What is Agent Observability (AgentOps)?

Agent Observability makes visible what an AI agent actually does in operation: which model calls and tool invocations it triggers, how long they take, what they cost, where errors occur, and how good the results are. It rests on traces – complete records of every step in an agent run. AgentOps refers to the operating practice around it: monitoring, testing, versioning, and continuously improving agents.

Why does this matter?

Agents do not behave deterministically: the same task may take three steps today and eleven tomorrow. Without observability, cost spikes, endless loops, and creeping quality loss only surface when users complain. It is the technical prerequisite for operating agents responsibly at all.

What does this look like in practice?

After a model switch, a service agent’s costs rise by 40%. The traces show why: the new model queries the knowledge base twice before every answer because it discards the first result list. An adjusted tool description fixes it — found within an hour rather than via the monthly bill.

Not to be confused with

Not the same as agent mining: observability shows individual runs in technical depth, whereas agent mining condenses thousands of runs into process patterns, variants and frequencies. Nor the same as classic application monitoring, which measures availability and response times; agent observability also covers decisions and output quality.