Organizations are racing to integrate AI agents into developer and operations workflows. Developers are using coding agents to help build new features and even entire applications, while simultaneously building agents to automate business processes, or even orchestrate multi-agent workflows. Companies are discovering what works, what breaks, and what moves the needle for developers in the agentic era. Across industries—from telecom to aerospace to finance—a few patterns are emerging.

Based on presentations from customers at Perform 2026, here are five real-world lessons for operationalizing agentic AI in developer workflows.

1. Give agents real-time access to telemetry data

Teams need real‑time, deterministic signals to govern autonomous agents safely. This message came through clearly at Perform.

At TELUS, engineers were doing what most teams do early in their AI journey: copying logs, pasting traces, and moving between dashboards and chat tools. The models could reason, summarize, and even suggest fixes – but they had no direct pathway to telemetry data. Without structured access to Dynatrace Intelligence, GKE, and change events, the agents lacked runtime awareness.

The turning point came when TELUS stopped stitching together ad-hoc data pulls and instead enabled agents to access telemetry through MCP. Instead of manually stitching context together, agents could retrieve live data including traces, logs, and dependency data directly through shared access patterns.

The result: AI grounded in the same real time observability data engineers use to investigate end-to-end.

That’s why MCP was pivotal. As TELUS described it, MCP replaced ad-hoc fetches with a consistent model for safely accessing structured infrastructure and application context. AI agents no longer relied on static or point in time snapshots; they operated against live state and followed shared rules of engagement.

Lockheed Martin faced a similar challenge. Their teams were navigating multiple tools, each with its own interface and data models. AI could help, but it needed real time, unified access across Dynatrace, ServiceNow, and other tools. As Lockheed Martin Chief Observability Engineer David Walker described it, MCP became “the universal translator” that provided a “universal language” for agents to access all of them – transforming siloed tools into a single context layer.

2. Meet developers where they are

The organizations moving fastest in agentic AI are treating developer experience as a first-class concern. The pattern is simple: bring AI and telemetry into the tools where developers already spend their time.

At TELUS, observability isn’t something engineers leave their IDE to access. Using local MCP servers in VS Code, agents can retrieve Dynatrace metrics, logs, and problem details directly within the editor.

They extended this further with n8n to automate workflows and embedded AI into Slack through IRIS, their conversational incident assistant. Engineers ask natural language questions — “What’s the status of service X?” — and IRIS routes the request to the appropriate n8n workflow and MCP endpoint.

At Autodesk, more than 80% of developers already use AI‑assisted IDEs. Many were independently wiring MCP servers into tools like Cursor to access telemetry while coding. Autodesk responded by formalizing this approach with a hosted, governed MCP platform integrated directly into those assistants.

Similarly, Lockheed Martin built MCP servers tailored for agentic assistants in VS Code, enabling developers to retrieve metrics, logs, and problem details without leaving their editor.

3. Standardize telemetry and enrich it with domain context

API access is only the starting point. AI is only as effective as the structure and context behind the data it can access.

Lockheed Martin customized MCP servers with internal knowledge, such as which systems were mission‑critical, how their baselines typically behaved, or which team owned specific services. They called these “Lockheed‑Martin’ized MCPs.”

In one comparison, the generic configuration produced about a 60% success rate. The contextualized version increased that to 95%. As Chief Observability Officer David Walker put it, the added domain knowledge was the difference between “noise and signal.”

Autodesk provided one of the clearest examples. After years of acquisitions and organic growth, they found themselves with 20+ observability tools and seven different tracing systems – a patchwork that made incidents slow and painful to unravel. Standardization was the solution explained Alex Bicalho, Autodesk Senior Director of Engineering, Developer Platform Services during his presentation. They adopted OpenTelemetry as a universal instrumentation layer and consolidated metrics and traces in Dynatrace.

United Wholesale Mortgage extended this concept further by promoting business events—structured signals extracted from logs via OpenPipeline. This created a telemetry layer that mapped technical anomalies directly to customer-facing processes and business impact.

Instead of asking only, “What broke?” the team could ask, “What business process is impacted, and how do we prevent it?”

