefully adding generative AI to automatically draft remediation workflows, simulate outcomes, and enhance decision-making, shaping the future of intelligent automation.

Dynatrace AI capabilities flag and remediate problems, surface insights, and feed them into IDEs. This process triggers ticket creation to the responsible teams and aligns them around automatically planned actions, including learning from past incidents while incorporating real-time facts in context.

To further evolve from automation to autonomy, Dynatrace magnifies its capabilities with agentic AI and delivers three reliable agentic AI requirements through an architecture built for intelligent action.

Leveraging agentic AI for redefined observability with Dynatrace

The future of observability is being redefined by a powerful triad: Knowledge, Reasoning, and Actioning.

1. Knowledge. Dynatrace transforms contextual full-stack observability data into fact-based, real-time knowledge optimized for AI access. The Grail massive parallel processing data lakehouse is schema- and index-free, boosting AI agents with limitless query permutations. Grail works in tandem with Dynatrace Smartscape dynamic topology, an auto-discovered, continuously updated knowledge graph. This allows AI to deliver precise insights efficiently and at petabyte scale, eliminating the need for redundant queries (hence, also the increased cost) while maintaining full context and performance integrity.
2. Reasoning. Dynatrace unifies causal, predictive, and generative AI to power expert AI agents that optimize the blend of deterministic logic with probabilistic and stochastic models, to provide precision and fact-based trustworthy decision-making, while minimizing risks of hallucinations. This enables context-aware decisions with built-in enterprise-grade safety, compliance, and observability of AI itself, ensuring transparency and trust to not only achieve a capable AI, but also a reliable one.
3. Actioning. Dynatrace turns high-level objectives into intelligent, automated actions, where humans define the goals and AI determines the best way to achieve them, both reactively and proactively. With AutomationEngine, AppEngine, and OpenFeature, it remediates, optimizes, and even triggers systemic fixes, transforming observability into a strategic business enabler.

Last, but not least: AI is already powering production workloads across global enterprises, but not all AI is created equal. To deliver real value, it must be reliable, context-aware, and purpose-built for an organization’s digital environment. Dynatrace is engineered to meet those demands, magnified with agentic AI that answers organizations’ specific needs and business outcomes.

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The world has become software-defined, distributed, and complex, creating a widening gap between digital complexity and our ability to manage and govern the systems that run businesses and organizations. To close this gap, we reimagine how observability works. It is no longer enough to collect and analyze telemetry after the fact. Organizations need trusted, intelligent systems that turn real-time data into reliable knowledge, apply advanced AI to reason through that knowledge, and take action to optimize outcomes at every level of the business.

This is the foundation of the Dynatrace 3rd-generation platform. We’ve spent the past two decades shaping the observability market. Today, we are transforming it from a rear-view mirror into a real-time control system for the modern enterprise. Thousands of organizations are already using Dynatrace 3rd generation to turn data into decisions and decisions into action. The result is faster innovation and stronger business results across every layer of the business.

A new model built on knowledge, reasoning, and actioning

Dynatrace 3rd generation introduces a new standard for observability and automation based on three foundational capabilities:

• Knowledge: The Dynatrace platform turns petabytes of real-time data into a continuously updated, queryable knowledge graph. Powered by Grail and Smartscape, it provides trustworthy, fact-based insights with real-time context across dynamic environments.
• Reasoning: Causal, predictive, and generative AI models work together to derive intelligent decisions. These models are context-aware, transparent, and built for enterprise-grade safety and compliance.
• Actioning: Dynatrace enables users to define goals and let intelligent automation determine the best path forward, through innovations like AutomationEngine, AppEngine, and OpenFeature. This shifts operations from reactive remediation to preventive operations and continuous improvement.

Together, these capabilities form the foundation for autonomous intelligence. Dynatrace doesn’t just provide visibility; it enables systems to understand and act. By continuously converting real-time data into trustworthy insights, applying AI to reason through business and technical context, and triggering intelligent, goal-based actions, Dynatrace transforms observability into a real-time engine for automation and impact.

This is not about removing humans from the loop. It’s about empowering teams to define outcomes and rely on the system to carry out the best path forward. As with any leadership decision, autonomy depends on the quality of information and confidence in its context. The same principle applies to AI systems. Dynatrace 3rd generation gives organizations confidence, allowing them to scale decision-making with speed and trust.

Trusted knowledge, not just data

Legacy observability platforms focus on collecting telemetry data. But to support real-time decisions, teams need a trusted knowledge foundation. Dynatrace eliminates silos between metrics, traces, logs, events, user sessions, and security signals by unifying them in Grail, our schema-on-read, massively parallel data lakehouse.

