As more agentic AI projects come online, the observability market is abuzz with familiar promises: tool consolidation, AI-powered insights, and faster remediation through smarter tools. On the surface, this sounds like progress. But beneath the excitement, many discussions are framed around the wrong question.

The real issue isn’t about how to adopt autonomous operations; it’s about ensuring AI agents are operating reliably and resolving problems without introducing new ones. When evaluating new observability solutions, the question should be:

Can this observability solution accurately analyze complex, dynamic telemetry in context so AI agents can act autonomously with trust, precision, and reliability?

As systems become increasingly agent driven, observability is crossing a structural boundary. Approaches designed for environments where only humans decide and act must adapt to a world where agents increasingly operate autonomously with human oversight, while keeping organizations informed.

Rethinking observability for the agentic age

Observability platforms were initially intended to support engineers in delivering reliable applications, services, and infrastructure to users, and alert them in the event of a problem. Dashboards, alerts, and correlation helped teams investigate incidents, piece together what happened, diagnose issues, decide on next steps, and resolve the problem. This model worked when changes were pushed manually.

The assumption was that more data, better correlation, and cleaner interfaces will lead to increased visibility and improved operational decision making.

Agentic AI systems break that assumption.

With faster release cycles and AI-generated code, manual investigations can no longer keep pace. Moreover, observability platforms must now provide actionable insights to both humans and AI agents.

As agents begin operating as autonomous participants in software environments by triggering mitigations, scaling infrastructure, and optimizing behavior in real time, observability can no longer function solely as a human interface. It must also provide AI systems with a reliable, contextual fact basis that agents can act on programmatically. Machines can’t rely on dashboards and alerts. They require a deterministic foundation of unified, real-time data that delivers accurate, context-rich answers at exabyte scale.

Agentic systems break the mold of “good enough”

Many observability platforms layer probabilistic AI on top of siloed data. They use LLMs to correlate signals and rank likely causes—but they can’t always determine correctness.

“Probabilistic” means that the same input will generate a different output based on a probability distribution of predefined outputs, delivering a different answer when the same problem occurs. This approach is also prone to hallucinations, requiring additional human validation, which can increase operational overhead and token costs, delay resolution of business-critical issues, and divert resources from strategic initiatives.

Enterprise-grade observability must now answer: Is this insight reliable enough for autonomous action?

AI built on siloed data is inherently unreliable. Autonomous systems depend on deterministic, contextual, and trustworthy data to act reliably.

“Deterministic” means that the same input always results in the same output by using factual data to trace the exact causal changes that created the issue. When agentic AI systems act on business-critical applications, the cost of being “mostly right” becomes operationally unacceptable.

This is where a subtle but critical divide appears in the market. Aggregating signals and correlating anomalies can surface patterns. Patterns alone are not a solid basis for decisions, and without deterministic understanding, AI systems inherit that uncertainty and can propagate it downstream.

To drive reliable enterprise autonomous operations, AI agents require a unified, AI-powered observability platform that can analyze exabytes of data in real time and across models to pinpoint root cause, delivering actionable answers in context of what’s affected and its business impact.

From correlated guesses to deterministic answers

This shift in the demands of observability hinges on a clear distinction:

• Probabilistic AI correlates signals that happened around the same time and therefore appear related, pulling information from fragmented data stores to propose a likely root cause.
• Deterministic AI uses causal analysis to pinpoint what happened and why, recommend remediation actions, and identify business impact.

Probabilistic AI is intended to narrow the search space and direct engineers toward potential resolution, but it still requires interpretation.

Deterministic AI establishes sequence, dependency, and impact, enabling systems to decide safely without waiting for humans to connect the dots.

Auto‑remediation, auto-prevention, and auto-optimization all depend on this leap. A platform that unifies telemetry only at the UI layer may deliver data and potential root cause, but it can’t compensate for fragmented understanding and missing context underneath. When context is pieced together after the fact, confidence is never guaranteed.

You can’t automate what you don’t precisely understand.

Context driven observability as the control plane for AI

In an autonomous enterprise, observability doesn’t sit beside execution; it’s embedded within it. This integration requires that teams adopt a new mindset toward observability architecture.

Because more AI workloads are happening at the source, telemetry must be optimized and streamlined before ingest, not after the fact, from the edge to the back end. Data access must be unified, context-aware, and always-hydrated on a massive scale. Answers must be explicit, not implicit, and they must be informed by automatic, real-time dependency mapping.

Likewise, intelligence must combine deterministic and agentic AI—not as add‑ons, but as a single reasoning system from ingest to execution.

In this model:

• AI agents can become the primary consumers of observability data.
• Humans can shift toward strategy, architecture, oversight, and exception handling.
• Observability evolves from a reactive lens into a control plane for autonomous operations.

Observability purpose-built for autonomous operations ensures successful agentic AI initiatives

This moment represents an architectural transition, not just an incremental upgrade cycle. Correlation-dependent observability that uses probabilistic AI can be extended, augmented, and rebranded, but it will always carry the limitations of approximation and human validation.

The next era belongs to an observability platform that’s built for machine understanding from the start: a unified, context driven architecture that delivers deterministic answers at machine speed, precision, and scale.

The post Beyond correlation to autonomous action: Why “good enough” observability fails in the age of agentic AI appeared first on Dynatrace news.

