Week 5 — Agentic AI & MCP
This week marks the shift from passive LLM usage to active agentic systems. You'll learn how to build AI agents that can reason about problems, plan multi-step solutions, use external tools, and even reflect on their own outputs to improve.
We also cover the Model Context Protocol (MCP) — Anthropic's open standard for connecting AI models to external data sources and tools. By the end of this week, you'll have built agents that can browse the web, analyze images, and orchestrate complex workflows.
Pages
| # | Page | Description |
|---|---|---|
| 1 | LLM Agents | ReAct loop, plan-and-execute, reflexion patterns |
| 2 | Pydantic AI | Defining agents, tool registration, structured output |
| 3 | MCP Protocol | MCP architecture, client/server model |
| 4 | MCP Server | Building custom MCP servers with FastMCP |
| 5 | Multimodal Agents | Vision + tool use, image analysis, web browsing |
| 6 | LangGraph | Stateful multi-agent workflows and graph orchestration |
Key Concepts
- Agent Loop: The core cycle of Observe → Think → Act → Reflect that powers autonomous agents
- Tool Use: How LLMs can call external functions, APIs, and services
- MCP: A universal protocol for connecting LLMs to tools and data sources
- Multimodal Reasoning: Combining text, image, and audio inputs for richer agent capabilities
Prerequisites
Before starting this week, make sure you're comfortable with:
- Python async/await patterns
- FastAPI basics (Week 2)
- LLM function calling (Week 3)
- API design and REST conventions
Lab Connection
Lab 6 — AI Agent + MCP puts all of these concepts together into a full agentic system. Plan to start it after completing pages 3 and 4.