# DeReAct improves weaker AI agents with external gating policies

Digest AI · Agents & Tools · published 2026-10-05T04:00:00Z

Canonical: https://digestai.news/story/dereact-improves-weaker-ai-agents-with-external-gating-policies

## Summary

Researchers introduced DeReAct, a modular agent architecture that separates action validation and task completion control from the main LLM policy. The approach uses a Critic to validate actions before execution and a Context Manager to verify task completion. Testing on GAIA and SWE-bench Verified showed DeReAct improved Pass@1 scores by 6.5–7.0 points for Qwen3-Coder-480B and 4.2–5.2 points for Claude Sonnet 4.5, with smaller gains for stronger models. With Claude Opus 4.5, performance remained similar to ReAct but produced more evidence-complete trajectories. The authors note external gating is effective when targeted failures are common and the gating policy is reliable.

## Key points

- DeReAct uses a Critic to validate actions and a Context Manager to certify task completion
- Pass@1 improved by 6.5–7.0 points for Qwen3-Coder-480B on GAIA and SWE-bench Verified
- Gains were 4.2–5.2 points for Claude Sonnet 4.5 and diminished with stronger models

## Why it matters

DeReAct shows how separating reasoning and acting can improve reliability in weaker AI agents without sacrificing grounding, offering a path to more trustworthy agent behavior.

## Sources

1. [DeReAct: Decomposed Reasoning and Acting for Reliable AI Agents](https://arxiv.org/abs/2610.02351) (arXiv cs.AI, 2026-10-05, primary source)

## Cite

Digest AI, "DeReAct improves weaker AI agents with external gating policies", 5 October 2026, https://digestai.news/story/dereact-improves-weaker-ai-agents-with-external-gating-policies

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