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Chapter 2 · 2025

Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents

Mrinal Rawat, Ambuje Gupta, Rushil Goomer

Abstract

The ReAct capability in LLMs forms the foundation of modern agentic systems, but smaller models struggle with complex reasoning tasks. This paper introduces Pre-Act, a novel approach that enhances agent performance by creating a multi-step execution plan with detailed reasoning. It incrementally incorporates previous steps and tool outputs, refining itself after each step. Experiments show that fine-tuned smaller models using Pre-Act can outperform larger models like GPT-4 in action accuracy and goal completion rate.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper directly addresses one of the core structural challenges in Zone III deployments. The research on LLM agents, multi-step planning, reasoning provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

LLM agentsmulti-step planningreasoningReActtask-oriented agents