Agents and Tooling — Free AI Learning Track

How models take action and interoperate.

The Agents and Tooling track covers how AI systems stop talking and start doing. An agent takes actions with side effects, which changes the engineering questions from answer quality to safety, specification, and interoperability.

Four modules map the territory. How AI Agents Work explains the core architecture: the language model as reasoning engine, tools as hands, memory, the ReAct loop, and the guardrails that keep irreversible actions behind checkpoints. How to Design an AI Agent lays out a ten-step evolution from a manual runbook to orchestrated multi-agent systems, with the human accountable at every stage. How MCP Works introduces the Model Context Protocol, the open standard that replaces bespoke integrations with a host, client, and server architecture carrying resources, prompts, and tools. How ChatGPT Apps Work shows the same protocol powering interactive widgets inside a conversational surface, along with its strict sandbox security model.

Read this track before approving or building any system that acts on production data. The recurring theme is that guardrails belong in code, not in prompts.

Modules in this track

• How AI Agents Work — Agents are where AI stops talking and starts doing things that change the world.
• How to Design an AI Agent — Agents are built through a ten-step iterative evolution, not a single deployment event.
• How MCP Works — The Model Context Protocol is the emerging standard for connecting AI systems to data and tools, and it is already in production.
• How ChatGPT Apps Work — The distribution surface has shifted. Eight hundred million weekly ChatGPT users can now use interactive third-party apps inside the chat.

Part of the free AI Learning Hub by Shahzad Asghar.