New research from Dynatrace reveals how organizations are adopting agentic AI to drive greater business value through automating operations. But as teams push toward AI‑driven automation at scale, they’re also confronting a core challenge: the variable, context-dependent nature of AI systems makes it difficult to establish the reliability, safety, and governance needed to fully realize ROI.

Context is key for AI systems to avoid losing track of instructions, hallucinating missing dependencies, or misinterpreting evolving system states—especially during extended multi‑step tasks. Some of the technical challenges AI agents present include:

• Context fragmentation: As tasks grow more complex, agents cannot reliably hold, retrieve, or apply the full operational context they need for accurate decisions.
• Unpredictable autonomy: Small gaps or inconsistencies in context can cause cascading errors that affect downstream systems, workflows, and data integrity.
• Lack of verifiable control signals: Without real‑time, fact‑based grounding, agents cannot validate their own assumptions or detect deviations, making it extremely difficult for leaders to operationalize autonomy safely.

These issues explain why agentic AI is accelerating but still challenged to become “production‑ready” without a new foundational layer of observability, governance, and human oversight.

The emerging reality: What the 2026 Pulse of Agentic AI reveals

The 2026 Pulse of Agentic AI is a global survey of 919 senior leaders and decision makers directly involved in or responsible for agentic AI development and implementation. Results show that agentic AI is advancing rapidly but encountering structural barriers on the path to scalable autonomy.

• Agentic AI is moving quickly from experimentation into real operations. Most organizations (72%) now run 2-10 agentic AI initiatives, and 50% have at least some production deployments. Adoption is strongest where reliability and risk sensitivity are highest: IT operations (70%), data processing (51%), and cybersecurity (49%), where automation can deliver fast, measurable gains.
• Maturity is uneven. While investment is rising and expectations for ROI are high—44% have projects in broad adoption in select departments—only 23% have projects in mature, enterprise-wide adoption. The primary blocker is not ambition, but trust. Leaders cite security and data privacy (52%), and technical challenges (51%)—especially limited visibility into agent behavior and difficulty defining when agents can act autonomously versus when humans must intervene.
• AI operations forge a new role for human oversight. Most agentic decisions are reviewed or validated by people (69%), and 44% rely on manual methods to monitor agent interactions—slowing scale and increasing operational risk. These findings make one conclusion clear: agentic AI cannot reach its potential through experimentation alone. Scaling autonomy requires stronger governance, clearer decision boundaries, and real‑time observability that connects AI behavior to system reliability and business outcomes.

From insight to execution: Why AI projects are stalling and how observability enables results

The research makes clear why many agentic AI initiatives stall before delivering full business value.

Leading organizations are already using observability as more than a monitoring tool

Observability is becoming the foundation for scaling agentic AI safely. Nearly seven in ten respondents apply observability during implementation to integrate agents with existing systems, monitor data quality, and detect anomalies. As agentic systems move into production, observability is increasingly used to track agent performance in real time, validate outputs, and correlate AI behavior with reliability, efficiency, and risk.

Observability data alone is not enough

At the same time, the research exposes a clear gap: many teams still rely on manual reviews to understand agent interactions, slowing scale and limiting trust. Respondents consistently point to limited real‑time visibility and weak connections between technical signals and business outcomes as barriers to autonomy.

Observability must become a fact-based control plane for agentic AI

This is the inflection point. Organizations that treat observability as a real‑time control plane—governing decisions, enforcing guardrails, and grounding AI actions in facts—are better positioned to expand autonomy with confidence. The following 90‑day action plan translates these proven practices into practical steps leaders can take now.

A 90‑day action plan for execs and IT leads

Operationalizing agentic AI requires moving deliberately—from experimentation to governed, observable autonomy. The first 90 days should focus on building AI trust, resilience, and measurable business impact.

The bottom line: Autonomy only scales with trust

Agentic AI is here—and it’s accelerating. The organizations that win the next phase of AI transformation will be those that implement autonomy with control to minimize risk:

• Build incrementally, moving from supervised to autonomous operations
• Ground all agent decisions in deterministic observability data
• Redesign human roles to guide, not replace, human judgment
• Treat reliability, safety, and transparency as business‑critical capabilities

With a well‑structured 90‑day plan, enterprises can convert experimentation into operational advantage—unlocking the resilience, scalability, and efficiency that agentic AI promises, while keeping humans firmly in control of outcomes.

The post Building trust in agentic AI: An observability‑led 90‑day action plan appeared first on Dynatrace news.

Agentic AI is accelerating into the enterprise faster than many leaders expected, bringing with it unprecedented complexity. Unlike traditional machine‑learning systems, agentic architectures combine goal‑directed reasoning, multi‑step autonomy, and real‑time adaptation across a wide variety of applications. This variability creates exponential interaction paths and the potential for unpredictable behaviors and downstream consequences that traditional monitoring simply can’t capture.

As organizations move from pilots toward autonomous operations, a clear trend is emerging: without guardrails, strategic human oversight, and a real‑time observability control plane, agentic systems face barriers to operating reliably at scale.

The Pulse of Agentic AI 2026 study—based on 919 global leaders responsible for agentic AI development and implementation—reveals how enterprises are adopting agentic AI, where they’re encountering barriers, and why observability is becoming foundational for building safe and reliable autonomous systems.

Agentic AI is rapidly expanding beyond ITOps

Although agentic AI is most established in IT operations, system monitoring, DevOps, cybersecurity, and software engineering, it’s expanding quickly into nearly every domain.

