AI & RAG

ADR Conflict Check: flag PRs that contradict accepted decisions

When a pull request opens, retrieves relevant accepted ADRs and uses an LLM to judge whether the change conflicts with a standing decision.

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerwebhook
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPull request opened or updatedGitHubGitHub
  • ActionRetrieve relevant ADR ids from Postgres indexPostgreSQLPostgres
  • ActionLoad accepted ADR bodies from ConfluenceConfluenceConfluence
  • ActionJudge diff against decisions with OpenAIOpenAI
  • LogicBranch only when a conflict is detected
  • OutputPost grounded conflict warning comment on the PRGitHubGitHub

What it does

Guards your accepted architecture decisions at code-review time. On every new pull request it pulls the diff and description, finds the ADRs most relevant to the touched areas, and asks an LLM whether the change appears to violate any accepted decision (for example introducing a banned datastore or bypassing a required boundary).

When to use it

Use it when teams drift from agreed standards between reviews, when reviewers cannot recall every ADR, or when you want a soft, citing guardrail rather than a hard CI block. Pairs with the ADR index sync template.

How it works

  1. 1A GitHub webhook fires when a pull request is opened or updated.
  2. 2The flow retrieves candidate ADR ids from the Postgres index using the PR title and changed paths.
  3. 3It loads the matching accepted ADR bodies from Confluence.
  4. 4OpenAI compares the diff against the decisions and returns a conflict verdict with the specific ADR cited.
  5. 5A logic branch posts a warning PR comment only when a real conflict is found; otherwise it stays silent.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect ConfluenceSpaces, pages, blueprints.
  4. 4
    Connect OpenAIModels, embeddings, files.
  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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