4. Observe and govern the AI stack itself

As agents become part of developer workflows, they must be treated with the same security and operational standards as existing systems.

Macquarie Group recently launched a customer-facing agent called Q for its Australian retail banking customers. “We needed total visibility, so we built a Dynatrace dashboard that has usage, effectiveness, cost, and downstream dependency health,” says Phillip Grasso-Nguyen, head of reliability for Macquarie’s Banking and Financial Services division. “That gave us the confidence to release.”

At TELUS, their automation backbone, powered by n8n and MCP servers, had become mission‑critical. To ensure reliability, they instrumented n8n itself with OpenTelemetry, routed metrics into Dynatrace, and correlated that data with Kubernetes and log analytics.

In other words, they treated their AI automation stack like any other production service: observable, measured, and governed.

5. Close the loop from insight to action

When teams put the right structure, data, and guardrails in place, operational feedback loops shrink from hours or days to minutes.

Agents don’t misbehave out of malice. They often misbehave because they try too hard. Autodesk provided a vivid example: As developers began experimenting with MCP servers from across the internet— integrating them into Cursor, Copilot Cloud, and other AI‑assisted IDEs—agents began issuing extremely broad observability queries, hammering GitHub, CI systems, and telemetry backends.

As Alex Bicalho, Autodesk Senior Director of Engineering, Developer Platform Services, described it, the agents were “trying to find an atom in the solar system.” They were doing exactly what they were asked, just without guardrails.

The solution was governance. Autodesk created a hosted, governed MCP platform, where each tool and workflow had defined scope, limits, and predictable behavior. Macquarie Group has an SRE agent that pulls real-time data from the Dynatrace API to help act on issues before they become incidents.

TELUS demonstrated this clearly with IRIS, their Slack‑based incident assistant. IRIS blends telemetry, logs, change history, and conversation transcripts into real‑time summaries during active incidents.

While engineers troubleshoot on an incident management call, IRIS ingests Dynatrace data and generates a rolling update every 15 minutes: what happened, who did what, what changed, actions taken, open questions, and the most likely root cause.

The impact is practical. Executives don’t interrupt status updates. Engineers don’t repeat themselves across Slack threads. This is closed-loop automation in practice. Everyone operates from the same continuously-updated view.

Other companies are leaning into this vision as well. Macquarie has an SRE agent that pulls data from the Dynatrace API to help act on issues before they become incidents.

United Wholesale Mortgage uses Business Flow visualizations that connect performance degradation directly to customer-facing processes. Remediation becomes faster and more aligned with business impact.

What’s next?

Agentic AI is already reshaping how developers build, observe, and deliver software. The organizations moving faster aren’t just experimenting with models – they’re integrating AI into their telemetry, workflows, and systems, from development through testing and into production.

The organizations succeeding with agentic AI are rebuilding their developer workflows with observability as the real-time control plane that gives agents context, guardrails, and the ability to act safely and autonomously.

The post Five real-world lessons for building developer workflows in the agentic era appeared first on Dynatrace news.

The agentic era has arrived, bringing together human insight and autonomous intelligence in entirely new ways.

The pressure is high for enterprises to become “AI-first,” but as we explored in our inaugural Agentic AI Research Report, most organizations are stuck in pilot-mode. Many are struggling to achieve meaningful outcomes, even as they invest ever more heavily on AI.

AI success requires a shift in how we think about operations. We’re moving from a world of manual changes and troubleshooting to one of autonomous systems. Observability tooling needs to adapt to this new reality. It’s not enough to merely provide insights into a system. Organizations need platforms that enable agents to act upon intelligence safely and reliably. That’s why Dynatrace has evolved into an agentic operations platform that enables autonomous, intelligent collaboration across development, operations, and business workflows.

At our flagship event Perform 2026, we’re expanding our platform to deliver the following capabilities that will carry organizations into the human + AI collaboration era.

Dynatrace Intelligence: The new foundation for reliable agentic AI

Organizations struggle with the growing complexity of cloud and AI‑driven digital ecosystems. Leaders have valid concerns about hallucinations, unreliable multi‑step agent workflows, and the inability of off-the-shelf models to process petabytes of heterogeneous observability data.