Key data points show:

• 72% use agentic AI in ITOps and DevOps, followed by software engineering (56%) and customer support (51%).
• Externally exposed use cases—product personalization, sales engagement, digital services—are the fastest‑growing over the next five years.
• 74% expect budget increases in the next 12 months, often by an additional $2–5M or more.

Agentic systems gain traction first in domains where quick response is imperative, such as those that demand reliability and controlled automation. Observability and deterministic guardrails must therefore be foundational, not optional.

Even as customer‑facing use cases rise, organizations prioritize agentic AI in measurable, repeatable workflows with strong ROI, such as ITOps, data processing, reporting, and cybersecurity. Value and risk scale together, and the only way to manage both is through real‑time, end‑to‑end visibility into agent behavior.

Autonomous operations are growing—but hitting barriers

Organizations are no longer just experimenting. Portfolios are expanding quickly:

• 72% have 2–10 projects; 26% have 11–21+.
• 44% have agentic AI in production for select departments.
• 23% have enterprise‑wide integration in some areas.

Yet progress is uneven. The bottleneck is establishing trust in production‑level autonomy.

Top blockers include:

• Security, privacy, and compliance concerns (52%)
• Technical challenges in managing and monitoring agents at scale (51%)
• Difficulty defining when agents act autonomously vs. require human approval (45%)
• Limited real‑time visibility to trace and troubleshoot behavior (42%)

Organizations aren’t struggling with ideas—they’re struggling with control. Without deterministic guardrails, transparent model behavior, and real‑time signals showing what agents are doing and why, teams can’t safely operationalize autonomy.

Building trust requires incremental progression: human‑in‑the‑loop models, supervised autonomy, and phased functional expansion, all enabled by observability.

Trust and human oversight are intentional—and enduring

Despite enthusiasm for fully autonomous agents, human oversight remains central:

• 69% of agentic AI decisions are verified by a human.
• Top validation methods include data‑quality checks, human review, drift detection, and logs/traces.
• Only 13% rely exclusively on fully autonomous agents, but 64% combine supervised and autonomous models.

Organizations are building human-AI partnerships, not replacements. In fact, in the long term, respondents expect a 60/40 human‑in‑the‑loop balance for business applications and 50/50 for IT and customer‑support functions.

Two insights stand out:

1. Because agentic AI is probabilistic, enterprises depend on human judgment for high‑risk validation.
2. Observability supplies the factual ground truth that makes this oversight effective.

As organizations scale, human involvement becomes more strategic—guiding goals and accountability while AI handles repeatable or time‑sensitive execution.

Reliability and resilience define success

To measure agentic AI success, organizations prioritize real‑time decision‑making, performance, efficiency, and reliability, for example:

• Technical performance is the top metric (60%)
• Developer and operational efficiency follow
• Customer satisfaction and business outcomes come next
• Compliance and security are rising, especially in large enterprises

Still, 44% manually review inter‑agent communication flows—a clear scaling limitation.

Agentic systems are inherently interconnected. A performance regression or hallucination in one agent can cascade downstream into applications, user experiences, or security posture. As a result, resilience and rapid recovery—not just efficiency—must be built into agentic systems.

Doing so requires:

• Observability signals that detect anomalous or unexpected actions
• Real‑time tracing of inter‑agent communication
• Automated risk detection informed by factual telemetry
• Deterministic guardrails preventing stochastic failures from propagating

Reliability and security are no longer separate concerns—they’re inseparable in autonomous systems.

Observability is a control plane for agentic AI

The study’s most strategic finding: observability is shifting from a supporting function to the control plane for agentic AI.

Usage is already broad:

• 69% use observability during implementation
• 57% in operationalization
• 54% during development

But gaps remain in transparency, real‑time visibility, risk detection, and linking signals to business outcomes.

Because agentic behavior can’t be fully tested in advance, teams need real‑time observability to monitor performance in production and respond quickly to anomalies. Traditional monitoring tools can’t explain why an agent took an action, detect hallucinations in real time, or trace downstream impact.

A modern observability control plane must:

• Blend deterministic telemetry with probabilistic model insights
• Standardize semantic conventions and agent‑action signals
• Link behavior to business outcomes
• Detect and correct anomalies instantly
• Keep agents aligned to shared, real‑time facts
• Maintain clear human accountability and governance

This is the foundation organizations need to progress from supervised autonomy to reliable, production‑grade autonomous operations.

The path to operationalizing agentic AI

Autonomous operations will redefine enterprise technology. But success requires treating autonomy as a maturity journey, not a leap:

• Start with preventive and recommendation‑driven workflows
• Build trust through human‑in‑the‑loop models
• Harden services, signals, and data paths
• Use observability to detect anomalies and validate actions
• Scale autonomy gradually—with transparency and governance

The message of the 2026 research is clear: the future is autonomous, but limited visibility is hindering reliability and control. Scaling agentic AI requires an observability‑based control plane that grounds probabilistic agent behavior in deterministic, real‑time facts.

The post Autonomous operations hits an inflection point: New agentic AI report reveals what’s fueling scale (and blocking it) appeared first on Dynatrace news.

