AI Agents From Scratch: Tools, Memory, MCP and Multi-Agent

About this course

This course contains the use of artificial intelligence.

Most AI agent tutorials stop at the demo. You build a loop, call a tool, and it works once. Then the model returns an unexpected argument, a tool fails, or the agent retries forever.

This course focuses on everything that comes after the demo works.

You will build a real tool-calling AI agent from an empty file and turn it into a system that can use tools, maintain memory, connect through MCP, coordinate multiple agents, run unattended, and fail safely.

What You’ll Build

You’ll build the actual agent loop yourself, including:

• Tool dispatch and structured schemas
• Argument validation
• Error handling and retries
• Permission boundaries
• Max-turn governors
• State management
• Cost and safety controls
• You’ll deliberately break the agent, inspect the failure, and fix the real problem instead of hiding it.

Tool Calling and Safety

Learn how to design reliable tools and control what an agent is allowed to do.

Topics include:

• Good and bad tool schemas
• Hallucinated arguments
• Infinite retry prevention
• Sandboxing
• Permission boundaries
• Execution limits

Memory and RAG

Learn why context is not the same as memory.

You’ll work with:

• File-based persistence
• Agent stateRAGDocument chunking
• Retrieval pipelines
• Detecting stale or incorrect memory

Build an MCP Server

You’ll build your own MCP server from scratch and learn how to:

• Expose tools through MCPDefine schemas
• Handle authentication
• Connect to Claude CodeDebug connection and schema failures

Multi-Agent SystemsBui

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