The solution isn’t to throw more AI at the problem and hope for the best. Simply adding AI agents to legacy workflows doesn’t work. Closing this gap requires an AI system that doesn’t just bolt onto workflows but rethinks how decisions are made and actions are automated.

Dynatrace Intelligence is an agentic operations system at the core of the Dynatrace platform and serves as the reasoning and decision-making layer for all the agentic layers built upon it. It fuses deterministic AI with contextual analytics to ground agentic decisions in real‑time facts and to create more reliable agentic workflows. It also coordinates different kinds of agents while minimizing hallucinations so organizations can trust automated actions.

The new Smartscape®: A source of truth for AI

Modern IT teams often operate with only a partial understanding of what’s running in their environment, especially as cloud‑native architectures, Kubernetes, and adaptive AI agents expand and shift at runtime. You can’t troubleshoot, secure, or optimize what you can’t see. This incomplete picture slows incident response, drives misaligned investments, and increases risk.

The new Smartscape® real-time dependency graph creates a shared, real‑time source of truth that both humans and AI agents use to understand what is actually happening across the environment and to act with confidence. It provides a precise, always‑current view of every entity and dependency in your digital ecosystem, including cloud and Kubernetes objects, domain‑specific metadata, and agentless cloud‑discovered components. The new Smartscape® capability underpins Dynatrace Intelligence, providing the context needed to empower teams and agents to act with confidence, using real‑time topology to detect changes, assess impact, and respond automatically.

From reactive to proactive with agents

Far too many IT teams are forever stuck in firefighting mode. They’re so busy troubleshooting existing systems that they have no time to understand why issues keep recurring or how to prevent them. Signals flood in faster than humans can interpret them, incidents surface without context, and critical decisions must be made with only fragments of the real picture. In this chaos, teams are forced into reactive loops that drain capacity, slow innovation, and make reliable operations nearly impossible.

Dynatrace Intelligence Agents are designed to break this cycle. Whether you use our own ready-made agents or build your own, the Dynatrace Intelligence agentic ecosystem provides the capabilities to continuously detect changes, assess impact, and automatically respond to emerging conditions, helping organizations shift from firefighting to proactive operations.

This is how the Dynatrace platform goes beyond insight, advancing to autonomous action by auto-remediating, auto-preventing and auto-optimizing.

Dynatrace Assist: Your portal into the agentic universe

Modern IT teams aren’t lacking in data or metrics. The problem is that they must jump between different dashboards, tools, and consoles to understand their systems and find the information they need. This slows investigation, increases cognitive load, and leads to delays, misdiagnoses, and inconsistent decisions.

We’re launching Dynatrace Assist to eliminate this constant context switching. Assist brings the full power of Dynatrace Intelligence to your fingertips. Instead of moving between dashboards, queries, and tools, you simply start by asking a question and let Dynatrace Intelligence do the work. Assist pulls together context from the Dynatrace Grail® unified data lakehouse, maps relationships utilizing the Smartscape® real-time dependency graph, and collaborates in context with Dynatrace agents.

As AI becomes a collaborative partner rather than a separate tool, Assist provides the conversational interface that brings agentic intelligence directly into human workflows—reducing cognitive load and enabling natural, real‑time co‑reasoning.

End-to-end intelligence with next generation Dynatrace RUM

Even the smartest agentic systems need to understand how real users experience your applications. Backend signals alone can’t reveal where friction occurs, how users navigate modern single‑page and mobile experiences, or which issues matter most to the business.

The next‑generation Dynatrace Real User Monitoring closes this gap with Grail-powered analytics that unifies frontend behavior with backend intelligence. It automatically captures modern user interaction patterns and couples them with precise, real‑time system insights, enabling teams and agents to detect issues faster, prioritize what truly impacts customers, and optimize digital experiences with confidence.

Dynatrace MCP Server: The connective tissue between agentic systems and Dynatrace Intelligence

Modern AI assistants and automated workflows are being asked to make high stakes decisions without access to the complete, real-time context of the systems they’re meant to support. Fragmented data, disconnected telemetry, and incomplete understanding of how services relate prevent AI from making reliable recommendations or taking safe autonomous actions.

