Why AI agents need a database of their own actions
Firsthand work on six over six months repeatedly showed a need for a dedicated agent database. This is not the normal database that stores customer or product data.
It records what the agent does. The first version was a simple in SQLite, covering events such as sending tasks, running , and saving results.
Those records later helped the agent inspect and learn from earlier behavior, suggest feature needs after a run, resume paused work, and maintain state. The broader practices were to keep prompts small, give the agent room to work, use fixed checks and policies at important steps, evaluate the full process, manage state, and isolate s.
Key points
- Separate the agent's action history from the 's customer and product data.
- Log task dispatches, evaluation runs, and results in time order.
- Use saved records to inspect behavior and resume paused executions.
- Keep prompts small, while placing fixed checks and policies around important steps.
- Manage state and run tasks in isolated environments.