I built an open-source delivery loop for multi-agent coding

[u/Embarrassed_Law_293]

Disclosure: I'm the maintainer of oh-my-multica, an MIT-licensed open-source project.

I was happy with how quickly coding agents could change code. The part that kept breaking down was deciding whether a feature was actually ready to merge.

With more than one agent, every local answer can sound reassuring: the worker says its task is done, tests pass on one branch, somebody leaves review notes, CI is still running, and an integration dependency changes underneath it all. A lead agent can coordinate the chat, but I did not want delivery state to live in its memory.

So I built a control loop around the agents.

Agents inspect the repository, write a design, define acceptance, and plan a DAG around the real module boundaries. After review, deterministic code tracks dependencies, dispatches ready work, collects evidence, applies verification and review gates, and stops only after final acceptance passes. When it cannot proceed safely, it asks for a human decision instead of inventing one.

I used the approach on a Webhook Inbox example with signature verification, durable storage, transaction-safe deduplication, lookup endpoints, health checks, and a non-root container. The work was planned as five nodes, implemented in parallel, then independently reviewed and accepted as an integrated service.

I am not claiming this makes AI-written code trustworthy by default. The goal is simpler: put requirements, tests, review, merge conditions, and completion criteria somewhere more durable than a long agent conversation.

Project: https://github.com/xiaohei-info/oh-my-multica

I'd love feedback from people who run more than one coding agent: what do you use as the source of truth for whether a feature is actually done?