The developer as conductor: Leading an orchestra of AI agents with the feature flag baton
A few weeks after Dynatrace acquired DevCycle, Michael Beemer and Andrew Norris discussed on The New Stack Makers podcast how feature flagging is becoming a critical safeguard in the AI era. By integrating DevCycle’s feature flagging into the Dynatrace observability platform, the combined solution delivers a “360-degree view” of software performance at the feature level. This closes a key visibility gap, enabling teams to see exactly how individual features affect systems in production.
As “agentic development” accelerates—where AI agents rapidly generate code—feature flags act as a safety net. They allow teams to test, control, and roll back AI-generated changes in live environments, keeping a human in the loop before full releases. This reduces risk while speeding enterprise adoption of AI tools. The discussion also highlighted support for the Cloud Native Computing Foundation’s OpenFeature standard to avoid vendor lock-in. Ultimately, developers are evolving into “conductors,” orchestrating AI agents with feature flags as their baton.
Learn more from The New Stack about the latest around AI enterprise development:
Why You Can't Build AI Without Progressive Delivery
Beyond automation: Dynatrace unveils agentic AI that fixes problems on its own
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The reason AI agents shouldn’t touch your source code — and what they should do instead
Dynatrace is at a pivotal point, expanding beyond traditional observability into a platform designed for autonomous operations and security powered by agentic AI. In an interview on *The New Stack Makers*, recorded at the Dynatrace Perform conference, Chief Technology Strategist Alois Reitbauer discussed his vision for AI-managed production environments. The conversation followed Dynatrace’s acquisition of DevCycle, a feature-management platform. Reitbauer highlighted feature flags—long used in software development—as a critical safety mechanism in the age of agentic AI.
Rather than allowing AI agents to rewrite and deploy code, Dynatrace envisions them operating within guardrails by adjusting configuration settings through feature flags. This approach limits risk while enabling faster, automated decision-making. Customers, Reitbauer noted, are increasingly comfortable with AI handling defined tasks under constraints, but not with agents making sweeping, unsupervised changes. By combining AI with controlled configuration tools, Dynatrace aims to create a safer path toward truly autonomous operations.
Learn more from The New Stack about the latest in progressive delivery:
Why You Can’t Build AI Without Progressive Delivery
Continuous Delivery: Gold Standard for Software Development
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The enterprise is not ready for "the rise of the developer"
Sean O’Dell of Dynatrace argues that enterprises are unprepared for a major shift brought on by AI: the rise of the developer. Speaking at Dynatrace Perform in Las Vegas, O’Dell explains that AI-assisted and “vibe” coding are collapsing traditional boundaries in software development. Developers, once insulated from production by layers of operations and governance, are now regaining end-to-end ownership of the entire software lifecycle — from development and testing to deployment and security. This shift challenges long-standing enterprise structures built around separation of duties and risk mitigation.
At the same time, the definition of “developer” is expanding. With AI lowering technical barriers, software creation is becoming more about creative intent than mastery of specialized tools, opening the door to nontraditional developers. Experimentation is also moving into production environments, a change that would have seemed reckless just 18 months ago. According to O’Dell, enterprises now understand AI well enough to experiment confidently, but many are not ready for the cultural, operational, and security implications of developers — broadly defined — taking full control again.
Learn more from The New Stack about the latest around enterprise developers and AI:
Retool’s New AI-Powered App Builder Lets Non-Developers Build Enterprise Apps
Solving 3 Enterprise AI Problems Developers Face
Enterprise Platform Teams Are Stuck in Day 2 Hell
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From Cloud Native to AI Native: Where Are We Going?
At KubeCon + CloudNativeCon 2025 in Atlanta, the panel of experts - Kate Goldenring of Fermyon Technologies, Idit Levine of Solo.io, Shaun O'Meara of Mirantis, Sean O'Dell of Dynatrace and James Harmison of Red Hat - explored whether the cloud native era has evolved into an AI native era — and what that shift means for infrastructure, security and development practices. Jonathan Bryce of the CNCF argued that true AI-native systems depend on robust inference layers, which have been overshadowed by the hype around chatbots and agents. As organizations push AI to the edge and demand faster, more personalized experiences, Fermyon’s Kate Goldenring highlighted WebAssembly as a way to bundle and securely deploy models directly to GPU-equipped hardware, reducing latency while adding sandboxed security.
Dynatrace’s Sean O’Dell noted that AI dramatically increases observability needs: integrating LLM-based intelligence adds value but also expands the challenge of filtering massive data streams to understand user behavior. Meanwhile, Mirantis CTO Shaun O’Meara emphasized a return to deeper infrastructure awareness. Unlike abstracted cloud native workloads, AI workloads running on GPUs require careful attention to hardware performance, orchestration, and energy constraints. Managing power-hungry data centers efficiently, he argued, will be a defining challenge of the AI native era.