For the first time, users can run any query at any time, with Grail supporting dramatically higher concurrency than traditional observability platforms. There’s no cold storage, no indexing, and no need for rehydration. This unlocks a goldmine of observability data and turns it into reliable, real-time answers.

That data is then contextualized in real time by Smartscape, our dynamic topology engine, and made instantly accessible to AI agents. The result is not just visibility, but deep, evolving system knowledge, providing machine-speed decisions no other platform can match.

AI that reasons with real-time context

Dynatrace has long set the standard for causal AI in observability. With the 3rd generation platform, we expand that foundation by combining causal AI with predictive and generative models. These AI types work together to support decisions at machine speed, with full context. Whether it’s automatically identifying the root cause of a service degradation, forecasting capacity needs, or evaluating how to improve online customer experiences, Dynatrace AI operates with the reliability and transparency required in enterprise environments.

Now, organizations can pursue modernization, transformation, and agentic AI initiatives with greater confidence. Dynatrace helps make AI accessible and actionable by reducing friction, delivering answers precisely when and where they are needed. Davis CoPilot enables natural language queries, workflow generation, and seamless integration into IDEs. It provides intelligent assistance at every step and supports a broad range of use cases across observability, security, and business operations with explainability, precision, and trust.

Automation that adapts to your goals

Traditional automation is limited by what is explicitly scripted. Dynatrace has taken a different approach. With the 3rd generation platform, you define high-level goals, and the platform determines the best way to achieve them. By grounding automation in real-time, high-quality data and precise causal analytics, Dynatrace ensures that actions are driven by accurate understanding, not assumptions.

This goal-based automation can resolve incidents, optimize performance, reduce cost, and even generate pull requests that improve code quality.

For example, preventive cloud operations allow site reliability engineers to move from firefighting to strategic orchestration. Instead of chasing alerts, SREs can focus on managing service-level objectives and improving business outcomes. Dynatrace handles the rest.

Built for the future of cloud and AI

The complexity of cloud-native architectures, Kubernetes deployments, and emerging agentic AI models are already testing the limits of traditional observability. Dynatrace 3rd generation is designed for the future.

By unifying telemetry, security data, and business context into a single real-time graph powered by Grail, Dynatrace provides the AI-powered intelligence required to operate modern systems with confidence. And by embedding automation throughout the platform, teams can scale faster than headcount, without sacrificing control or trust.

Turning observability into intelligent action

Organizations like TELUS and Air France-KLM are already seeing results: faster resolution, improved resiliency, and reduced downtime.

“By combining our Agentic AI initiatives with Dynatrace’s AI Observability capabilities, we’ve successfully optimized our development and operations workflows. We’re driving innovation and delivering measurable business impact while reducing downtime.”

– TELUS

“The AI and predictive capabilities from Dynatrace were a differentiator. We’re confident that any problem that arises can be dealt with quickly, dramatically reducing operational and revenue impact.”

– Air France-KLM

The combined impact of agentic AI initiatives and AI-powered observability extends beyond IT. These outcomes drive business performance, from greater availability and productivity to better customer experiences.

What this means for your organization

The Dynatrace 3rd-generation platform defines the path forward to a future where software can understand, reason, and act. It helps your organization move from reactive to proactive, from fragmented tools to unified intelligence, and from scripted automation to AI-driven operations.

Whether you are focused on cloud modernization, application security, cost optimization, or AI governance, Dynatrace provides a foundation to build and scale with confidence.

This is the evolution of observability. One built on context, driven by reasoning, and capable of taking action.

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As organizations embrace a cloud- and AI-native future, the pressure to control infrastructure spending while meeting sustainability goals intensifies. As a CTO, I want my investments to go into people—building strong, innovative development teams—rather than overspending on cloud resources that don’t deliver business value.

This is where Dynatrace plays a crucial role: helping organizations optimize cloud costs while advancing sustainability goals and enabling AI innovation. These priorities are no longer at odds; instead, they go hand in hand.

The true cost of cloud and AI

The rapid spread of AI—including LLMs, agentic AI systems, and coding assistants—and the shift to dynamic, multicloud environments have created yet more layers of complexity. AI workloads are compute-intensive. In fact, one of our customers in the banking sector shared that GenAI tasks cost five times more than traditional cloud workloads. Gartner® predicts that by 2028, more than 50% of cloud compute will be AI-related, up from just 10% in 2023.¹

The International Energy Agency – Electricity 2024 report stated that when comparing the average electricity demand of a typical Google search (0.3 Wh of electricity) to OpenAI’s ChatGPT (2.9 Wh per request), and considering 9 billion searches daily, this would require almost 10 TWh of additional electricity in a year. That’s enough to power approximately 3 million households—or all private households in London—with energy for a year.

