Galileo Experimental Prover Connector for AI agents.

It forces your AI to move beyond 'best practices' and actually design controlled experiments. It ensures that every architectural change is backed by evidence, isolated variables, and clear predictions.

It stops your agent from blindly accepting vendor whitepapers. Instead, it forces the AI to demand a specific benchmark and design a test to verify those claims against your actual data.

Yes, by forcing the agent to isolate a single variable at a time. This prevents you from making multiple changes and guessing which one actually fixed the problem.

It is best suited for high-stakes technical decisions like infrastructure migrations, performance tuning, or vendor evaluations where the cost of being wrong is high.

It builds a logical guardrail into your agent's reasoning. It requires the AI to question the source of a claim and provide a repeatable experiment before it can confirm a solution.

The Connector will flag the reasoning as evidence-deficient. It will tell you that the current logic is based on authority deference and will prompt you to define a measurable test.

Curie forces measurement rigor in existing data. Galileo forces experimental DESIGN. question authority, create a NEW test, predict outcomes, revise beliefs. Curie asks 'did you measure correctly?' Galileo asks 'did you design the right experiment?'

Accepting a claim because of WHO said it. 'The documentation recommends,' 'the industry standard says,' 'leading companies use'. all authority deference. The source's prestige does not make the claim true. Galileo TESTED Aristotle instead of citing him.

Yes. 'Customers prefer feature X'. design a test, control variables, predict the outcome, measure. The Galilean method applies wherever claims need testing against reality.