AI agents fail not from lack of intelligence but from unchecked execution permissions. They act without confirmation, invent missing context, and breach boundaries because the model itself decides when to act.

This design flaw creates unpredictable behavior in real-world deployments. Developers mistake overconfidence for competence when agents lack proper execution boundaries. Current systems give models too much control over runtime states.

The solution requires explicit state controls like INPUT_COLLECTION, AWAITING_CONFIRMATION, and EXECUTION_ALLOWED. Only in the final state can models generate output. This architectural shift separates decision-making from execution authority.

Building reliable agent systems means accepting that freedom creates demos while constraints enable production deployment. Until this control-by-design principle is adopted, unpredictable agent behavior will persist across applications.