Reflection Without Thrash: Re-entrant Self-Monitoring Loops as a Regulated Control Regime in Agents and Robots

As agentic AI systems gain persistence, tool access, and autonomy, teams increasingly try to add “self-awareness” via reflection: self-critique, self-evaluation, and iterative plan revision. In practice, these reflective loops often destabilize—producing oscillatory planning, revision spirals, tool thrash, silent reversion under unchanged evidence, and self-disowning reasoning. This note frames “self-aware” behavior operationally as a re-entrant self-monitoring loop (propose → critique → revise → re-critique) with standard stability constraints (gain, delay, and evidence noise). We argue that stable reflection is not a capability add-on; it is a regulated regime. Building on Baseline’s coordination vs compensation lens, the note defines measurable failure signatures and proposes a minimal governor: evidence-delta gating for durable updates, band-limited reflection budgets, gain-limited edits, phase-scoped durable commits with rollback semantics, and recovery half-life after override or thrash. The goal is practical and falsifiable: improve decision stability, reduce thrash, and make “reflection” regression-testable in both software agents and robotics stacks—without making any claims about machine consciousness or sentience.