Agent Lifecycle
The states an AI agent moves through, from creation to deletion, and the actions that move it between them.
Agent Lifecycle
An agent lifecycle is the sequence of states an AI agent instance passes through from the moment it is created until it is permanently deleted, along with the operations that move it between those states. Just as a virtual machine or container has a defined lifecycle (created, running, stopped, terminated), a long-lived AI agent needs an equivalent model so operators can reason about its resource usage, recoverability, and current status at any point in time.
Common lifecycle states
Most agent platforms converge on a similar small set of states, even if the names differ:
- Running: the agent is active and able to receive or process tasks.
- Paused: the agent is temporarily suspended but retains its state and can be resumed.
- Archived: the agent is set aside for longer term storage, typically after it is no longer in active use.
- Error: the agent has hit a failure condition that needs investigation or recovery before it can resume normal operation.
Transitions between these states are triggered either by an explicit operator or API action, such as pause, resume, archive, restore, or delete, or by automated policy, such as pausing an agent that has been idle for a period of time.
Why lifecycle management matters
Agents that run inside containers consume CPU, memory, and sometimes GPU capacity even when they are not actively working on a task. Without a defined lifecycle, an operator running dozens or hundreds of agents has no systematic way to reclaim idle capacity, audit what is currently active, or safely bring a failed agent back online. A clear lifecycle model turns agent management into a predictable state machine rather than a collection of ad hoc scripts.
Agent lifecycle in Agenhood
Agenhood models each agent with an explicit lifecycle: running, paused, archived, and error, with actions to pause (optionally cancelling any in-flight work), resume, archive, restore, recover, and delete. These transitions are exposed through the web console and mirrored in the REST API, so the same lifecycle operations that a human triggers by clicking a button can also be scripted or automated by external systems.