Key insights:

• Why it matters. Traditional IT operations are caught in a reactive cycle of alerts, tickets, and manual fixes that slow innovation, drive up costs, and drain resources.
• What’s new. The Dynatrace and ServiceNow partnership introduces a new model for autonomous IT operations that connects observability and automation to predict and resolve issues before they impact users.
• How it works. Dynatrace provides real-time, causal intelligence through deterministic and agentic AI, while ServiceNow transforms those insights into automated, closed-loop workflows that detect, diagnose, and remediate incidents.
• Proof in action. Organizations such as BT Digital, CareSource, and Commerzbank have already reduced incident volume and mean time to resolution (MTTR), demonstrating measurable gains in reliability and efficiency.
• The bigger impact. Together, Dynatrace and ServiceNow are enabling self-healing IT ecosystems that elevate operations from reactive firefighting to proactive, continuous optimization across multicloud environments.

—

It’s 3 a.m., and a critical service alert lights up your phone. You log in, sift through dashboards, trace dependencies, and chase symptoms while customers wait. By the time the root cause is found, you’ve lost hours, sleep, and user trust.

Traditional IT operations are stuck in a loop of alerts, tickets, and manual fixes—always responding, never advancing. That approach is no longer sustainable. As systems expand across clouds and teams, the cracks widen. When incidents strike, teams scramble to manually correlate data across disconnected systems to pinpoint root cause and impact, wasting precious time, as outages drag on.

A new model is taking shape, shifting IT from reacting to predicting.

The timeline for autonomous IT operations accelerates today with a new, multiyear partnership between Dynatrace and ServiceNow. Together, our goal is to move enterprises from managing incidents to managing intelligence: creating a foundation for systems that see, decide, and act on their own.

“We help customers anticipate issues, coordinate remediation, and continuously optimize services by combining deterministic and agentic AI — bringing them closer to autonomous prevention, remediation, and optimization at enterprise scale.”
— Steve Tack, chief product officer at Dynatrace

Why reactive operations can’t keep up

Most organizations still operate in reactive mode. The symptoms are familiar:

• Visibility gaps fragment understanding across applications, infrastructure, and cloud environments.
• Manual incident response requires human intervention at every step, from detection through resolution. Problems are often discovered only after users are affected.
• Alert fatigue overwhelms teams with noise instead of insight. Without business context, it’s nearly impossible to prioritize what truly matters.

The result: slow mean time to resolution (MTTR), recurring issues, and IT teams trapped in a costly cycle of firefighting instead of innovation.

Connecting observability and automation through AI

Breaking the cycle requires more than adding automation to existing processes. It requires aligning observability and automation so systems can see clearly, decide confidently, and act autonomously.

Dynatrace and ServiceNow are advancing toward agentic operations, where AI agents collaborate across platforms to detect, triage, and resolve issues end-to-end. These systems share context, coordinate decisions, and execute actions autonomously, creating a continuous flow between observability and automation.

This evolution unfolds in three phases:

• AI-assisted. Operators interact with Dynatrace directly from ServiceNow using natural language, accessing observability insights in context. This capability is available today through integrated workflows.
• AI-led. Agents begin coordinating workflows across platforms autonomously, while maintaining human oversight.
• AI-driven. Agents validate hypotheses, assess business impact, and execute full remediation workflows automatically — realizing the vision of autonomous IT.

This approach builds trust gradually, delivering immediate value today while preparing organizations for the agentic future of operations.

The convergence that makes autonomous operations possible

Dynatrace provides in-bound intelligence, combining deterministic and agentic AI for precise root-cause analysis, predictive detection, and proactive remediation across modern, multicloud environments.

“By bringing together real-time, AI-powered observability from Dynatrace with ServiceNow’s AI-powered IT Service & Operations Management, we’re empowering IT teams to move beyond traditional operations into a new era of proactive systems that continuously learn, adapt, and self-heal at scale.”
— Rahul Tripathi, group vice president and general manager, ITSM and ITOM at ServiceNow

ServiceNow provides out-bound intelligence, transforming observability insights into immediate, reliable action. Incidents are automatically created, enriched, routed, and remediated with full business context, reducing MTTR and operational overhead.

Here’s how the combined solution enables closed-loop operations:

1. Proactive detection and response. Dynatrace Davis® AI continuously detects performance, availability, and resource anomalies before they impact users. When a problem arises, ServiceNow automatically generates a context-rich ticket, complete with root cause, dependency mapping, and business impact, eliminating manual triage and ensuring the right teams act fast.
2. Continuous environment synchronization. Dynatrace Smartscape® automatically maps every service and dependency across your environment. Through the Service Graph Connector for Observability – Dynatrace, that real-time topology feeds directly into the ServiceNow CMDB, keeping it accurate and actionable without manual updates.
3. Closed-loop automation. When Dynatrace detects specific problems, such as memory saturation, capacity constraints, deployment anomalies, ServiceNow workflows automatically initiate remediation actions. Each workflow verifies the outcome via Dynatrace APIs, confirming the root cause is resolved, not just masked.

This is closed-loop remediation in practice: detect, diagnose, act, validate, and learn – continuously.

When self-healing IT meets real-world scale

Organizations worldwide are already realizing measurable gains from this unified approach:

• CareSource reduced MTTR by >98% and cut downtime from 12 hours to 2 through automated self-healing workflows powered by Dynatrace and ServiceNow, while increasing observability adoption by 450%.
• BT Digital achieved a 93% reduction in mean time to detection and resolution. When a critical Apache process failed, Dynatrace detected it in 2 minutes, and ServiceNow remediated it automatically in under 6.
• Commerzbank realized a 70% reduction in major incidents and 96% faster MTTR – from 30 hours to 1 – as part of its journey toward ticket-free IT operations. (October 2024)

These are strong indicators of a deeper transformation, from reactive operations to proactive, self-healing systems that elevate the role of IT from maintenance to innovation.

