Memory Hygiene Audit
Home

AI coding agent context audit

AI Coding Agent Context Audit for Safer Continuity

An AI coding agent context audit asks whether the next agent run will receive the right facts, in the right amount, with the right boundaries. It is useful when teams want continuity without letting memory files become an uncontrolled second codebase.

View pricing plans

When this matters

  • A team uses several coding agents and wants a common quality bar for shared context.
  • Repo notes include build commands, deployment warnings, payment rules, and privacy constraints.
  • Leads need to know whether context quality is improving or drifting.

How to run the audit

  1. Collect memory snippets, instruction files, handoff notes, and recent decision records.
  2. Score freshness, source evidence, duplication, contradiction, actionability, and privacy risk.
  3. Generate a compact agent brief and a list of rejected memory candidates.
  4. Create a retention policy for temporary facts and incident notes.
  5. Track the context score over time after each cleanup.

Common risks

  • Bad context can make a capable agent confidently repeat the wrong action.
  • Undocumented memory sources make it hard to explain why an agent recalled something.
  • Large briefs can crowd out task-specific code evidence.

How Memory Hygiene Audit connects this to checkout

Memory Hygiene Audit provides the console, scoring, and evidence exports needed to operationalize context hygiene for coding-agent teams.

Teams can preview the score, then use the Team Hygiene annual checkout to generate the full cleanup diff, reviewer notes, and agent-readable JSON policy.