This growth brings significant environmental and financial implications. Yet, most organizations are beholden to opaque carbon footprint multipliers calculated by the cloud provider, which is often insufficient for actionable insights. Similarly, traditional cost reporting tools lack the depth and runtime visibility needed to drive meaningful optimization.

Dynatrace fills that gap, combining real-time observability, AI-powered insights, and topology-aware mapping to bring deep clarity into both cost and carbon impact.

Four steps to smarter cloud cost and energy management

1. Eliminate waste from idle or underutilized resources

Much of today’s cloud waste stems from overprovisioning and forgotten instances, especially in development and AI workloads. Dynatrace automatically detects underutilized or idle resources across the environments and surfaces insights that can drive decisions whether to shut down or re-size them, reducing both spend and carbon footprint.

Smartscape® automatic discovery and topology mapping adds unique value here, showing not just what’s idle, but whether it’s tied to business-critical processes or genuinely redundant.

2. Align cloud consumption to business value

Executives need more than cost data—they need to understand the why behind consumption. Dynatrace connects cloud utilization directly to applications, users, and business processes, enabling teams to assess whether resources are delivering real business value.

By linking costs to outcomes, organizations can prioritize what to keep, right-size what’s inefficient, and decommission what’s no longer serving a purpose.

3. Optimize architecture and energy efficiency

Most organizations are already taking basic steps like using contracted discount reserved instances or more flexible on-demand spot instances. The next level is architectural and source code optimization, such as green architecture and green coding. Dynatrace helps identify inefficient data flows, underperforming services, and high-cost cross-region transfers.

These insights enable teams to apply green coding techniques, reduce energy-hungry compute patterns, and bring data flows closer to where they’re needed, cutting both cost and carbon emissions.

4. Enable smart, automated orchestration

Finally, Dynatrace has a clear vision to make operations more autonomous. Its predictive, AI-driven orchestration of cloud resources enables teams to automatically scale resources up or down based on real-time demand, user behavior, and business impact.

However, autoscaling based on cloud metrics alone can’t ensure a great user experience or cost efficiency. Dynatrace links infrastructure and deep application observability to user-facing outcomes, allowing for smarter scaling that adapts dynamically to seasonal spikes, new product launches, or unexpected load while eliminating idle time and energy waste.

Accelerating sustainable innovation

Sustainability is now a strategic lever, not just a compliance checkbox. It resonates with environmentally conscious customers and a new generation of employees who want to work for conscientious companies.

By using Dynatrace Cost & Carbon Optimization and full-stack observability, organizations can:

• Gain real-time, fine-grained insights into the energy and carbon impact of workloads
• Make carbon reporting actionable and automatable instead of superficial
• Build a more efficient, resilient, and future-proof cloud environment

Imagine your cloud-native teams rapidly scaling up environments to test the scalability of new AI features, leading to a 40% spike in compute usage. Without visibility, one wouldn’t notice that this test left over idle or oversized instances, quietly driving up both cloud costs and carbon emissions. Now imagine having real-time insights from Dynatrace that reveal 200 idle instances across non-critical environments, costing thousands monthly and consuming unnecessary energy. Dynatrace AI leverages Smartscape® real-time topology to know automatically which instances can be confidently decommissioned or right-sized—cutting waste, aligning spend to business value, and advancing your sustainability goals.

The bottom line: Intelligent clouds mean a more sustainable planet

Organizations today must move beyond basic FinOps or simple sustainability checklists. The future lies in intelligent, self-optimizing clouds that balance performance, cost, and sustainability in real time.

Dynatrace empowers executives to realize this vision—transforming cloud environments into engines of innovation that are efficient, responsible, and aligned with business and environmental goals.

^{1 Gartner Press Release, “Gartner IT Symposium/Xpo 2024 Orlando: Day 3 Highlights,” October 23, 2024, https://www.gartner.com/en/newsroom/press-releases/2024-10-23-gartner-it-symposium-xpo-2024-orlando-day-3-highlights.}

^{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.}

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Almost daily, teams have requests for new tools—for database management, CI/CD, security, and collaboration—to address specific needs. Increasingly, those tools involve AI capabilities to potentially boost productivity and automate routine tasks. But proliferating tools across different teams for different uses can also balloon costs, introduce operational inefficiency, increase complexity, and actually break collaboration. Moreover, tool sprawl can increase risks for reliability, security, and compliance.

As an executive, I am always seeking simplicity and efficiency to make sure the architecture of the business is as streamlined as possible. Here are five strategies executives can pursue to reduce tool sprawl, lower costs, and increase operational efficiency.

