What Is AgentOps and How Does It Work?

This is the process of deploying AI agents to run reliably in production, observing their actions, debugging them, measuring their performance, and controlling their cost and behavior. It is operations for autonomous agents.

LLMOps includes the operations involved in handling large language models, such as prompts, tokens, context, and evaluating the output generated. AgentOps adds the difficult aspects of agents, like tracing multi-step runs, tool use, and governing autonomous actions. AgentOps builds on top of LLMOps.

Because an agent's run is a branching tree of reasoning and actions, rather than a single call, it may behave differently each attempt, make many tool calls, and fail in unexpected ways, which makes tracing, assessment, and cost control difficult.

Because an agent's behavior is composed of several steps, only by replaying exactly what it did and the reasoning behind it can you find and fix its errors. Without traces, an agent is a closed system that is difficult to understand and repair.

Once agents make the transition from prototypes to actual production, especially if they take real actions, run at scale, or must be trusted. That is when tracing, monitoring, evaluation, and guardrails become necessary for reliable execution.