The Dynatrace MCP Server provides the governed bridge between AI ecosystems outside of Dynatrace and the production‑grade insights generated by Dynatrace Intelligence. Using the open Model Context Protocol, it delivers real‑time insights directly into agentic workflows. This gives autonomous agents and other AI tools the observability truth they need to reason accurately, take decisive actions, and operate safely at scale.

Building the future of autonomous operations

Perform 2026 marks a turning point for agentic AI Adoption. We’re shifting from human‑paced management of systems into a world where systems anticipate, adapt, and advance on their own. For the first time, organizations can operate with an intelligence layer that doesn’t wait to be directed but collaborates, reasons, and evolves in real time. Teams will finally be able to focus on the work that moves them forward, rather than the work that holds them back.

The post Dynatrace introduces a new foundation for agentic AI at Perform 2026 appeared first on Dynatrace news.

Key takeaways:

• Perform 2026 will demonstrate how blending human creativity with agentic AI unlocks new levels of business impact to thrive in the agentic AI era.
• Attendees will gain actionable strategies for moving stalled AI pilots to scalable, high‑value AI adoption.
• Perform will reinforce how Dynatrace drives competitive advantage in the age of autonomous intelligence for every practitioner, leader, and partner.

______

Perform 2026 will be held January 26–29 at the Venetian in Las Vegas. If you’re unable to join us in person, be sure to register to attend virtually—or view sessions on-demand afterward—so you don’t miss out.

Ahead of the event, we released the Agentic AI Research Report, an inaugural global study focused on how observability and reliability determine the successful operationalization of agentic AI. Its key findings set the stage for Perform 2026:

• From pilot to production: 72% of organizations are now using AI agents for IT operations and DevOps, with rapid growth in customer-facing and non-IT applications.
• Top challenges: Security, data privacy, reliability, and the need for robust observability as a control plane remain critical hurdles.
• Human + AI collaboration: Success depends on intentional human oversight, resilience, and real-time decision-making—underscoring the vital role of observability in scaling agentic AI and autonomous operations.

At Perform 2026, these insights come to life through mainstage keynotes, deep-dive sessions, and real-world success stories, all focused on helping organizations harness AI with confidence and trust.

Be sure to check out the full agenda to find out what you’ll learn. Here are a few highlights:

Succeeding together in the age of AI

Dynatrace CEO Rick McConnell will set the stage Wednesday by outlining how enterprises can advance toward autonomous operations and shift from reactive problem‑solving to preventive operations—a model where intelligent systems anticipate issues before they impact the business. He’ll share his vision for how organizations can partner with platforms designed to harness real‑time, contextual data to operate with greater precision, speed, and resilience.

Rick will also highlight the essential role AI‑powered platforms play in elevating operational maturity across modern digital ecosystems. As teams face growing complexity and mounting pressure to deliver reliable, scalable services, he will show how the Dynatrace platform helps unify data, automate decisions, and empower people to focus on higher‑value innovation rather than manual firefighting.

Also on Wednesday, CTO Bernd Greifeneder will detail how real‑time intelligence rooted in contextual data and causal reasoning enables organizations to understand their digital ecosystems with greater clarity and act with precision at scale. Just as importantly, Bernd will spotlight the growing importance of agentic collaboration, showing how AI agents, human expertise, and the broader partner ecosystem accelerate innovation when they operate as a unified system.

Empowering people: The human + AI workforce

On Wednesday afternoon, Chief Transformation Officer Colleen Kozak and Chief AI Officer Sol Rashidi will explore how organizations can build a workforce where humans and AI strengthen each other. Their sessions focus on the operating models, governance structures, and data foundations required to make agentic AI reliable at scale.

They’ll also discuss how to prepare teams for this new era and a practical path toward a workforce that thrives in the agentic world.

On Thursday, a fireside chat with gold medalist, innovator, and entrepreneur Shaun White will bring an additional lens to the theme of human achievement, showing how AI collaboration helps people reach new heights, whether in sports or in business.

