The ReAct Method: How AI Agents Reason and Act

Why Agents Matter

Think about how you solve a complex problem. You do not just think about it, you:

1. →Reason about what to do next
2. →Act by looking something up, running a calculation, or asking someone
3. →Observe the result
4. →Repeat until the problem is solved

This is exactly what ReAct agents do. They are AI systems that can plan, use tools, learn from feedback, and iterate toward a solution.

The ReAct Loop

Agents vs Chains: When to Use What

Tools: The Agent's Hands

Safety and Guardrails

1. →Tool whitelisting, Only give agents access to tools they need. A customer support agent does not need file system access.
2. →Confirmation for destructive actions, Require human approval before sending emails, making purchases, or deleting data.
3. →Iteration limits, Set a maximum number of ReAct loops (typically 5-10) to prevent infinite loops.
4. →Output validation, Check the agent's final answer against the observations to detect hallucination.
5. →Cost controls, Each loop is an API call. Set budget limits to prevent runaway costs.

Test Your Understanding

What's Next

You now understand the ReAct framework. In the next workshop, you will build your own AI agent with tool use and safety guardrails.

• →AI Agent Architecture Patterns, The 5 workflow patterns for building effective agents

Continue to the workshop: Build an AI Agent with Safety to create an autonomous agent.