A trusted foundation for Zero Outage outcomes

The Dynatrace and ServiceNow partnership creates a reliable, AI-powered foundation for Zero Outage outcomes. It combines:

• Davis® AI, the Dynatrace hypermodal AI engine for deterministic root-cause analysis and predictive insights.
• Smartscape®, dynamic topology mapping that continuously updates ServiceNow’s CMDB.
• OneAgent®, unified instrumentation for complete, code-level observability across any environment.

Together, these capabilities give ServiceNow workflows the trustworthy, real-time context needed to act autonomously. As organizations mature, they evolve through stages of operational intelligence, from visibility to automation to prediction, ultimately achieving self-healing IT ecosystems.

For executives, this means lower operational costs and higher reliability. For ITOps, SRE, and platform teams, it means faster resolution, fewer alerts, and more time for strategic innovation.

Getting started: from insight to autonomy

The path to autonomous operations begins with a solid observability foundation:

1. Deploy Dynatrace OneAgent® for full-stack visibility.
2. Integrate ServiceNow using certified apps available in the ServiceNow Store:
   • Service Graph Connector for Observability – Dynatrace
   • Dynatrace Incident Integration
   • Dynatrace Workflows for ServiceNow
3. Automate remediation for common, high-frequency scenarios.
4. Expand continuously toward predictive and self-healing automation.

Put autonomous operations into practice

Experience how Dynatrace and ServiceNow combine intelligent observability and automation to turn reactive operations into proactive innovation.

The post Intelligence in, intelligence out: How Dynatrace and ServiceNow are powering autonomous IT appeared first on Dynatrace news.

The NVIDIA Enterprise AI Factory addresses the rapidly evolving needs for AI infrastructure to support the rise of agentic AI. Since its launch, customers have leveraged this validated design to build agents by following structured methodology and recommended frameworks, which simplifies deployment and configuration while facilitating the implementation of AI factories in both on-premises and hybrid cloud environments.

Dynatrace has been an integral part of this initiative. Dynatrace full-stack AI and LLM observability helps organizations move forward with confidence in building their AI and agentic AI initiatives.

Observable AI: Turn a black box into a glass box to build confidence

With the publication of comprehensive guidelines, it’s simpler than ever for Dynatrace customers to set up and start monitoring their full-stack NVIDIA enterprise AI infrastructure, including its key tiers and components. Covering the infrastructure layer from GPUs to Kubernetes, NVIDIA NIM microservices, NVIDIA NeMo, and other technologies up to the application layer, Dynatrace observability enables customers to confidently run and operate complex AI workflows on NVIDIA infrastructure.

In parallel, Dynatrace has worked to significantly advance our AI and LLM observability offering by introducing the following:

• A dedicated app experience for AI observability with out‑of‑the‑box analytics, auto‑instrumentation, targeted metrics, a debugging flow, and ready‑made dashboards.
• Data governance and audit trails for AI services, turning governance into a competitive advantage.
• Model versioning and A/B testing to compare model performance across versions.
• Multi-modal agent explainability and tracing throughout RAG pipelines and agentic frameworks.

These improvements address challenges such as missing observability insights, scale, sovereignty, and trust. This empowers organizations to operationalize AI by building trust and monitoring guardrails; providing analytics capabilities to detect user-facing issues; helping SREs and AI-native engineers maintain performance, reliability, and security; and reducing cost across the agentic, AI, and LLM stack.

Privacy and security lead the way to scaling AI with confidence

AI is delivering significant productivity improvements, with 66% of senior executives reporting positive trends in productivity, according to PwC’s AI Agent Survey. This momentum is driving the demand to manage AI expenditures, enhance the decision-making quality of agents, and optimize development through visibility into AI components’ behavior in production environments — from pilot projects to full-scale operations.

However, sensitive data considerations and strict compliance requirements often impede progress, preventing organizations from fully realizing the benefits of AI adoption. As enterprises prioritize data privacy, regulatory compliance, and data sovereignty, there is an increasing need for high-performance NVIDIA AI infrastructure alongside frameworks designed to preserve control, trust, and autonomy in AI development.

In a recent blog on sovereign AI, NVIDIA shares strategies for nations and enterprises to develop AI factories that uphold local governance, security, and cultural values. Combining such factories with the Dynatrace advanced observability solution enables organizations to operationalize AI at scale — building secure and scalable agents, deployed on premises or in hybrid environments.

From privacy needs to public-sector requirements: NVIDIA AI Factory for Government

At NVIDIA GTC Washington, D.C. today, NVIDIA AI Factory for Government was announced, in support of the needs for regulated environments to drive AI initiatives. The U.S. Office of Management and Budget’s decision to establish scorecards for agencies’ AI maturity and management is in line with a 2024 Gartner Research forecast that more than 60% of government organizations will be prioritizing their investments in business automation by 2026 — up from 35% in 2022. The NVIDIA AI Factory for Government is a full-stack, end-to-end reference design that brings the power of reasoning AI to federal organizations. It helps organizations unlock productivity gains just like it does for enterprises, from service delivery to threat detection and day-to-day operations.

