title: "Building a Multi-Step Reasoning Agent with Tool Use" date: "2025-08-05T00:00:00Z" excerpt: "Architecting an autonomous ReAct agent with GPT-4 function calling, dual memory, and self-correction loops." tag: "AI Agents" readTime: "6 min" accentColor: "teal"

Beyond Single-Shot LLM Calls

Research tasks require multi-step reasoning: decomposing questions, searching across sources, evaluating relevance, synthesizing findings, and producing coherent reports. Single-shot LLM calls can't handle this complexity reliably.

To solve this, I built an autonomous AI agent using the ReAct (Reason + Act) pattern and GPT-4 function calling.

The ReAct Pattern

ReAct interleaves reasoning with action, allowing the agent to adapt its research strategy based on intermediate findings rather than committing to a fixed plan upfront. The agent can search the web, extract content, and manage citations dynamically.

Thought: I need to find recent papers on speculative decoding.
Action: search_web("speculative decoding LLM optimization 2025")
Observation: [Search Results]
Thought: The second result looks like a comprehensive benchmark. I will extract its content.
Action: extract_content(url_2)

Dual Memory Architecture

Memory architecture decisions fundamentally shape what an agent can learn over time. This agent uses a dual memory system:

1. Short-Term Conversation Buffer: Maintains session coherence and immediate context.
2. Persistent Vector Memory (ChromaDB): Enables the agent to build cumulative knowledge across sessions, which is critical for ongoing research topics.

Self-Correction Loops

Without self-correction, agents accept first-pass results regardless of quality. Adding reflection and retry steps significantly improved report completeness and accuracy. If the agent detects contradictory findings, it automatically spawns additional research steps to resolve the conflict before generating the final cited report.