LLM observability for AI agents.
AI agents in production need visibility into messages, tool calls, latency, cost, errors, escalation, and answer quality so teams can improve safely.
Topic summary
How to monitor AI agents in production with traces, logs, cost, latency, tool calls, evaluation, safety signals, and human handoff. This guide helps you understand when the topic makes sense, what risks need control, and which commercial page goes deeper into the solution.
Operational traces
Track user input, model response, tools called, retrieved context, errors, and final outcomes.
Quality signals
Review resolution rate, handoff rate, unsafe answer attempts, user feedback, and policy adherence.
Cost and latency
Monitor model usage, token volume, response time, retries, and expensive workflows.
Incident response
Create runbooks for failed integrations, abnormal cost, unsafe behavior, and degraded model quality.
To turn this topic into a project, see our page on LLM observability or contact ArkGenesys to map a safe pilot.