autoresearch

Automated research workflows use persistent loops to continuously collect data and validate progress until explicit evidence is obtained. You initialize the process with a mission and callback configurations, reducing manual oversight.

A persistent research loop is a stateful process that continuously refines data collection until meeting predefined quality standards. You need stateful validation for iterative scenarios requiring supervision to ensure task completion without manual intervention.

Automated data collection setup requires providing an initial mission and callback configurations. You then choose a validation mode at initialization, selecting either script-based validation or prompt-based review to auto-validate the research outputs.

Yes, automated validation supports iterative data collection without manual intervention. The looping process continually refines outputs and validates progress against predefined standards, ensuring robust state management throughout the task lifecycle.

The best way to maintain state management during research automation is using persistent loops with built-in validation strategies. This approach ensures robust state handling and automatically nudges the process until obtaining explicit evidence.