Customer success stories: Transforming operations with AI

Across industries, customers are already proving what’s possible when intelligent systems and human expertise work together. This year’s lineup will share the lessons they’ve learned, including:

• United Airlines: Head of IT Operations, Observability, and Engineering Ramiro Zavala will share how they achieve end‑to‑end business observability to keep complex digital experiences running smoothly for millions of passengers.
• Princess Cruises: VP Cloud, Infrastructure, and Cloud Engineering Operations Rick Lapenna will discuss how AI-powered observability helps the company deliver operational excellence and frictionless customer experiences.
• Macquarie Group: Head of Reliability Phillip Grasso-Nguyen will discuss how observability drives reliability and success throughout the company, including their AI workloads.
• TELUS: Principal SRE Dana Harrison and Director, Site Reliability and Engineering Enablement Kulvir Gahunia will talk about Black Friday, their biggest day of the year, and how Dynatrace drives success by acting as the common language between the business level and the practitioners deep in the data.
• Autodesk: Senior Director of Engineering Alex Bicalho will unpack how AI-powered observability empowers greater automation and productivity for a development team of thousands.​
• Vodafone: Head of Engineering and Transformation Luke Bradley will explore how Vodafone moved from their traditional log monitoring solution to an AI-powered observability strategy.​
• Visa CashApp Racing Bulls Formula One Team: Peter Bayer, VCARB CEO, returns after one incredibly eventful Formula One racing season to unpack successes the team is seeing at both the data and AI-powered insight level and on the track.​
• Nationwide Building Society: Head of Service Operations Kate Bristow will explore how observability builds trust and brings business and IT together.

Partner focus: Driving innovation and value together

No company is an island. We’re working with partners from across the industry to build an ecosystem that meets the diverse needs of our customers.

Here are some of the partners you’ll find leading sessions at Perform 2026:

• AWS: Showcasing real‑time developer‑focused observability inside the IDE with Kiro powers, plus autonomous troubleshooting and issue resolution using the AWS DevOps Agent integrated with Dynatrace.
• DXC: Covering how organizations can simplify hybrid‑environment complexity through deep observability adoption, and how integrating Dynatrace with ServiceNow enables predictive, self‑healing, autonomous IT operations.
• Accenture: Providing best‑practice integration patterns for Dynatrace + ServiceNow to advance AIOps maturity, alongside guidance on building business‑journey dashboards that reduce MTTX and connect business and technical insights.
• AHEAD: Demonstrating how Dynatrace and ServiceNow come together to enable closed‑loop, autonomous IT operations, along with practical AIOps strategies for reducing noise, accelerating incident response, and scaling Dynatrace adoption across the enterprise.
• Microsoft: Highlighting 2026 product innovations and deeper Dynatrace–Fabric integration, plus broader updates across the Microsoft ecosystem that strengthen intelligent observability, cloud transformation, and AI‑first operations.

Other partners offering sessions include: SteadyBit, Tricentis, Red Hat, CDW, Crest Data, Nutanix, RHONDOS, ServiceNow, and Google.

Developers @ Perform: Built for people who ship code

Modern teams ship fast. Sometimes really fast. And when you’re pushing code every day, there’s not always time to slow down, experiment, or learn from what just happened. Most of the hard problems developers deal with—understanding how systems actually behave, catching issues early, working across teams—start way before production and change with every commit.

Developers @ Perform is a mix of familiar conference formats and more interactive, hands-on experiences. You’ll find structured talks when it makes sense, quick lightning talks when a story is best told in five minutes, and plenty of time to get your hands on a keyboard and build. You can pick up ideas in a lightning talk, then turn around and try them yourself in your IDE:

• The Developer Lab, a self-service environment where you can explore MCP, agentic AI, vibe coding, and cloud-native observability at your own pace.
• Vibe Coding activities powered by GitHub Copilot, a hackathon-style experience with guided sessions designed to encourage experimentation and creative problem-solving.
• Community recognition and open source discussions around OpenTelemetry, OpenFeature, cloud-native architectures, and AI-assisted development practices.

The post Human creativity meets agentic intelligence: Your guide to Perform 2026 appeared first on Dynatrace news.