Autonomous AI Agents & Tool Calling

The ReAct Loop, Function Calling, Memory, and Multi-Agent Orchestration

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AI Agents extend static language models into autonomous systems that observe environments, reason about multi-step plans, call external APIs and tools, and iteratively verify outputs.

The ReAct (Reason + Act) Loop

1Thought: The agent breaks down the user objective into intermediate sub-goals ("I need to query the user table for customer id 482").

2Action: The model outputs structured JSON invoking an external tool function (e.g., execute_sql_query({"id": 482})).

3Observation: The runtime executes the tool and passes the raw execution result back to the model.

4Loop / Final Output: The agent inspects the observation, decides if more actions are required, or synthesizes the final response.

Function Calling Tool Definition Schema

Try It Yourself »

const weatherToolDefinition = {
  name: "get_current_weather",
  description: "Get real-time temperature and conditions for a given location",
  parameters: {
    type: "object",
    properties: {
      location: { type: "string", description: "City and state, e.g. Seattle, WA" },
      unit: { type: "string", enum: ["celsius", "fahrenheit"] }
    },
    required: ["location"]
  }
};

Tool definitions use JSON Schema so the LLM outputs strictly validated arguments.

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Test Yourself With Exercise

+15 XP

What are the core steps of the ReAct pattern in autonomous agent architectures?

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