Build Your First AI Agent: Tools, Loops & Self-Correction

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About Course

Most people use AI like a search box: type a prompt, get an answer, repeat. AI agents work differently. They can take actions, use tools, and keep working until the job is done.

In this free mini course, the first step of the Zero-to-Hero Agent Roadmap, you’ll build your first AI agent from scratch in Python. You’ll learn how an agent decides what to do next, how it calls tools to get real work done, and how a self-correcting loop lets it catch and fix its own mistakes without you stepping in.

No frameworks or magic. You’ll write the core agent loop yourself, so you understand exactly what’s happening under the hood before moving on to tools like LangGraph in the next course.

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What Will You Learn?

  • Understand why basic chatbot prompting hits a wall, and what makes an AI agent different.
  • Learn the core agent loop: how an agent thinks, decides, acts, and observes results.
  • Build a tool-using AI agent from scratch in Python, without relying on frameworks.
  • Connect your agent to custom tools such as a calculator, file reader, or web lookup.
  • Design a self-correcting loop so your agent can check its own output and fix its mistakes.
  • Handle common agent problems like infinite loops, bad tool calls, and confusing outputs.
  • Gain a solid foundation for agent frameworks like LangGraph, covered in the next course.
  • Capstone Project: Self-Healing Code Agent
  • You'll put everything together by building an agent that writes Python code to solve a task, runs it, reads any errors or failed tests, and rewrites the code until it works. The project brings together every skill from the course: tool calling (running code and tests), the agent loop (plan, act, observe), and self-correction (learning from errors and retrying). You'll finish with a working project for your portfolio that shows you can build AI that does real work, not just chat.

Course Content

Module 01: The Agentic Shift – From Chatbots to Autonomous Reasoning Engines
Uncover why traditional zero-shot prompting fails at complex enterprise tasks due to the Single-Prompt Bottleneck. This module shifts your mindset from viewing LLMs as simple "text generators" to structuring them as dynamic Reasoning Engines. You will explore the Autonomy Spectrum to balance AI flexibility with enterprise predictability, master Task Decomposition to break complex human jobs into discrete micro-steps, and examine the 4 Core Agent Design Patterns (Reflection, Tool Use, Planning, and Multi-Agent Collaboration) that form the foundation of modern AI engineering. Key Skills Learned: Single-pass inference limits, Autonomy Spectrum management, Task Decomposition, 4 Core Agentic Patterns.

  • Lesson 1.1: Why ChatGPT Fails at Complex Work
    05:07
  • Lesson 1.2: Degrees of Autonomy – How Much Control Should AI Have?
    05:04
  • Lesson 1.3: Task Decomposition – Breaking Complex Jobs into AI Steps
    03:48
  • Lesson 1.4: The 4 Core Agent Design Patterns
    03:30

Module 02: Tool Use & Function Calling – Connecting LLMs to Real-World APIs
Break your AI models out of their static training sandboxes and connect them directly to live external data without hallucinating facts. In this hands-on module, you'll learn the crucial architectural distinction between consumer ChatGPT interfaces and Raw LLM APIs. You'll master how modern Python SDKs automatically parse Type Hints and Docstrings into JSON Schemas - teaching the LLM when and how to invoke functions. Finally, you will write pure Python code to connect the model to live, keyless APIs, inspecting the exact tool_calls payload generated by the LLM in real-time. Key Skills Learned: Tool Use Architecture, Docstring-to-JSON Schema conversion, Function Calling execution loops, API integration. Hands-on Exercise: Building a live Weather Forecast Tool using Open-Meteo API.

Module 03: Self-Healing Agents – Mastering Reflection & Error Recovery
Eliminate logic bugs, formatting errors, and broken code outputs by implementing self-correction loops. Discover why single-pass LLM generation falls into the First-Draft Trap and how the Reflection Design Pattern allows models to critique and refine their own work. By incorporating Andrew Ng's concept of External Deterministic Feedback, you will write Python code that intercepts terminal traceback errors (TypeError, SyntaxError) and feeds them back to the model - building an autonomous, self-healing agent that fixes its own code execution bugs. Key Skills Learned: Reflection Pattern implementation, First-Draft Trap mitigation, Traceback error catching, Self-Correction loops. Hands-on Exercise: Building a Self-Healing Python Code Execution Agent.

Module 04: Capstone Project – Building a Multi-Tool Agent & The Enterprise Bridge
Combine Tool Calling and Reflection into a single, production-style application: the Automated Weather & Travel Advisory Agent. You will build a multi-turn CLI agent that dynamically coordinates multiple free APIs (weather forecasts and live currency exchange rates), passes the output through a Reflection Quality Auditor, and generates a polished Markdown report. To wrap up, you will explore the Production Reality Check - analyzing why raw Python while loops fail in enterprise settings and discovering how LangGraph State Machines bridge the gap to enterprise-grade AI engineering. Key Skills Learned: Multi-Tool orchestration, Quality Audit Reflection loops, CLI Agent architecture, Enterprise Production limits (Persistence, Concurrency, Observability). Capstone Project: Complete Automated Travel & Packing Advisory CLI Agent.

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