1. Increase operational efficiency to foster seamless collaboration

❌ Reinventing the wheel: One of the biggest challenges organizations face is connecting all the dots so teams can take swift action that’s meaningful to the business. Too many signals from point solutions and DIY tools spread across multiple teams hinder collaboration. Moreover, inconsistency in the tech stack and a lack of enterprise-ready integration and authentication approaches means teams must reinvent the wheel, forcing repeated builds and solving the same problems, instead of focusing on delivering business goals.

✅ Automate and collaborate on answers from data: By uniting data from across the organization in a single platform, teams can focus on making faster, high-quality decisions in a shared context. With AI they can trust, teams can understand the real-time context of digital services, enabling automation that can predict and prevent issues before they occur, such as service-level violations or third-party software vulnerabilities. The Dynatrace AutomationEngine orchestrates workflows across teams to implement automated remediations, while with AppEngine, teams can tailor solutions to meet custom needs without creating silos.

2. Unify tools to rein in costs and ease compliance

❌ High costs: Organizations often feel the pain of tool sprawl first in the pocketbook. Multiple tools increase the total cost of ownership through the sum of license fees, reduced negotiation power, and redundant maintenance and operations efforts. For example, organizations typically utilize only 60% of their security tools. Too many tools and DIY solutions also complicate regulatory compliance and make integrations harder, which reduces agility and drives up costs through wasted time.

✅ Business-focused, unified platform approach: A unified platform approach enables platform engineering and self-service portals, simplifying operations and reducing costs. The Dynatrace AI-powered unified platform has been recognized for its ability to not only streamline operations and reduce costs but also to provide better, faster data analysis. Standardizing platforms minimizes inconsistencies, eases regulatory compliance, and enhances software quality and security. Dynatrace integrates application performance monitoring (APM), infrastructure monitoring, and real-user monitoring (RUM) into a single platform, with its Foundation & Discovery mode offering a cost-effective, unified view of the entire infrastructure, including non-critical applications previously monitored using legacy APM tools.

3. Break data silos and add context for faster, more strategic decisions

❌ Data silos: When every team adopts their own toolset, organizations wind up with different query technologies, heterogeneous datatypes, and incongruous storage speeds. Last year Dynatrace research revealed that the average multi-cloud environment spans 12 different platforms and services, exacerbating the issue of data silos. Worsened by separate tools to track metrics, logs, traces, and user behavior—crucial, interconnected details are separated into different storage. It becomes practically impossible for teams to stitch them back together to get quick answers in context and make strategic decisions.

✅ All data in context: By bringing together metrics, logs, traces, user behavior, and security events into one platform, Dynatrace eliminates silos and delivers real-time, end-to-end visibility.

• The Smartscape® topology map automatically tracks every component and dependency, offering precise observability across the entire stack.
• Davis®, the causal AI engine, instantly identifies root causes and predicts service degradation before it impacts users.
• Generative AI enhances response speed and clarity, accelerating incident resolution and boosting team productivity.
• Fully contextualized data enables faster, more strategic decisions, without jumping between tools or waiting on correlation across teams.

This unified approach gives teams trustworthy, real-time answers, which is critical for navigating today’s complex digital ecosystem.

4. Strengthen security with unified observability

❌ On average, organizations rely on 10 different observability solutions and nearly 100 different security tools to manage their applications, infrastructure, and user experience. Traditional network-based security approaches are evolving. Enhanced security measures, such as encryption and zero-trust, are making it increasingly difficult to analyze security threats using network packets. This shift is forcing security teams to focus instead on the application layer. While network security remains relevant, the emphasis is now on application observability and threat detection. As a result, many organizations are facing the burden of managing separate systems for network security and application observability, leading to redundant configurations, duplicated data collection, and operational overhead.

✅ The convergence of security and observability tools is becoming essential, especially for cloud- and AI-native projects, as traditional network-based security approaches evolve. Platforms such as Dynatrace address these challenges by combining security and observability into a single platform. This integration eliminates the need for separate data collection, transfer, configuration, storage, and analytics, streamlining operations and reducing costs.

From a security risk mitigation perspective, integrating security and observability not only reduces overhead but also enhances security and risk management, providing organizations with better visibility into potential threats and breaches in today’s complex, encrypted environments. Such an approach is in line with my personal mantra and Dynatrace founding principle: reduce to the max.

5. Simplify data ingestion and up-level storage for better, faster querying

❌ Complex data optimization: As organizations adopt more distributed services and AI-driven technologies, data is proliferating at steep rates. IT teams must now ingest petabytes of data and then store, process, and query it cost-effectively and securely. To save on storage and query costs, teams transition older data to cold storage, trimming out valuable details to save space. Re-indexing data and rehydrating it from cold storage for incident investigation and forensics causes query latency and additional management overhead and cost.

