#4743 AI Agent Loops
#4743 AI Agent Loops #4743 Describe how a set of AI agents of various roles, including critique/antagonist, can work together to improve a user-specified system, possibly cycling over numerous loops/iterations An iterative, multi-agent refinement system operates like an automated engineering review board. Rather than relying on a single model to propose, analyze, and validate a solution simultaneously—which tends to produce blind spots, sycophancy, and premature convergence—the workload is decomposed into specialized roles operating over a governed state machine. Here is the operational architecture, role taxonomy, loop dynamics, and governance mechanisms required to make such a system converge effectively. 1. Core Agent Roles & Functional Specialization To prevent mutual confirmation bias, agents must have distinct operational charters, separate context windows, and explicitly misaligned incentives. Role Operational Charter Primary Objective / Invariant System Architect (Synt...