Built on the experience of deploying internal AI factories, the reference design offers guidance for deploying agentic AI, physical AI, and high-performance computing workloads on premises and in hybrid cloud environments, while meeting the compliance needs of federal and other secure organizations. The NVIDIA AI Factory for Government reference design includes NVIDIA Blackwell accelerated computing and NVIDIA networking, NVIDIA-Certified Systems, NVIDIA AI Enterprise software, NVIDIA Nemotron open models, and third-party software from AI leaders, all validated by NVIDIA.

Dynatrace delivers trusted observability and automation for regulated environments

Dynatrace has always been committed to supporting the public sector and other industries with regulatory requirements by providing customers with capabilities to control data flow through its lifecycle and manage sensitive data from ingestion to deletion, as well as global deployment options to meet data residency requirements, configurable retention times for different data types and use cases, unique encryption keys for customer’s stored data, and more.

Our dedication is reflected in customers’ success stories from regulated industries, as well as a growing list of global and local certifications, such as ISO 27001, SOC 2 Type II, CSA STAR 2, ENS, Tisax, and others. Find out more about our certifications and supported compliance frameworks in our Trust Center. For organizations also navigating evolving sovereignty requirements, our approach to digital sovereignty demonstrates how Dynatrace combines technical innovation with policy alignment to deliver trusted solutions globally.

Benefit from full-stack observability for end-to-end validated design

Dynatrace observability with the NVIDIA AI Factory for Government reference design enables organizations to accelerate the deployments of their AI agents and applications for federal and enterprise environments, and benefit from real-time, AI-powered insights.

These benefits range from improved scalability and performance to reduced complexity and total cost of ownership by simplifying processes, mitigating deployment risks to improved data security and compliance.

Visit the Dynatrace Playground to experience the possibilities of AI and LLM observability, and discover how Dynatrace is accelerating enterprise AI at scale.

Dynatrace and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies. All other trademarks are the property of their respective owners.

The post Unlocking productivity and trust: Dynatrace observability in NVIDIA AI Factory appeared first on Dynatrace news.

Key insights

• Confidence where it counts: Real time, contextual data turns AI from a black box into decisions you can see, explain, and trust.
• Outcomes, not features: Teams reduce risk, control cost, and improve answer quality by grounding AI in better data and context.
• Role-based paths: Leaders, platform and SRE, AI engineering and ML ops, application developers, and security each have clear ways to act.
• Dynatrace 3rd generation foundation: Grail unifies telemetry, Smartscape maps live dependencies, and Davis AI turns insight into explainable actions.

The shift is underway

Your AI helped ship a feature customers love. Then a bad answer slips through, support tickets rise, and no one can explain why. Was it a model change, a prompt tweak, or missing context from an upstream service? When AI behaves like a black box, you cannot manage risk, cost, or trust.

You’re not alone. Budgets and expectations reflect the push to make AI observable and accountable. In our 2025 State of Observability data, 70% of organizations increased observability spend in the last year, and 75% expect to increase it again. Leaders often see the biggest returns from optimizing model configurations, detecting anomalies in model outputs, and automating remediation. Most believe that AI decisions still require a human check because proof matters.

At Dynatrace, we believe the difference is the data. Grail keeps observability, security, and business telemetry together in real-time, connected context. Smartscape maintains a real-time map of services, dependencies, and releases. Davis AI reasons over this context to explain cause and effect and suggest what to do next. Together they make AI explainable and governable, so teams can move faster without losing trust.

Here’s how your teams can build confidence in AI

Choose models with real context

Run evaluations on real workloads, not synthetic tests. Compare latency, cost per answer, and relevancy side by side, then use prompt traces to see why outputs differ. Your team picks the right AI model for the job with evidence, not guesswork. Use canary rollouts to validate the winning configuration on a small slice of traffic, then scale with confidence while a policy records who approved the change.

• This showcases: AI model evaluation and versioning across providers, prompt tracing and debugging, cost and performance insights tied to real services.

Explain every decision on demand

Capture inputs, prompts, context, and model versions so you can show how a decision was made. Export an auditable bundle, attach it to your review, and keep evidence with the workflow. Approvals move faster because proof is built in. Evidence travels with the workflow so leaders can audit decisions without meetings.

• This showcases: Exportable audit evidence, lineage across prompts and context, review-ready artifacts for governance.

Spot and prevent drift

Watch for quality changes after releases or upstream shifts in agentic AI systems that span multiple models. Because data lives together in Grail and dependencies are mapped in Smartscape, Davis AI detects drift, explains the likely root cause, and recommends next steps. Your team rolls back or tunes with confidence and documents the outcome.

• This showcases: End-to-end drift detection and explanation across services and models, causal analysis tied to releases, and actionable root cause context.

Move fast with guardrails

Set policy rules that pause risky paths for humans and promote safe improvements automatically. When a policy is triggered, the flow pauses for approval. When targets are met, changes move forward on their own. Speed and accountability rise together.

• This showcases: Policy-driven automation, human-in-the-loop controls, measurable targets for quality and cost.

One place to work, together

Platform and SRE, AI engineering and ML ops, developers, and security see the same facts and act in the same space. A built-in experience brings multi-cloud and multi-model views together with alerts, traces, and reviews. Teams focus on outcomes because data and context are already aligned, and agentic tracing makes complex multi-LLM paths explainable.