✅ Unified data ingest, storage, and querying: With Dynatrace OpenPipeline, teams can ingest data from any source, in any format, and at any scale: think hundreds of terabytes per day and more. That volume and flexibility eliminate the need for extra data ingest tools and ease data normalization, filtering, and pre-processing, which makes data more reliable.

With the Grail data lakehouse, Dynatrace also reduces the need for countless tools to store, index, retrieve, and query data. Grail’s always-on hydration removes the burden of cold and hot storage management without incurring extra costs. Unique data warping technology allows for index-free, schema-on-read, high-performance queries, reducing storage costs further while giving teams the ability to query all data at any time. With this unrestricted availability, organizations can gain insights significantly faster by consolidating data storage and analytics models into a single, standardized approach. Executives can empower their teams to unlock the goldmine of value locked up in their data far more easily and cost-effectively.

Integrating observability and security to reduce tool sprawl

Today’s need for optimization and efficiency pushes executives to see alternative setups and architectures, which often leads them to Dynatrace as a unified platform that can cover all these needs at once.

Here is a typical array of tools found in common IT environment architectures of Fortune 500 companies. Each category shows the limits of numerous tools and services an enterprise may use for their observability and security needs, and the benefits organizations have when these architectures are addressed with Dynatrace.

To meet the immediate needs of individual teams, organizations often find themselves bound up in a network of disparate tools and data silos that hamper productivity. Change takes time, and IT teams are under significant pressure already: many leaders hesitate to take on new challenges unnecessarily, and in many cases they are right. That’s why executives must lead the shift—because consolidation isn’t just about cost, it’s about unlocking better ways to work, collaborate, and create value.

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As the digital world grows more complex, 2025 will bring a tipping point for organizations navigating increasingly dynamic and interconnected IT environments. Observability, long a cornerstone of IT operations, will take on transformative new roles. Driven by rapid advances in AI, evolving regulatory frameworks, and mounting sustainability pressures, observability will no longer be a passive diagnostic tool. Instead, it will lead to proactive, automated, and intelligent operations.

These prediction themes for 2025 outline how observability will evolve to meet the needs of a rapidly changing landscape. From new standards for automation and security convergence to redefining sustainability in IT, these shifts represent not just technological advancements but paradigm changes in how organizations operate, innovate, and compete.

Key insights for executives

• Adopt preventive observability to stay ahead of disruptions. Shift from reactive to proactive IT management by leveraging AI-driven systems that autonomously predict and prevent issues before they become a problem, ensuring uninterrupted operations and enhanced customer satisfaction.
• Integrate observability and security for continuous compliance. Simplify regulatory adherence and enhance resilience by implementing platforms that automate compliance reporting and proactively mitigate risks, safeguarding your reputation and bottom line.
• Observability becomes mandatory for any serious sustainability strategy in IT. Instead of just reporting sustainability, leverage observability tools to optimize energy usage and reduce carbon footprints, achieving sustainability goals while lowering operational costs and meeting regulatory expectations.
• Ensure trust in AI with robust observability. Monitor and validate AI-driven decisions with observability platforms that enforce ethical standards and prevent errors, building stakeholder trust and ensuring automation aligns with business objectives.
• Leverage AIOps to enable preventive operations and boost agility. Replace reactive workflows with AI-powered, observability-driven systems to predict and resolve issues proactively, reducing costs, increasing efficiency, and accelerating time to market.

Here are five ways observability will shape the future, starting in 2025.

Prediction #1: Observability shifts from reactive to preventive

Preventive observability will move beyond siloed systems into interconnected, autonomous ecosystems, redefining how organizations ensure reliability and resilience. These ecosystems will function seamlessly across distributed environments, leveraging AI to understand the real-time context of digital services. This capability enables automation to predict and prevent issues before they occur, leading to an era of foresight and collaboration.

Unlike earlier AIOps approaches that struggled to deliver due to limited contextual understanding, this new generation of AI-powered observability integrates insights from across systems to identify root causes, predict cascading failures, and act autonomously in real time.

Consider these examples:

• A logistics company could leverage preventive observability to identify potential bottlenecks in supply chains and reroute shipments before delays occur.
• In healthcare, observability could predict system slowdowns during critical periods, ensuring seamless patient care.
• Financial services organizations could use it to preempt system outages during peak trading hours, protecting both customers and market stability.
• During the holiday season, an e-commerce platform anticipating a traffic surge could use preventive observability to predict slowdowns or overloads, proactively scale resources, optimize performance, and balance cloud costs.

Imagine a future where operational insights are shared seamlessly across industries—whether logistics, healthcare, financial services, or online services—creating a level of interconnectedness that will drive operational excellence.

In these autonomous ecosystems—built on hybrid and multicloud environments—preventive observability automates the complex task of orchestrating distributed systems. By predicting and resolving issues before they impact operations, organizations can ensure service availability, minimize downtime, and reduce operational overhead. This proactive, context-aware approach will soon become the industry standard.

