Building AI Agent Workflows

The ReAct Pattern

ReAct interleaves Reasoning and Acting, letting the Agent alternate between thinking and executing.

Implementation

def agent_loop(query, max_steps=5):
    history = [{"role": "user", "content": query}]
    for step in range(max_steps):
        thought = llm.think(history, tools)
        if thought.action == "answer":
            return thought.content
        result = execute_tool(thought.action, thought.input)
        history.append({"role": "assistant", "content": f"Result: {result}"})

See AI Agent Research Status for background and Agent Framework Comparison for framework evaluation.