harness

To automate long-running agent workflows reliably, use a framework that provides automatic progress persistence and checkpointing. This ensures that autonomous tasks save their state to files like harness-tasks.json, allowing execution to resume seamlessly if the session is interrupted.

Managing task dependencies in autonomous agent execution requires a framework that enforces execution order based on defined rules. This ensures complex, multi-stage processes run sequentially or concurrently as needed, preventing out-of-order execution errors in production environments.

Recovering failed autonomous tasks involves using built-in failure recovery mechanisms like git rollback and retry strategies. By executing git reset --hard, the system reverts codebase changes, allowing the agent to safely retry the failed task without corrupting the project state.

Yes, you can run multiple agent tasks concurrently in a distributed environment using concurrency control. The framework supports both exclusive and concurrent execution modes, allowing distributed agents to work in parallel or lock resources to prevent conflicts during complex operations.

To initialize a project for multi-session agent task management, run the /harness init command in your current directory. This sets up the necessary configuration files, such as harness-progress.txt and harness-tasks.json, to track state, dependencies, and progress automatically.

Checkpointing is essential for multi-day autonomous development processes because it automatically saves progress at regular intervals. If an agent session crashes or is interrupted, checkpointing prevents data loss by allowing the workflow to resume from the last saved state rather than starting over.