Prediction #2: Observability and security converge around continuous compliance

In 2025, compliance will no longer be a static exercise—particularly for European and globally operating financial services companies—with more changes to follow. Continuous compliance will evolve into a real-time dynamic system driven by security standards and regulatory frameworks like the following:

• EU’s Digital Operational Resilience Act (DORA) and Network and Information Security Directive 2 (NIS2),
• Bank of England’s Operational Resilience Policy in the UK
• Australia’s CPA 230
• Hong Kong’s Monetary Authority Operational Resilience Framework
• The Federal Reserve Regulation HH in the United States

This shift adds to the growing need for observability and security to converge, providing organizations with unified insights to address compliance, reduce redundant data collection, and strengthen threat detection and incident response.

For example, AI systems will continuously monitor threat exposure to assess risks and prepare configuration adjustments. These adjustments can be reviewed and approved by humans or applied automatically, ensuring organizations maintain compliance without disrupting operations. This human-in-the-loop approach is essential for maintaining accountability, particularly when regulatory violations must be reported to governing institutions or national competent authorities (NCAs).

Continuous compliance will replace periodic audits with automated systems that monitor, analyze, and alert on regulatory adherence. By integrating observability and security, organizations gain the additional context needed to qualify violations, track interdependencies across systems, and address vulnerabilities proactively. For instance, a financial services organization could leverage a unified observability and security platform that uses AI to identify potential compliance risks, such as service-level violations or third-party software vulnerabilities, and implement automated remediations to maintain adherence to stringent regulations.

The convergence of observability and security offers more than just regulatory benefits—it equips organizations to combat increasingly sophisticated cyber threats. Observability widens the lens through which security professionals view and analyze data, delivering the context necessary to enhance resilience and reduce costs. By integrating observability into security strategies, organizations can foster the trust needed to operate confidently in an era of heightened risk.

Prediction #3: Observability is mandatory for any serious IT sustainability strategy

Sustainability will take center stage in 2025, as organizations face growing energy demands from cloud environments and increasingly AI-driven operations.

Observability platforms will become essential for monitoring and optimizing the energy consumption of AI workloads, identifying inefficiencies, and enabling intelligent workload distribution. As an added benefit, optimizing energy efficiency through observability not only lowers operational costs, but also aligns with sustainability commitments set by cloud providers. This approach ensures businesses stay competitive as energy costs rise and sustainability regulations tighten.

For example, a global retailer could leverage observability to track energy efficiency across its data centers. With platforms offering discovery and automatic topology mapping, a team can easily identify underutilized resources, revealing significant opportunities for architectural optimizations—often referred to as “green coding.” Such platforms can also enable smart orchestration for dynamic resource utilization. By embracing these strategies, the retailer could significantly reduce energy consumption and operational costs while fulfilling its environmental commitments. This evolution redefines IT’s role from a traditional cost center to a strategic enabler of sustainability.

As energy-intensive AI workloads become the norm and new sustainability quotas gain traction through regional mandates, like the EU’s Green Deal and Corporate Sustainability Reporting Directive (CSRD), sustainability will no longer be optional. Organizations that fail to integrate sustainability into their IT strategies risk non-compliance, reputational harm, and rising costs. Observability plays a leading role in this transformation by delivering the detailed insights necessary to optimize operations—not just report on sustainability. This shift helps businesses strike a sustainable balance between innovation and environmental stewardship.

Prediction #4: AI observability becomes indispensable for AI-driven services

In the evolution of digital transformation, the rise of AI-based services introduces new complexities that make observability more critical than ever. With observability, teams will be able to build and operate new AI-powered digital services for performance and reliability, keeping cost, AI drift, user experience, and transparency in mind. These capabilities will give organizations the confidence to deploy AI technologies at scale.

As businesses deploy AI-driven services for predictive maintenance, financial forecasting, or cybersecurity, observability platforms will go beyond monitoring system performance to include visibility into AI queries. With this clarity, organizations can identify potential errors, correct biases, and ensure decisions align with both business goals and ethical standards.

In 2025, observability will play an even larger role, as organizations increasingly rely on AI to power critical services. By providing end-to-end visibility and actionable insights, observability will empower businesses to confidently scale AI systems, maintaining accountability, reducing risks, and building trust. Therefore, observability will no longer be an optional enhancement, but a mandatory component for delivering safe and effective AI-driven services.

Prediction #5: AIOps is dead, long live AIOps!

AIOps has promised to transform operations for some time, but its fragmented technologies and limited context have prevented it from achieving its full potential. In 2025, AIOps will finally deliver on its promise, fueled by advances that enable AI systems to effectively communicate and collaborate. This evolution will redefine how organizations manage IT and business operations, setting a new standard for preventive operations.

