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AI Context Audit Checklist

Audit the context before you scale the agents.

This checklist is for engineering teams adopting Claude, Cursor, Codex, Copilot, or internal agents. The goal is simple: make sure the agent can tell what is true, current, relevant, allowed, and source-backed before it acts.

Map Where Truth Lives

List the systems agents need to read before they touch code.

  • GitHub repos, issues, PRs, reviews, CI, and release history
  • Linear or Jira tickets with acceptance criteria and ownership
  • Slack decisions, customer reports, and incident channels
  • Sentry, logs, feature flags, runbooks, and deployment rules
  • Docs, ADRs, onboarding notes, architecture diagrams, and stale wikis

Attach Provenance

Every durable context item should explain why it is believed.

  • Source URL or stable identifier
  • Source type such as PR, ticket, chat, incident, doc, or deploy
  • Created and last-verified dates
  • Owning team, repo, customer, or environment
  • Confidence and contradiction status

Separate Read From Write

Useful agents need broad context before they need broad authority.

  • Read-only connector scopes by default
  • Explicit write policies for tickets, PRs, comments, memory, and deploys
  • Private, team, org, customer, and restricted visibility labels
  • Human approval for durable memory updates
  • Audit trail for why an agent believed and changed something

Define Memory Policy

Memory is a governed data model, not a random pile of summaries.

  • Allowed memory types: facts, decisions, warnings, incidents, preferences
  • Superseded and deprecated states
  • Freshness windows for volatile facts
  • Review queue for inferred memories
  • Deletion and scope-change process

Hunt Stale Context

Bad context is worse than missing context because it sounds useful.

  • Docs that disagree with current code
  • Tickets contradicted by later Slack decisions
  • Runbooks that predate infrastructure changes
  • Feature-flag or environment assumptions that changed
  • Incident learnings that never reached docs or agent rules

Measure Agent Reliability

The context layer should improve outcomes in tasks you can replay.

  • Pick real PR, debugging, onboarding, and incident tasks
  • Run the same tasks with and without the context system
  • Score wrong assumptions, wasted reading, risky actions, and PR quality
  • Record which source changed the result
  • Repeat after docs, connectors, or memory policy changes

Quick readiness scale

Most teams start at level 0 or 1. The first valuable step is not autonomy. It is source-backed read access and a reviewable memory policy.

Level 0: agents only see files and whatever the human pasted
Level 1: project rules and repo docs are available but weakly maintained
Level 2: tickets, PRs, docs, and incidents are source-backed and scoped
Level 3: memory updates are reviewed, permissioned, and contradiction-aware
Level 4: evals prove the context layer reduces agent failure modes
Start with context

Audit Your AI Context Layer

Tell us which tools your team uses today. We'll help map the context surface, permissions, stale assumptions, and first reliable agent workflows.

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