AI & RAG

ADR Staleness Audit: flag decisions overtaken by reality

On a schedule, scans accepted ADRs, uses an LLM to assess which are likely stale or contradicted by newer decisions in the index.

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMonthly staleness audit schedule fires
  • ActionLoad accepted ADRs and metadata from PostgresPostgreSQLPostgres
  • ActionFetch ADR bodies from ConfluenceConfluenceConfluence
  • ActionScore each ADR for staleness with OpenAIOpenAI
  • LogicKeep only ADRs above the staleness threshold
  • OutputOpen Linear review tickets for flagged ADR ownersLinearLinear

What it does

Finds Architecture Decision Records that have quietly gone out of date. It reviews accepted ADRs against newer accepted decisions in the index, asks an LLM to flag likely-stale or superseded-in-practice records, and creates a Linear ticket so an owner can confirm, update, or formally supersede each one.

When to use it

Use it when your ADR space accumulates decisions that nobody revisits, when "accepted" records silently contradict current practice, or when you need a recurring hygiene pass that turns into actionable owner tasks rather than a report nobody reads.

How it works

  1. 1A monthly schedule starts the audit.
  2. 2The flow loads all accepted ADRs and their metadata from the Postgres index.
  3. 3It fetches the bodies of accepted ADRs from Confluence for full context.
  4. 4OpenAI scores each ADR for staleness, citing the newer decision or signal that contradicts it.
  5. 5A logic branch keeps only ADRs above a staleness threshold.
  6. 6For each flagged ADR, a Linear review ticket is opened and assigned to the recorded owner.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect LinearIssues, projects, cycles, triage.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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