The breakthrough comes from composing diverse AI techniques to work together toward common goals. Through interconnected AI systems—some specialized in prediction, others in precision processing of context, and others in suggesting remediations—AIOps will deliver intelligent automation and real-time root-cause analysis. These systems will predict disruptions, resolve issues before they escalate, and ensure business continuity. By integrating these capabilities, AI can provide deeper insights and take more precise actions by analyzing data in context and learning continuously from operational feedback.

For example, an enterprise managing complex, distributed environments could leverage this advanced AIOps approach to proactively address potential capacity bottlenecks during peak demand, ensuring smooth customer experiences while minimizing costs. Beyond IT, this approach will help organizations address broader challenges, such as forecasting supply chain risks or adapting to shifting market conditions.

The resurgence of AIOps will redefine industry benchmarks for efficiency and resilience. By automating tasks, reducing operational overhead, and enabling faster time-to-market, organizations will achieve unprecedented agility. To succeed, businesses must invest in advanced observability solutions and ensure teams are equipped to unlock the full potential of AI-driven operations.

The post Five observability predictions for 2025 appeared first on Dynatrace news.

At the time when I was building the most innovative observability company, security seemed too distant. However, customers began approaching me, praising Dynatrace’s deep end-to-end insights into even the most complex digital service deployments and asking how to use it for security compliance, exposure, and response use cases. I realized that our platform’s unique ability to contextualize security events, metrics, logs, traces, and user behavior could revolutionize the security domain by converging observability and security.

We have taken that opportunity and expanded Dynatrace to protect applications, remediate exposures, and investigate threats to enable an automated AISecOps approach to continuous compliance.

More requirements, more pressure

Evolving regulations, such as the following, add to the already monumental reporting tasks:

• The NIS2 Directive and the DORA regulation in the European Union both aim to enhance cybersecurity but target different sectors with distinct approaches. NIS2 focuses on harmonizing cybersecurity across various critical sectors within the EU, emphasizing risk management and incident reporting. DORA, on the other hand, is tailored to the financial sector, improving operational resilience through detailed ICT risk management and third-party risk oversight.
• The Bank of England’s Operational Resilience Policy requires financial institutions to identify critical services, set impact tolerances, and ensure recovery from disruptions and it applies to banks, investment firms, and financial market infrastructures, mandating resilience in governance, risk management, continuity planning, and outsourced relationships.
• The Australian Prudential Regulation Authority (APRA) has released a cross-industry Prudential Standard CPS 230 Operational Risk Management to strengthen operational risk management and resilience across APRA-regulated entities. It applies to entities in financial services including banking, insurance, and superannuation fund organizations.
• The Hong Kong Monetary Authority (HKMA)’s Operational Resilience Framework provides guidance for Authorized Institutions (AIs) to ensure the continuity of critical operations during disruptions: governance, risk management, business continuity planning, and oversight of third-party dependencies. It applies to all AIs under HKMA supervision, including banks, restricted license banks, and deposit-taking companies.
• The Federal Reserve Regulation HH in the United States focuses on operational resilience requirements for systemically important financial market utilities.

For executives, these directives present several challenges, including compliance complexity, resource allocation for continuous monitoring, and incident reporting. Carefully planning and integrating new processes and tools is critical to ensuring compliance without disrupting daily operations. Additionally, DORA’s emphasis on third-party risk means executives must validate that their vendors and partners comply with the same high standards, adding another layer of oversight. Visibility of all business processes – starting from the back end and ending with customer experience – is perhaps the biggest challenge. The lack of visibility often is the culprit that doesn’t allow fast decision-making in the case of a security incident.

The ability to make a call on how to approach a security incident can only be possible if executives have an immediate, clear understanding of an incident’s impact. Proactive systems like Dynatrace’s Davis AI can automate responses to threats, swiftly implementing remediation while keeping executives informed of actions taken and their impact. Additionally, effective decision-making during security incidents requires an immediate, clear understanding of their impact. This necessity makes merging observability and security inevitable, providing actionable insights and enabling leaders to confidently guide strategies while automated systems handle threats in real time.

More technology, more complexity

The benefits of cloud-native architecture for IT systems come with the complexity of maintaining real-time visibility into security compliance and risk posture. In dynamic and distributed cloud environments, the process of identifying incidents and understanding the material impact is beyond human ability to manage efficiently.

For most organizations, the security process involves multiple departments and teams, often each with its own siloed tools. Per the Gartner® Simplify Cybersecurity With a Platform Consolidation Framework report, “Complexity is the enemy of security; yet the average organization works with 10 to 15 security vendors and 60 to 70 security tools.” [1] This creates a fragmented picture that must be assembled to give executives the full context – which often takes days, if not weeks.

