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system architectureChapter 5Apple ML Research · 2025

Reinforced Agent Inference Feedback

Apple ML Research Team (Apple)

Abstract

We present a method for improving tool-calling agents at inference time through a reviewer agent that evaluates tool calls before execution. The reviewer provides feedback that allows the primary agent to correct its tool calls without retraining.

Key Contributions

  • →Reviewer agent for tool call validation
  • →Inference-time correction without retraining
  • →Reduced tool call errors in production
Eigenvector Commentary

This paper represents a paradigm shift: verification is cheaper than perfect generation. The reviewer-agent pattern is directly applicable to enterprise deployments where you cannot retrain models but must ensure tool call correctness. Every enterprise agent pipeline should implement some variant of this.

Topics

inference-time feedbacktool useself-correctionreviewer agent