long-running-harness-kit

To build a long-running agent harness that survives context resets, you design workflows with resumable checkpoints and persistent run state. This ensures critical information is not lost during interruptions across multiple sessions.

The best way to manage state for an agent task spanning multiple sessions is to use resumable checkpoints. This approach saves incremental progress and ensures critical information survives context resets without losing work.

You structure handoffs in agent development by defining distinct initializer and coder roles. The initializer sets up context while the coder makes incremental progress, facilitating explicit handoffs between setup and execution phases.

Yes, you can resume an interrupted agent task from the last saved checkpoint. The harness emphasizes persistent run state and incremental progress, allowing you to stop and restart complex tasks without losing work.

You need checkpointing for long-running agent workflows when tasks might take days to complete or require recovery after interruptions. Checkpointing ensures progress is not lost due to interruptions across multiple sessions.

Artifacts that survive context resets in a resumable agent harness include persistent run state and incremental progress records. The harness requires clear artifacts that persist through context resets and explicit handoffs between phases.