Advanced Agents & Tools: From Chatbots to Problem Solvers

Welcome to the final module of our Prompt Engineering course. This is the Advanced tier. We're moving beyond simple Q&A into the world of Agents—models that can use tools, make plans, and execute complex workflows.

ReAct stands for Reasoning + Acting. It's a prompting framework that allows LLMs to interact with the external world.

1. Thought: The model reasons about the current state. ("I need to find the population of France.")
2. Action: The model decides to use a tool. ("Search: Population of France")
3. Observation: The tool executes and returns the result. ("67 million")
4. Thought: The model processes the new information. ("Okay, 67 million. Now I need to find the population of Germany.")
5. Action: ("Search: Population of Germany") ...
6. Final Answer: "Germany has a larger population than France."

Prompt Template:

You have access to the following tools:

• Search: Use this to search Google.
• Calculator: Use this for math.

Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [Search, Calculator] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Observation can repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question

Modern models (Gemini, GPT-4) have native support for "Function Calling". Instead of parsing text like "Action: Search", you define a JSON schema for your functions, and the model outputs structured arguments for those functions.

Schema:

EXPANDCOPY
json
{
  "name": "get_weather",
  "description": "Get the current weather in a given location",
  "parameters": {
    "type": "object",
    "properties": {
      "location": {"type": "string"},
      "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
    },
    "required": ["location"]
  }
}

Model Output: get_weather(location="Tokyo", unit="celsius")

This makes building reliable agents much easier because the model is guaranteed to output valid arguments.

For multi-step tasks (e.g., "Plan a trip to Paris"), simple ReAct loops can get stuck. Planning agents first generate a high-level plan and then execute it step-by-step.

Prompt:

You are a travel agent. Create a detailed itinerary for a 3-day trip to Paris.

1. List the top 3 attractions.
2. Create a day-by-day schedule.
3. Suggest restaurants near each attraction.

Plan: [Model generates plan]

Execution: [Model executes plan using tools]

Congratulations! You have completed the Prompt Engineering University Course. From zero-shot basics to building autonomous agents, you now have the tools to master LLMs. Go build something amazing!