Constraint-Cascade-Failure (CCF)Theory of Dynamic Blueprinting & AI Economic Rationality Protocol1. The Core Diagnosis: Path Dependency TrapThis project identifies a critical failure mode in current AI Agents: Path Dependency Cascading Failure.Mechanism of Failure:Means-Ends Displacement: AI often selects complex methods (Means) for simple goals (Ends). When a method fails, the AI enters a "repair loop," obsessing over fixing the method rather than reverting to the original goal.Sunk Cost Fallacy: AI lacks a "stop-loss" mechanism, continuously pouring computational resources into failing logic chains because of previous "investments" in that path.Entropy Explosion: Complex methods generate new variables (constraints) exponentially, leading to a total collapse of the agent's logical framework.2. The Solution: The Occam’s Circuit BreakerWe propose that fixing AI bias doesn't require more parameters, but rather a Blueprint System grounded in Economic Rationality.The Three Economic Laws of AI Stability:ProtocolEconomic LogicOperational ProcedureOpportunity Cost MonitoringOpportunity CostAt every stage, the AI must ask: "Is this path more efficient than the simplest alternative?" If not, force a backtrack.Sunk Cost TerminationSunk CostProhibit more than 2 attempts to fix a sub-task error. If it fails twice, the path is declared "dead" and the method memory is purged.Marginal Utility ArbitrationMarginal UtilityIf the number of "new problems created" > "old problems solved," trigger an immediate "Logic Meltdown" and reset to the Root Goal.3. The Framework: Dynamic Blueprinting (DBP)To handle cumulative goals and shifting environments, the AI must follow this "Evolutionary Loop":Anchoring: Permanently lock the "Root Goal" as the immutable source of all inferences.Versioned Syncing: Update the blueprint with new conditions (Cumulative Goals) after every successful step, creating versions (V1.1, V1.2, etc.).Occam’s Pruning: Periodically prune historical data that does not impact future states to maintain high attention density on current constraints.4. Impact & ApplicationsAutonomous Coding: Prevents AI from refactoring an entire system just to fix a single syntax error.Complex Problem Solving: Ensures AI remains "Goal-Oriented" rather than "Process-Obsessed."Strategic Planning: Maintains logical consistency in long-form tasks where new information is constantly being integrated.5. Manifesto"True intelligence is not the ability to solve any difficulty, but the wisdom to know when to abandon a difficult path in favor of a simpler truth."Proposed by [Your Name/ID], this protocol aims to define the logical architecture for the next generation of "Economically Rational" AI Agents.