Phase 15: AI Agents

Build intelligent agents that can reason, plan, and use tools to accomplish complex tasks

🎯 Learning Objectives

By the end of this phase, you will:

  • ✅ Understand what AI agents are and how they differ from simple chatbots

  • ✅ Design and implement tool/function schemas for agents

  • ✅ Build agents that can use multiple tools to accomplish tasks

  • ✅ Implement the ReAct (Reasoning + Acting) pattern

  • ✅ Create multi-step agents with memory and state management

  • ✅ Use agent frameworks (LangChain, LangGraph, CrewAI)

  • ✅ Deploy production-ready AI agents

📚 What Are AI Agents?

AI Agent: An autonomous system that can:

  • Perceive its environment (receive inputs)

  • Reason about what action to take

  • Act by using tools/functions

  • Learn from feedback to improve

Chatbot vs Agent

Feature

Chatbot

AI Agent

Capability

Responds to queries

Takes actions

Tools

None

Can use external tools

Autonomy

Passive

Proactive

Memory

Conversation history

Persistent state + context

Reasoning

Single-turn

Multi-step planning

Example

“Tell me about Paris”

“Book me a flight to Paris”

📂 Phase Content

Notebooks

  1. 01_intro_to_agents.ipynb

    • What are AI agents?

    • Agent architecture

    • Simple agent example

    • Agent design patterns

  2. 02_function_calling.ipynb

    • Tool schema design

    • OpenAI Function Calling API

    • Tool selection strategies

    • Error handling

  3. 03_react_pattern.ipynb

    • ReAct: Reasoning + Acting

    • Chain-of-thought with tools

    • Multi-step problem solving

    • Building a research agent

  4. 04_agent_frameworks.ipynb

    • LangChain agents

    • LangGraph for workflows

    • CrewAI for multi-agent systems

    • Framework comparison

  5. 05_multi_agent_systems.ipynb

    • Coordinating multiple agents

    • Agent communication protocols

    • Task delegation

    • Building an agent team

  6. 06_mcp_model_context_protocol.ipynb

    • MCP: the emerging standard for AI tool integration (2026)

    • Connecting LLMs to external tools and data sources

    • Building MCP servers and clients

  7. 07_openai_agents_sdk_langgraph.ipynb

    • OpenAI Agents SDK

    • LangGraph stateful agents

    • Comparing agent frameworks

  8. 08_reasoning_models.ipynb

    • o1, o3, DeepSeek R1 reasoning models

    • Using reasoning models in agents

    • When to use reasoning vs standard models

  9. 09_autonomous_agents_2026.ipynb

    • State of the art: autonomous agents in 2026

    • Production patterns and best practices

    • Future directions

Assignments & Practice

Quizzes

  • Pre-Quiz: Assess baseline knowledge

  • Post-Quiz: Validate learning outcomes

🛠️ Tools You’ll Use

  • OpenAI Function Calling - Native tool use

  • LangChain - Agent framework

  • LangGraph - Workflow orchestration

  • CrewAI - Multi-agent coordination

  • AutoGPT - Autonomous agent

  • BabyAGI - Task-driven agent

🚀 Real-World Applications

1. Customer Support Agent

  • Answer FAQs

  • Query knowledge base

  • Create support tickets

  • Escalate to humans

2. Research Assistant

  • Search web/papers

  • Summarize findings

  • Generate reports

  • Cite sources

3. Code Generation Agent

  • Understand requirements

  • Write code

  • Run tests

  • Debug errors

  • Deploy to production

4. Data Analysis Agent

  • Load datasets

  • Exploratory analysis

  • Generate visualizations

  • Statistical testing

  • Create reports

5. Personal Assistant

  • Check calendar

  • Send emails

  • Book meetings

  • Set reminders

  • Research topics

📊 Project: Build Your Own Agent

You’ll build one of these agents:

  1. SQL Agent - Natural language → SQL queries → Results → Insights

  2. Research Agent - Topic → Search → Summarize → Report

  3. Coding Agent - Requirements → Code → Test → Fix → Deploy

  4. Customer Service Agent - Query → Knowledge base → Response → Ticket

⏱️ Time Commitment

  • Videos/Reading: 4 hours

  • Notebooks: 6 hours

  • Assignment: 8 hours

  • Challenges: 4-12 hours (optional)

  • Total: ~18-30 hours

📖 Prerequisites

Before starting this phase, ensure you understand:

  • ✅ LLM Basics (Phase 10: Prompt Engineering)

  • ✅ API Usage (Python, REST APIs)

  • ✅ JSON (Tool schemas are JSON)

  • ✅ Async Programming (For concurrent tool calls)

  • ✅ RAG Systems (Phase 7) - helpful but not required

🔗 Resources

Documentation

Papers

Videos

Community

🎓 Learning Path

Week 1: Fundamentals
├── Day 1-2: What are agents? (Notebook 1)
├── Day 3-4: Function calling (Notebook 2)
└── Day 5-7: ReAct pattern (Notebook 3)

Week 2: Frameworks & Production
├── Day 1-3: Agent frameworks (Notebook 4)
├── Day 4-5: Memory systems (Notebook 5)
└── Day 6-7: Multi-agent (Notebook 6)

Week 3: Project
├── Day 1-2: Design your agent
├── Day 3-5: Build & test
├── Day 6-7: Optimize & deploy

✅ Assessment

Pre-Quiz (10 questions)

Test your baseline knowledge of:

  • Agent concepts

  • Tool use patterns

  • API design

Post-Quiz (10 questions)

Validate your mastery of:

  • Agent architecture

  • Function calling implementation

  • ReAct pattern

  • Production best practices

Assignment (100 points)

Build a production-ready agent with:

  • Multiple tools (30 pts)

  • Error handling (20 pts)

  • Memory management (20 pts)

  • Evaluation & testing (30 pts)

🏆 Success Criteria

By the end of this phase, you should be able to:

  • ✅ Explain the difference between chatbots and agents

  • ✅ Design effective tool schemas

  • ✅ Implement function calling with OpenAI API

  • ✅ Build a ReAct agent from scratch

  • ✅ Use LangChain/LangGraph for complex workflows

  • ✅ Implement agent memory and state

  • ✅ Deploy a production agent

  • ✅ Debug common agent issues

🚀 Next Steps

After completing this phase:

  1. Phase 15: Low-Code AI Tools (Gradio, Streamlit)

  2. Phase 16: Debugging AI Systems

  3. Phase 17: Model Evaluation & Metrics

Or explore advanced topics:

  • Multi-agent collaboration

  • Agent fine-tuning

  • Reinforcement learning for agents

  • Human-in-the-loop systems

Ready to build intelligent agents? Let’s go! 🤖✨