# Multica Agent Task Lifecycle: From Queue to Completion

> Understand the Multica agent task lifecycle from enqueue to completion. Learn how tasks are claimed, run, and reported back in this in-depth look at the multica-ai/multica repository.

- Repository: [multica-ai/multica](https://github.com/multica-ai/multica)
- Tags: internals
- Published: 2026-04-11

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**Multica agents follow a five-phase lifecycle orchestrated by the backend `TaskService` and local daemon: tasks are enqueued when issues are assigned or chats initiated, claimed by runtimes via HTTP API, marked as running during execution, completed or failed with results reported back, and finally reconciled with real-time UI broadcasts.**

The Multica platform coordinates AI agent execution through a structured task lifecycle that bridges server-side orchestration and local daemon processes. This workflow ensures agents efficiently transition from queued to running states while maintaining synchronization with the web interface. Understanding this lifecycle is essential for debugging agent behavior,