autoresearch

Autoresearch automates iterative problem solving by running a constraint-driven experimentation loop using a mechanical Verify command. You define a Scope, a Direction, and an Iterations budget to operate the modify, verify, keep or discard cycle.

Autonomous goal-directed iteration is a looping process that modifies, verifies, and keeps or discards changes automatically. It uses git memory to inform future experiments and preserve learning across bounded iteration cycles.

Yes, deterministic verification is required to guide automated experimentation. You must configure a mechanical Verify command that extracts a specific metric to evaluate each iteration's success or failure.

Set bounded iteration budgets using the Iterations N inline configuration. This parameter defines the maximum number of autonomous modify, verify, and keep or discard cycles the loop will execute before stopping.

Automated workflow iteration works for software, data, infrastructure, security, and content tasks. The loop adapts to different domains by adjusting your defined goals, scopes, and guards.

Autonomous iteration requires a defined Scope, a Direction, an Iterations budget, and a mechanical Verify command to operate. It cannot function without deterministic verification metrics to evaluate each experiment.