The essential discipline for deploying, managing, and monitoring AI agents in production. If you're moving beyond simple chatbots to autonomous fleets, you need AgentOps.
Running a single agent in a notebook is easy. Managing a fleet of non-deterministic agents in a live enterprise environment is a completely different class of problem.
Unlike traditional software, AI agents don't always output the same result for the same input. Tracking these variations, hallucination rates, and drift is critical for reliability.
Standard APM tools monitor latency and uptime. AgentOps monitors intent. Did the agent actually solve the user's problem? Did it get stuck in a loop?
Agents act autonomously. Without strict governance layers, an agent might access sensitive PII or execute unauthorized database deletions.
A runaway agent loop isn't just a bug—it's a massive bill. Managing token usage and tool calls across thousands of concurrent agent sessions is a financial necessity.
AgentOps is a set of practices and tools inspired by DevOps and MLOps, specifically adapted for the unique lifecycle of autonomous agents. It focuses on four core pillars.
Managing agent prompts, model versions, and tool configurations as code.
Deep tracing of agent thought chains (CoT) and external tool interactions.
Role-based access control and human-in-the-loop approval workflows.
Automated testing of agent logic against golden datasets before deployment.
Defining the category is one thing; building the tools is another. We are building AgentControlLayer, the first true AgentOps platform for enterprise teams.
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