When I talk to enterprise teams deploying systems, there’s a pattern I keep seeing. The compute is there. The models are there. The workflows are there. What’s consistently missing is a layer of trust. And without that layer, you’re essentially flying blind.
That’s what I want to walk you through here. The case for a control plane for autonomous AI, why observability alone isn’t enough, and what it actually takes to govern these systems at scale.
How we got here: from predictive models to autonomous agents
It’s worth stepping back for a moment to appreciate how quickly the landscape has shifted.
Early AI adoption was largely about predictive models. A model would make a prediction, a human would review it, and then a decision would be made. The human was always in the loop. Then came single LLM call applications, things like chatbots and search interfaces.
Then multi-step workflows. And now we’re firmly in the era of complex autonomous agents that make decisions and take actions with minimal human oversight.
Each step in that progression has added a layer of complexity. And with complexity comes a new category of risk.
The teams deploying these systems are often moving fast, which is understandable. But many of them aren’t fully accounting for the risks that come with this new model of AI. Agentic AI adoption is ramping up quickly, and the infrastructure needed to govern it hasn’t kept pace.
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Why traditional monitoring falls short
Here’s something that comes up again and again in conversations with enterprise teams: they assume their existing monitoring tools will cover them. APM tools, IT monitoring, security tooling. And those tools are still important. Absolutely they are. But they weren’t built for this.
Agentic applications are fundamentally different. They have dynamic routing. Models make decisions. The system can adapt in ways you didn’t anticipate when you built it.
