Agent Memory Audit with Claude Code: Result JSON Schema

A production playbook for agent memory audit in cross-industry operations using Claude Code: result json schema, run-scoped inputs, logs, typed results, and artifacts.

Audience: AI platform and privacy teams

The problem

AI platform and privacy teams need agent memory audit to run repeatedly against stored memories, policies, logs, and deletion requests. In cross-industry operations, the pain is not one good answer; it is repeatability, auditability, exception handling, and evidence that survives handoff.

Implementation path

Define the outer result contract once, let the agent memory audit skill own body.data, and reject terminal output that does not match the expected schema.

Tradeoffs and failure modes

Schema enforcement adds upfront design work, but removes prompt parsing from the product surface. For agent memory audit, the practical test is whether a second run can be debugged, retried, and consumed by a product without reading the raw agent transcript.

Result shape

{
  "schema_version": "argo.result.v1",
  "summary": "agent memory audit completed",
  "body": { "type": "agent_memory_audit", "data": {}, "exceptions": [] },
  "artifacts": []
}

Run this on Argo