Generalized Agent Iteration unifies iterative policy improvement and recursive self-improvement
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Generalized Agent Iteration reduces policy improvement and recursive self-improvement to two operational switches: whether the updater is inside the agent, and whether the evaluation standard is external or self-referential. For production agent systems, this gives a concrete way to classify self-modifying loops and identify where goal drift or ungrounded evaluation can enter before you let agents rewrite policies, tools, prompts, or evaluators.
The Generalized Agent Iteration framework mathematically unifies iterative policy improvement and recursive self-improvement using two architectural dials: whether the improvement mechanism is internal, and whether the evaluation metric is anchored externally. For production engineers building autonomous agent loops, this taxonomy provides a formal method to predict, isolate, and prevent goal drift and self-referential system degradation. It enables you to systematically audit self-improving pipelines and design rigorous, externally grounded guardrails that keep autonomous agents aligned with their original deployment objectives.