DaoAgent

To identify the core problem in decision-making, you analyze the question to extract a structured definition including problem_type, essence, immutable_constraints, and hidden_assumptions. This distills ambiguous scenarios into clearly defined parameters.

The best way to break down resource allocation and risk assessment dependencies is using a modular causal-gear model. This decomposes problems into 3-5 interrelated gears with drives, driven_by, and leverage to reveal dependencies.

You extract immutable constraints and hidden assumptions by providing a decision question and optional constraints. The analysis returns a defined schema with causal_gears and death_traps, clarifying safe boundaries for strategic direction.

Yes, you can use causal gears for trade-off and timing decisions without external dependencies. The framework requires explicit fields for question, constraints, stakeholders, and clarification_result to generate the structured artifact.

A limitation of the causal gear model for decision governance is its strict schema requirement: it demands explicit inputs for constraints and stakeholders to accurately identify bottlenecks and death_traps, potentially oversimplifying ambiguous inputs.