For SREs, platform engineers, and AI/ML teams, agentic AI makes many promises that are hard to ignore. Autonomous agents that can interact with cloud-native systems, trigger actions, and make decisions at machine speed represent a genuine leap forward in how organizations operate. The investment has been substantial. While investments in these platforms are substantial, the results can be disappointing for many teams.The problem is that generative models alone still struggle to deliver the reliable, repeatable outcomes that production systems demand. Agents can hallucinate, misinterpret context, or cascade small errors across multi-step workflows. And as agent-to-agent interactions multiply, dependency graphs shift and failure modes become harder to reason about. Without end-to-end observability and a unifying control layer, scaling agentic AI not only stalls but also introduces unpredictable system behavior at scale.Why production-ready agentic AI requires a control planeMost teams moving from AI experimentation into production quickly discover that isolated models and smart prompts are not enough. Autonomous decisions need to be grounded in deterministic, context-rich, real-time system data. Agents need to act on facts, not guesses. And as organizations progress along the maturity path from automated workflows to fully autonomous systems, human intent, feedback loops, and clear boundaries remain essential.A control plane can help you address this. By coordinating agents, distilling observability data into actionable context, and orchestrating actions across the stack, a control plane provides teams with the foundation they need to move from AI pilots to real-world impact without sacrificing reliability or operational stability.If your team is ready to make that move, join us at 11 a.m. Pacific on March 24 for a special online event: Building Production-Ready Agentic AI: Why a Control Plane Matters. During this free webinar, Dynatrace‘s Greg Findlen, Head of Technology Evangelism, and Wayne Segar, RVP Field CTOs and AI Innovation, will sit down with TNS Host Chris Pirillo to break down what it actually takes to run agentic AI at scale, including a real-world example of how a modern control plane can support safe, scalable autonomous operations.Register for this free webinar today!Can’t join us live? Register anyway, and we’ll send you a recording following the webinar.What you’ll learnBy attending this special online event, you’ll leave with practical insights and a clear framework for scaling agentic AI in production, including:How agentic AI changes system complexity: Understand why agent-to-agent interactions shift dependency graphs and failure modes, and why end-to-end observability is required to reason about agent behavior.Why generative models alone are not enough: Learn how reliable autonomous decisions depend on deterministic, real-time system context rather than generative models operating in isolation.The maturity path to autonomy: See how organizations progress from automated workflows to supervised and eventually fully autonomous systems, and where human intent and feedback loops remain critical.What a control plane makes possible: Discover how a shared control layer coordinates agents, distills observability data, and enables organizations to operate agentic AI at scale without introducing chaos.The post Why agentic AI stalls in production — and how a control plane fixes it appeared first on The New Stack.