Agents that work for hours (or days): how to manage long tasks with AI

Agents that can run long tasks are becoming common. The secret to making it work is management: checkpoints, output contracts and decision logging.

What changes with long-running agents

You need periodic visibility and clear criteria for what "done" means at each step. Without that, the agent becomes a black box and rework explodes.

Checkpoints and contracts

Define a checkpoint every 30–60 min: what should be done? Create an output "contract": tests, logs, docs, migrations. Ask the agent to log decisions and questions in a DECISIONS.md file.

Most common mistakes

Letting the agent change architecture without permission. Accepting a huge PR without review. Not locking secrets and environment variables.

Key takeaways

Checkpoints and contracts turn the agent into an accelerator. Without them, it becomes a gamble. Log decisions and protect secrets.

Read also

• Code assistant is also an attack surface: how not to leak secrets and keys
• Legal and license risk: the side almost no one considers when using AI in code

FAQ

How often to checkpoint? Every 30–60 min or every logical step (e.g. module, flow).

What if the agent gets stuck? Have a timeout and "rollback" criterion (discard and restart from the last valid checkpoint).