An AI context-layer audit checks whether agents can safely understand the company they are helping run.
Start with agent tasks, not abstract policy. Pick the workflows where agents read, decide, write, recommend, or act: renewal summaries, support triage, account research, billing exceptions, forecast prep, implementation updates, internal reporting.
For each task, ask what business context the agent needs:
- Which objects and relationships are involved?
- Which sources are authoritative?
- How fresh must each fact be?
- What permissions apply to the requester, agent, and audience?
- Which memories can be reused, and which must be revalidated?
- What quality signals should change behavior?
- When should the agent escalate instead of act?
- What context use must be logged?
Then inspect the current setup. Look for prompt-stuffed docs, stale summaries, missing source priority, broad retrieval access, internal labels leaking into external drafts, memories with no expiry, and tool calls that do not require enough business context.
Rank findings by action risk. A weak context rule for an internal brainstorm is annoying. A weak context rule for customer communication, billing action, access change, or executive reporting is serious.
The audit should produce a short fix list: context packets to define, sources to prioritize, permission boundaries to enforce, stale memories to expire, escalation rules to add, and audit fields to log.
The goal is not to make agents omniscient. It is to make them honest about what they know, where it came from, and whether it is safe to use.
This is part 10 of 10 in The AI Context Layer.
