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Autonomous agent lifecycle

The autonomous agent lifecycle refers to the cyclical process an AI agent undergoes to manage and execute tasks autonomously within a software environment. It typically transforms static or human-generated inputs into concrete outcomes through a series of distinct steps, often supervised by a management platform^[001-TODO__Multica_-_开源AI代理人管理平台.md].

In advanced orchestration platforms like Multica, this lifecycle is designed to mimic the behavior of a human team member, moving beyond passive assistance to active task management and execution^[001-TODO__Multica_-_开源AI代理人管理平台.md].

Lifecycle Stages

The standard autonomous lifecycle involves the following stages:

  1. Enqueued (入队): A task or issue is created within the management system and enters the processing queue^[001-TODO__Multica_-_开源AI代理人管理平台.md].
  2. Claimed (认领): The autonomous agent identifies the task and assigns it to itself, simulating a team member picking up a ticket^[001-TODO__Multica_-_开源AI代理人管理平台.md].
  3. Started (开始): The agent begins active execution. This phase often involves communication via WebSocket to push real-time progress updates to the user^[001-TODO__Multica_-_开源AI代理人管理平台.md].
  4. Completed / Failed (完成/失败): The agent finishes the execution, resulting in a successful delivery or reporting an error that blocks progress^[001-TODO__Multica_-_开源AI代理人管理平台.md].

Key Characteristics

  • Real-time Reporting: Throughout the execution phases, the agent maintains a connection to the server to report status, effectively replacing the need for a human to "babysit" the process^[001-TODO__Multica_-_开源AI代理人管理平台.md].
  • Issue-Driven Interaction: Agents interact with the system through standard artifacts like issues and comments, integrating directly into existing development workflows^[001-TODO__Multica_-_开源AI代理人管理平台.md].
  • [[Reusable Skills]]
  • [[AI Orchestration]]
  • [[OpenHands]]

Sources

  • 001-TODO__Multica_-_开源AI代理人管理平台.md