Agent Forensics

Ability to reconstruct why an agent performed an action by linking audit logs, distributed traces, tool/agent calls, and prompt-response records.

Key points

  • Google Cloud uses the example "this BigQuery access happened; why did it happen?" to define the need for agent forensics [src-043].
  • Forensics requires correlation across audit logs, traces, spans, agent calls, tool calls, and potentially the exact prompt-response pair that triggered the access [src-043].
  • Prompt and response logs may require finer-grained access control because they can contain sensitive data or PII [src-043].
  • Agent forensics moves observability from "which component made the call" to "what reasoning path and intent produced the call" [src-043].

Related concepts

Source references

  • [src-043] Google Cloud Events — "Operationalize AI: A blueprint for managing enterprise agents at scale" (2026-04-24)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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