Agents and Tooling — Free AI Learning Track

Last updated: 2026-07-11

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 (4 min)

Agents are where AI stops talking and starts doing things that change the world.

A chatbot answers questions. An agent takes actions that have side effects. The difference is not intelligence, it is architecture. An agent combines a language model as the reasoning engine, a context window as its working memory, tools as its hands, and external memory as its long-term storage. Its control flow follows the ReAct loop: reason, act, observe, reason again, until the goal is reached. Plan-and-execute is a faster alternative when the path is known in advance. In practice, most systems blend both, adding human checkpoints before any irreversible action.

How to Design an AI Agent (4 min)

Agents are built through a ten-step iterative evolution, not a single deployment event.

A durable agent is the product of a disciplined evolution from manual work to autonomous operation. You start by doing the task yourself and documenting the runbook. You then use a language model as an assistant. You add tools through the Model Context Protocol so the agent can act on external systems. You teach the agent procedures through reusable skills. You give it memory. You orchestrate its work through standard operating procedures. You package it as a named agent. You schedule it. You integrate it with production triggers. Finally, you scale to multiple specialized agents coordinated by an orchestrator. At each stage, the human moves right along the workflow, doing less direct work but holding more accountability.

How MCP Works (4 min)

The Model Context Protocol is the emerging standard for connecting AI systems to data and tools, and it is already in production.

Every AI integration used to be a custom build. With N AI applications and M data sources, that meant N times M bespoke connectors. The Model Context Protocol, released by Anthropic in late 2024, replaces that pattern with a single open specification. N clients plus M servers instead of N times M connectors. An AI host such as Claude Desktop or an IDE runs a client that talks to one or more servers, each of which exposes a database, an API, a file store, or an action. The protocol carries three primitives: resources that the model can read, prompts that are reusable templates, and tools that the model can call to take action.

How ChatGPT Apps Work (4 min)

The distribution surface has shifted. Eight hundred million weekly ChatGPT users can now use interactive third-party apps inside the chat.

ChatGPT Apps replaced the earlier Plugins and GPTs approach with a richer architecture. Instead of returning plain text, an app can render a full interactive widget directly inside the conversation. The app has three parts: a Model Context Protocol server that exposes tools and UI resources, a widget that runs in a sandboxed iframe with its own state and event handling, and ChatGPT itself as the host that decides when to invoke the app and renders the widget. Users install apps through settings; conversations can then summon widgets automatically based on context or by explicit mention.

Part of the free AI Learning Hub by Shahzad Asghar. The whole track takes about 16 minutes and is free to read with no account required.