• This showcases: Unified, role-aware experience in the Dynatrace 3rd generation platform, powered by Grail for data, Smartscape for live topology and dependencies, and Davis AI for reasoning.

How it works

Dynatrace brings key, differentiated capabilities together, so AI becomes observable, governable, and improvable. Grail stores and relates telemetry with context as a single source of truth. Smartscape maps real-time topology and dependencies. Davis AI analyzes that knowledge to explain issues, correlate cause and effect, and drive or recommend actions. These explainable insights show up in built-in experiences so teams can act quickly in one place, with the same context everyone trusts. Together, they give you a glass-box view of AI across your environment.

Use cases you can try today

Platform Engineering and SRE

Use progressive delivery to your advantage and run a canary release with two model versions on a real service. Compare key metrics and data points, such as latency and error rates, then set an automated rollback if quality drifts beyond the threshold.

AI engineering and ML ops

A/B test two providers for a summarization workload. Measure token usage, cost per answer, and relevancy. Use prompt debugging to tune instructions, then promote the winning configuration.

Application developers

Trace a problematic response from UI to model call. Inspect the prompt, context, and dependencies, then commit a configuration change to fix a latency regression.

Security and compliance

Export an audit bundle for a high-risk workflow. Attach it to your review ticket and record a human approval step. Automate export on schedule.

The market signal

Leaders are investing to make AI observable and governable. From our 2025 State of Observability report, 70% increased observability budgets last year, with 75% of those surveyed planning to increase again next year. Teams expect the biggest return from optimizing model configurations, detecting anomalies in model outputs, and automated remediation. Ninety-eight percent already use AI to support security compliance.

What this means for your organization

For leaders

• Improve decision quality with model evaluations grounded in a real workload context.
• Reduce risk with audit trails that satisfy compliance.
• Control spend by measuring cost next to performance.

For Platform Engineering and SRE teams

• Standardize model rollouts with versioning and repeatable A/B tests.
• Tie prompt traces to services, releases, and alerts to cut MTTR.
• Automate safe rollbacks or configuration changes with policy guardrails.

For AI engineering and ML ops

• Compare models and versions on latency, cost per answer, and relevancy.
• Use prompt debugging to tune instructions and ground responses in context.
• Track drift and trigger-governed actions when quality drops.

For application developers

• See how AI components behave in production next to service health.
• Debug prompts without leaving the release context.
• Ship changes with data on performance and cost impact.

For security and compliance

• Export signed audit trails that show inputs, outputs, models, and context.
• Prove who did what, when, and why for policy and regulatory reviews.
• Route risky outputs for human approval and record the decision.

The post From black box to glass box appeared first on Dynatrace news.

Organizations are integrating artificial intelligence into their operations at a rapid pace. This transformation is changing how businesses work, innovate, and compete. The State of Observability 2025 report confirms that while 100% of responding organizations are now using AI, how they’re using it is often fragmented.

Senior IT and business leaders should pursue a unified strategy to link AI initiatives with clear business results. A practical solution that’s gaining traction is AI-powered observability, which is evolving from a technical monitoring platform or tool suite into a strategic control plane for AI transformation.

The emergence of AI technologies within observability presents a novel opportunity for leaders to drive tangible business value from data across the full stack. Insights from our research highlight several key trends that are reshaping priorities so you can create a new action plans for sustainable growth, efficiency, and resilience.

Key takeaways from The State of Observability 2025 report

• Observability is a fast-growing AI use case. With 75% of organizations increasing their observability budgets, it’s clear that leaders see it as a critical investment for managing AI. In fact, AI capabilities are now the #1 criterion for selecting an observability solution.
• The AI trust gap is real. Humans are still very much in the loop. A significant 69% of AI-powered decisions are verified by humans, and one in four leaders believes improving trust in AI should be a top priority.
• AI-powered observability encompasses application security, DevOps, and sustainability. Nearly all security leaders (98%) use AI for security compliance, and 69% have increased budgets for AI-powered threat detection. At the same time, more than 70% of organizations use observability to manage sustainability initiatives.
• Business observability is on the rise: While only 28% of organizations currently use AI to align observability data with business KPIs, the opportunity is clear. Leaders are moving toward real-time solutions that connect technical performance directly to customer experience and business agility.

These findings illustrate that observability is no longer just about keeping systems running. It’s about optimizing performance, reducing risk, and aligning every aspect of your technology stack with strategic business goals.

How AI-driven insights translate into business results

Being able to understand what’s happening in all dimensions of your operating environments presents some clear business benefits. Here are just a few.

Lower risk and faster response
With AI-assisted detection and guided remediation, teams can reduce the impact of incidents and significantly cut response times.

Lower unit cost and carbon impact
By correlating observability telemetry with cloud spend, energy usage (kWh), and CO₂ emissions, leaders can uncover operational waste and identify clear opportunities for savings.

Stronger security posture
Integrating security and observability enhances compliance, extends threat visibility, and improves the overall quality of incident response.

Greater AI trust and accountability
Human-verified guardrails and comprehensive audit trails improve the transparency and trustworthiness of AI-driven actions.

Clear KPI alignment
It’s now possible to tightly link services and customer journeys to business-critical metrics like MTTR, SLO attainment, cost per request, revenue at risk, and customer experience, enabling informed, real-time decisions.

