how-to-solve-it-state-machine-skill

You can prevent premature actions by enforcing a state-driven execution model that gates manipulation until diagnostic artifacts like problem-frame.md and evidence-log.md are created, ensuring bounded and verifiable AI agent actions.

Hypothesis-driven exploration is a structured diagnostic process where AI agents rank potential explanations for uncertain problems, gather supporting evidence, and apply gated planning before executing bounded, verifiable actions.

Structure an AI agent workflow by applying a state-machine model with sequential phases including Intake, Recon, Hypothesis Ranking, Plan, Execution Unlock, and Look Back, requiring diagnostic artifacts at each step for traceability.

You need an evidence log when handling complex, uncertain tasks requiring traceability and justification before execution, ensuring the AI agent's diagnostic work and hypothesis ranking are verifiable.

Yes, the state-machine approach is effective for complex, uncertain tasks requiring careful diagnostic work, traceability, and gated execution, converting ambiguous inputs into precise problems before acting.

A limitation is the requirement to create and maintain problem-frame.md and evidence-log.md artifacts, adding overhead to the workflow before any bounded execution or manipulation can occur.