Inversion of Control (IoC) in Autonomous Agents: A Paradigm Shift Towards Deterministic Orchestration

The prevailing Reasoning-and-Acting (ReAct) paradigm for Large Language Model (LLM) agents relies on the executive model to maintain control flow, leading to high stochasticity and "Intent Drift" in complex enterprise workflows. This paper introduces the Inversion of Control (IoC) framework for agents, where the decision-making authority is shifted from the execution model (Worker) to a centralized supervisor (Navigator). By decoupling strategic planning from atomic actions, we enable a Trace-to-Asset (T2A) evolution loop, allowing agents to distill successful reasoning traces into deterministic executable scripts. We argue that IoC is essential for transforming volatile AI interactions into stable, auditable, and self-evolving organizational assets.