While these insights are a good start, turning them into an action plan is the critical next step.

A 90-day action plan to drive measurable results and understand your business

For executives looking to deliver measurable ROI from AI projects by harnessing the power of AI-driven observability, here’s an actionable 90-day plan.

Why Dynatrace for reliable agentic AI projects

Dynatrace provides the context and controls leaders need to run AI like a business program:

• Contextual analytics of unified observability, security, and business data.
• Advanced predictive, causal, and generative AI to provide deterministic answers and validate generative AI results.
• Preventive operations through ecosystem workflow automation capabilities.

If you’re seeking to turn AI-driven observability into a source of competitive advantage, explore what’s possible with Dynatrace and take the next step toward resilient, agentic AI projects.

The post The State of Observability 2025: Business impact, key trends, and a 90-day plan for decision-makers appeared first on Dynatrace news.

Executives and technology leaders are prioritizing AI observability to reduce risk, lower unit cost, and accelerate delivery, aligning to business objectives.

The State of Observability 2025 report reveals how organizations are moving from experimenting with AI to integrating it into core operations. Not surprisingly, 100% of responding organizations now use AI in some capacity. But this universal adoption isn’t uniform. Data management, AI governance, and security are the most common AI use cases, with observability growing significantly.

As organizations seek to realize ROI on their overall AI investments, observability is clearly emerging as the key to unlocking AI value while mitigating its inherent risks. In other words, AI observability is becoming a prerequisite for the success of AI initiatives.

Why observability is now a C-suite imperative

Executives now recognize that a comprehensive observability strategy is essential for reducing AI risk, lowering unit costs, and accelerating service delivery. Observability is emerging as a vital intelligence layer for managing complex AI initiatives and aligning them with strategic business goals. Further, AI capabilities within observability platforms are becoming a determining factor for selecting an observability vendor.

The report’s findings underscore this shift:

• Observability budgets are increasing: 70% of organizations increased their observability budgets this year, and 75% plan to increase them again next year. These increases signal the importance and value leaders are placing on this capability for the success of their business goals.
• AI capabilities are now the #1 criterion for choosing an observability platform: For the first time, AI capabilities (29%) have surpassed cloud compatibility as the primary criterion for selecting an observability platform. This highlights the market’s demand for intelligent, automated solutions.
• The AI trust gap is real: Despite widespread AI adoption, a significant trust gap remains. Humans verify 69% of all AI-driven decisions, and 70% of organizations increased budgets for trust and transparency initiatives this year. This indicates that while leaders are eager to use AI, they require guardrails designed to enhance its reliability.

AI is expanding the value of observability across security, sustainability, DevOps, and more

Using AI for security compliance, sustainability, and real-time DevOps automation initiatives is on the rise, fueling the evolution of agentic AI—autonomous systems that plan and execute tasks.

AI-powered threat detection is influencing budget priorities

Security is a prime example of how AI and observability are converging. A staggering 98% of security leaders report using AI to manage security compliance, and 69% are increasing budgets for AI-powered threat detection. Enhancing threat visibility is the top expected growth area for AI over the next five years. By converging security data with observability telemetry, organizations gain faster time-to-contain and fewer customer-impacting incidents.

AI pays dividends for sustainability and managing costs

The scope of observability is also expanding to include environmental sustainability. Our research shows that 70% of organizations use observability to monitor and manage their sustainability initiatives, which in most cases also drives cost reductions. A full 64% report growing budgets for observability-aligned sustainability efforts. Correlating telemetry with resource consumption reduces cost per request and CO₂ emissions by linking telemetry to spend and energy.

Real-time DevSecOps automation is giving rise to agentic AI

The ongoing expansion of AI into combined DevOps and security (DevSecOps) automation represents another powerful shift. Up to 50% of DevSecOps leaders currently use real-time automation, with adoption expected to grow to 70% in five years, driven by use cases like security risk mitigation and anomaly detection. The focus on agentic AI promises high ROI (41%) and is reshaping incident response, infrastructure management, and debugging. Real-time observability with natural language interaction results in AI systems with a shorter time to value through safe, policy-gated actions.

From data to business impact: Closing the KPI gap

While the potential is clear, many organizations are still working to connect observability data to tangible business outcomes. Currently, only 28% use AI to align observability data with key performance indicators (KPIs). This “KPI gap” represents a significant opportunity.

Leaders who successfully bridge this gap can transform their operations. About 22% of leaders report that converging real-time data and AI-driven automation with observability positively impacts business agility, so they can respond more quickly to market changes and customer demands. By tying technical performance metrics like mean time to resolution (MTTR) and service level objectives (SLOs) directly to business metrics like cost per request, revenue at risk, and customer experience scores, leaders can gain real-time insight into how technology performance affects business agility and financial efficiency.

The mandate for observability in the AI era

As organizations increasingly rely on AI, they are also turning to observability to make these complex systems more explainable, reliable, and auditable. Observability is no longer just about monitoring systems. It’s about providing the intelligence and control needed to steer the enterprise through AI transformation. AI-driven observability provides the foundation for lowering risk, strengthening security, and aligning every technological decision with strategic business value.

To explore these findings in greater detail and build a comprehensive strategy, get the State of Observability Report 2025 below.

The post State of Observability 2025: AI use cases are growing as business leaders seek to build AI trust and ROI appeared first on Dynatrace news.