AI & RAG

Agentic Deep-Dive API Researcher for Hard Spec Questions

An agent fielded via webhook that answers multi-part API questions by iteratively searching OpenAPI specs, changelogs, and Confluence runbooks.

CategoryAI & RAG
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWebhook receives an escalated API questionHTTP webhook
  • ActionPlan sub-queries and search spec index across versionsPostgreSQLPostgres
  • ActionPull related changelogs and Confluence runbooksConfluenceConfluence
  • LogicReconcile version conflicts and decide what applies
  • OutputReturn cited answer with follow-ups to SlackSlack

What it does

For complex questions a single retrieval can't answer, this agent runs multiple grounded searches across the OpenAPI spec index, the changelog history, and Confluence runbooks. It reconciles differences between versions, reasons about which version applies, and returns a structured answer with citations and suggested follow-ups.

When to use it

Use it for the hard questions: "how did pagination behavior change between v2 and v4, and which clients are still on v2?" When a flat lookup falls short and you need iterative, cross-source investigation, route the question here.

How it works

  1. 1A webhook delivers a question, optionally escalated from the simpler Slack bot.
  2. 2The agent plans sub-queries and searches the Postgres spec index for relevant operations across versions.
  3. 3It pulls related changelog entries and Confluence runbooks to fill gaps.
  4. 4A reconciliation step resolves version conflicts and decides what actually applies.
  5. 5The agent returns a cited answer with confidence and follow-up questions to the requesting channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HTTP webhookTrigger any URL on agent actions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect ConfluenceSpaces, pages, blueprints.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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.

Run this workflow in your colony.

14-day trial. No DevOps. No Sales call. Provisioned in under a minute.