Converging security and observability into a unified platform not only reduces the technical debt from tool sprawl but also reduces risks of security oversights.

Dynatrace unifies all the different data types at scale and in context. UMELT are kept cost-effectively in a massive parallel processing data lakehouse, enabling contextual analytics at petabyte scale, fast. This also reduces redundancy of data and tool sprawl, while high data privacy standards accelerate team collaboration and automation of security analytics and processes.

Dynatrace not only brings all security data into one place for contextual analytics but also increases security analytics coverage with the addition of observability data. For example, user behavior helps identify attacks or fraud. Another example is when anomaly detection identifies services impacted by ransomware.

Security capabilities that welcome efficiency

On the security front, Dynatrace Application Security provides Continuous Threat and Exposure Management (CTEM) through three core areas:

• Vulnerabilities and Exposures, continuously at runtime.
• Configuration and Compliance, adding the configuration layer security to both applications and infrastructure and connecting it to compliance.
• Detection and Response, connecting log management and security, addressing what previously required a dedicated SIEM, and automating detection and response.

Dynatrace Runtime Security delivers advanced protection for cloud-native and on-premises applications. It continuously detects vulnerabilities, ensures compliance, provides real-time insights beyond logs, and automatically blocks code-level attacks, including zero-day exploits, with intelligent response automation.

Runtime Security integrates seamlessly with static code analyzers, container scanners, and application security testing tools. Customers ingest these findings to Dynatrace and track software quality and security from development to production. With Dynatrace in pre-production, they validate software before deployment and secure it in production, automatically leveraging a dynamic bill of materials to assess both first- and third-party software.

With CTEM alone, by streamlining security needs with Dynatrace, executives can achieve significant savings. For example, for companies with over 1,000 DevOps engineers, the potential savings are between $3.4 million to $5 million annually in increased developer efficiency with our vulnerability and exposure offering alone.

Dynatrace observability and security posture management means Site Reliability Engineers (SREs) get configuration assessments mapped to compliance for Kubernetes, cloud, and VMware environments – with the ability for auto-remediation via workflows.

With logs and threat intelligence data, Dynatrace Query Language (DQL) provides detection findings, rounding out the offering to, for example, secure an entire Kubernetes cluster from vulnerabilities and exposures to configuration and compliance to detections, all combined with response automation. 

Operational resilience with observability and security

Executives might not have considered the role that Dynatrace can play in their security compliance efforts because they see the platform as an observability solution.

We’re challenging these preconceptions. The following are seven ways that the Dynatrace platform can improve how teams conduct security analytics:

1. Unify data storage + context: Gain rich context with end-to-end observability that siloed security tools (such as container scanners, enterprise SIEM, and static code analysis solutions) so often lack resulting in inefficiencies and organizational complexities.
2. Contextual analytics: Enable real-time and contextually holistic analytics and automation with unified observability and security, powered by the Grail data lakehouse. No more manually piecing together data sources for security analytics.
3. Collaboration: Operationalize tasks and increase productivity by enabling greater collaboration between SRE/Ops, development, and security teams.
4. Causal AI-based risk analysis: Reduce false positives and automatically prioritize and route vulnerabilities to developers with automated risk analysis, similar to configuration/compliance issues routed to SREs. Gain the fast insights you need to comply with requirements (such as the SEC’s four-day reporting rule) so you can focus on what matters.
5. Automate security analytics with AISecOps: Modern hypermodal AI usage increases automation of contextual analytics tasks, reduces false positives, detects threats and vulnerabilities impossible before, and speeds collaboration through automated workflows.
6. Real-time cyber resilience: Continuous compliance status and exposure risk assessment and contextual-prioritization eases dealing with growing cyberthreats and hardened compliance requirements.
7. Focus on prevention: Changes in existing and release of new regulations and directives show that it is no longer enough to respond to security threats after they occur. Regulations, especially NIS2 or Cybersecurity Maturity Model Certification, increasingly emphasize proactive risk management. The convergence of observability and security allows for real-time, AI-powered anomaly detection, which is essential for identifying risks before they escalate into full-blown incidents that breach compliance thresholds.

Modern delivery, reliability, and security teams are navigating an exciting yet challenging landscape. With the growing complexity of their roles, these teams are rising to the occasion, leveraging innovation to manage increasing workloads and address the expanding security attack surface with resilience and determination. While it takes time and effort to implement and learn how to use new solutions, the reality is that organizations must act faster than ever to stay ahead of the competition and keep their organizations secure and efficient. Now is the time for executives to take the driver’s seat: dismantle the silos, converge observability and security, and drive a more automated approach to operational resilience.