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How to assign work cards to AI agents and check results on one board with Retinue

Give each agent a card with one holder, an acceptance line, and a receipt trail you can review.

Credentials. The one-time agent token and the admin password grant write access to the board, so store them outside the data volume and keep them out of cards.

Who it is forA developer or team lead supervising several AI coding agents who needs one place to see who holds each task and whether it is really finished.

The problemAgent work gets scattered across chat threads and terminals, so nobody can tell which agent is doing what or whether a claimed result matches the request.

The outcomeA running local hub where a card is claimed by one named agent, updated with progress and a result reference, and marked done only after its acceptance line is true.

What you need first

  • Docker with Compose installed
  • Host port 9219 free on the machine that will run the hub
  • An admin password of at least 8 characters
  • Python for the optional MCP install (pip install 'retinue[mcp]')
  • An agent that can send HTTP requests or speak MCP

Steps

  1. Export RETINUE_ADMIN_PASSWORD with at least 8 characters, then run docker compose up --build from the repo folder.
  2. Run curl -fsS http://127.0.0.1:9219/api/health and confirm the JSON status is ok.
  3. Sign in as operator, open Administration, and prepare an executor with actor id worker-1. Save its one-time token outside the data volume.
  4. Open Task workspace, then Task board, then New task. Set the holder to worker-1 and write an acceptance line the agent can check.
  5. Have the agent claim the card and post progress with its agent token as a bearer header to the card update endpoint, then set the status to done once the acceptance line is true.

Prompt for your agent

Using the Retinue repo at <repo path>, start the hub with docker compose up --build and confirm /api/health returns status ok. Create a card for actor worker-1 with the acceptance line in <acceptance text>, then claim it and post progress through the update endpoint. Report the task id and the final status, and verify that a token for a different actor gets 403 holder-only-writes.

Caveat. The README's quick path stays on loopback, so reaching the hub from another machine needs the exposure steps in SELF_HOSTING.md and is not covered here.

Use this in your AI agent

Ask your agent

Using the Deduped MCP, get the setup steps for jklthinking/retinue and "Assign work cards to AI agents and check results on one board"

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claude mcp add --transport http deduped https://dedupedai.com/mcp

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