Learn more from The New Stack about evolving cloud native ecosystem to an AI native era
Cloud Native and AI: Why Open Source Needs Standards Like MCP
A Decade of Cloud Native: From CNCF, to the Pandemic, to AI
Crossing the AI Chasm: Lessons From the Early Days of Cloud
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See Why GenAI Workloads Are Breaking Observability with Wayne Segar
What happens when you try to monitor something fundamentally unpredictable? In this featured guest episode, Wayne Segar from Dynatrace joins Corey Quinn to tackle the messy reality of observing AI workloads in enterprise environments. They explore why traditional monitoring breaks down with non-deterministic AI systems, how AI Centers of Excellence are helping overcome compliance roadblocks, and why “human in the loop” beats full automation in most real-world scenarios.
From Cursor’s AI-driven customer service fail to why enterprises are consolidating from 15+ observability vendors, this conversation dives into the gap between AI hype and operational reality, and why the companies not shouting the loudest about AI might be the ones actually using it best.
Show Highlights
(00:00) - Cold Open
(00:48) – Introductions and what Dynatrace actually does
(03:28) – Who Dynatrace serves
(04:55) – Why AI isn't prominently featured on Dynatrace's homepage
(05:41) – How Dynatrace built AI into its platform 10 years ago
(07:32) – Observability for GenAI workloads and their complexity
(08:00) – Why AI workloads are "non-deterministic" and what that means for monitoring
(12:00) – When AI goes wrong
(13:35) – “Human in the loop”: Why the smartest companies keep people in control
(16:00) – How AI Centers of Excellence are solving the compliance bottleneck
(18:00) – Are enterprises too paranoid about their data?
(21:00) – Why startups can innovate faster than enterprises
(26:00) – The "multi-function printer problem" plaguing observability platforms
(29:00) – Why you rarely hear customers complain about Dynatrace
(31:28) – Free trials and playground environments
About Wayne Segar
Wayne Segar is Director of Global Field CTOs at Dynatrace and part of the Global Center of Excellence where he focuses on cutting-edge cloud technologies and enabling the adoption of Dynatrace at large enterprise customers. Prior to joining Dynatrace, Wayne was a Dynatrace customer where he was responsible for performance and customer experience at a large financial institution.
Links
Dynatrace website: https://dynatrace.com
Dynatrace free trial: https://dynatrace.com/trial
Dynatrace AI observability: https://dynatrace.com/platform/artificial-intelligence/
Wayne Segar on LinkedIn: https://www.linkedin.com/in/wayne-segar/
Sponsor
Dynatrace: http://www.dynatrace.com
Next-level observability: live breakpoint debugging
SPONSORED BY DYNATRACE
Dynatrace is an AI-powered observability platform. It empowers today’s AI-enabled digital enterprises to understand their systems and data so they can analyze, automate, and innovate faster.
Learn more about Dynatrace’s Live Debugger.
Connect with Henrik Rexed on LinkedIn or check out Is it Observable on YouTube.
See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Keptn, with Alois Reitbauer
Keptn, a control plane for continuous delivery, came out of the need to install Dynatrace's software at their customer's environments. Alois Reitbauer is Chief Technical Strategist at Dynatrace, reponsible for open source, and a co-chair of the CNCF App Delivery SIG. He talks to your hosts about Keptn, observability after deployment, and how owning a 40 year old sports car is more "curation" than "operation".
Do you have something cool to share? Some questions? Let us know:
web: kubernetespodcast.com
mail: kubernetespodcast@google.com
twitter: @kubernetespod
Chatter of the week Loved: Thinking, Fast and Slow
Unloved: a pile of Sex and the City
News of the week Anthos Attached Clusters
New Anthos pricing
GKE on The Keyword
Cloudian introduces operator
Canonical introduces Kubernetes 1.19
Portainer CE 2.0
Kuberntes client comparison by Yolan Vloeberghs and Pieter Vincken
Distributed tracing overview by Jonathan Gold
Links from the interview Dynatrace
OpenTelemetry
OpenMetrics
Keptn What it is, how it works, and how to get started
Blogs by Alois:
Micro operations — A new operations model for the micro services age
How your delivery pipeline will become your next big legacy-code challenge
Related CI/CD tools: Spinnaker
Jenkins
Argo
Flux
GitLab
CD Foundation SIG Interoperability
CNCF SIG App Delivery
Alois's car marque of choice
Alois Reitbauer on Twitter
Episode 66: VMware? VMhere with Sean O’Dell
About Sean O'Dell
Sean is a troublemaker living on the bleeding edge of technology and innovation. As a member of the VMware Cloud Services - Solution and Technology team, Sean is responsible for Evangelism, Developer Relations and assists in many GTM functions. Sean joined VCS in February of 2017 and helped shape and launch the set of SaaS solutions at VMworld 2017. Prior roles include Global Technical Lead for Network Insight (vRNI), Sales Engineer Leader for Arkin, VMware Cloud Management SE and CUSTOMER.
Links Referenced:
Twitter: @theseanodell
VMware