AI Agents: End to End
Tools, the agent loop, memory, workflows, MCP, and production safeguards.
7 lessons · about 60 minutes of reading
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Start lesson 1- 1
What Are AI Agents? Workflows vs Agents and the Agent Loop
Understand what makes an AI agent different from a chatbot or a fixed workflow, and when you actually need one.
8 min read
- 2
AI Agent Tools: How Function Calling Works
Define tools with JSON schemas, see how the model requests them, and return results correctly.
9 min read
- 3
Building an AI Agent Loop in Python
Write a complete, safe agent loop that calls tools until the task is done, with a turn limit and error handling.
9 min read
- 4
AI Agent Memory and Context Management
Keep long-running agents effective with conversation history, summarization, external memory, and context limits in mind.
8 min read
- 5
Agent Workflow Patterns: Chaining, Routing, and Evaluator Loops
Learn the building-block workflow patterns that often beat a fully autonomous agent, with code for each.
9 min read
- 6
Model Context Protocol (MCP) Basics: Connecting Agents to Tools
Understand what MCP is, build a tiny MCP server in Python, and learn how agents connect to MCP servers safely.
8 min read
- 7
AI Agent Guardrails, Evaluation, and Production Readiness
Add limits, approvals, logging, and evaluation to make an agent safe, observable, and reliable enough to ship.
9 min read