autonomous-cycle

Automated project decision making works by loading state from a `seed.json` file, analyzing multiple data sources like git logs and audits, and executing a single prioritized action. The system verifies the outcome and updates the seed file accordingly.

An automated decision cycle is a recurring process where an AI agent analyzes project tensions, potential, and momentum to select and execute the most impactful action. It continuously adapts project direction by verifying outcomes and updating the state seed.

Autonomous research execution is set up by initializing a `seed.json` file with your project state. The decision engine then identifies the next promising experiment based on recent findings and theoretical contradictions, executes it, and updates the project direction.

Yes, you can control automation levels through predefined autonomy settings. The decision engine respects these configured levels when selecting, executing, and verifying maintenance actions, ensuring actions run within your desired boundaries.

An AI agent uses multiple integrated data sources including git logs, project files, and audits alongside the `seed.json` state file. It combines these inputs to rank actions based on project tensions, potential, and momentum.

You should avoid an autonomous decision engine for one-off tasks or projects lacking a trackable state file. This system is designed for recurring decision processes that require continuous adaptation and multi-source data analysis over time.