Generalized Agent Iteration unifies iterative policy improvement and recursive self-improvement
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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.
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.