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Use the AI Observability API for aggregate dashboard views over LLM requests, costs, latency, sessions, users, and hosted fix activity. Use Agent Observations API for row-level generation, tool-call, retrieval, and agent-step evidence.

Operations

Keep dashboard queries bounded by time when supported. Use observation filters when you need the exact prompt, output, tool call, token count, or cost row. ai.stats reports prompt, completion, total, cached-input, and reasoning token totals alongside tool-call counts, p95 latency, and error counts, and ranks top models, providers, tools, sessions, and users. ai.facets returns the values and counts a filter UI needs for the same time range.

CLI and MCP

Use squasher ai ..., squasher agent-observations ..., or squasher fixes ... depending on the workflow. In MCP Code Mode, call search("ai") or search("agentObservations") first.

Agent handoff

Related: AI observability, Agent SDK.