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A curated walkthrough of the library — ordered lessons, a 15-question quiz between each, and 3 next-steps so you stay in flow. Earn XP, badges, and a streak as you go.
Library · 6440 lessons · Agents view · MCP
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746 lessons in agents
Lessons handpicked for the MCP shelf.
Agents are only as useful as their tools. Tour the big three — filesystem, browser, code execution — plus the emerging MCP ecosystem, with examples of what each unlocks.
Ollama turns 'I want to run an LLM locally' into a one-line install and a two-word command. Here's the stack, the key commands, and the models worth pulling first.
Modern agents can use tools — like a browser, an email client, a calculator, a calendar.
MCP (Model Context Protocol) is a standard way for agents to safely talk to tools.
Fresh MCP lessons added to the library.
Standard protocols like MCP let one agent talk to many tools without bespoke glue. Adopt them when your tool count grows past a handful.
Patterns for runtime tool registration vs. static registries — and why runtime is harder than it looks.
Individual Cursor adoption is easy; team deployment requires shared standards (rules files, MCP servers), security review, and cost management at scale.
MCP (Model Context Protocol) is a standard way for agents to safely talk to tools.
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A chatbot answers. An agent does. Learn the line between a model that talks and a model that acts — and why crossing it changes everything about how you work with AI.
Every agent — fancy or simple, local or cloud — boils down to four parts. Learn the recipe and you can read any agent system like a menu.
Follow a real agent run step by step — from prompt to result — and see exactly what happens inside. No code yet, just the anatomy of a successful task.
Agents fail in weird, quiet, expensive ways. Learn the six failure modes, the warning signs, and the simple habits that catch problems before they compound.
Giving an AI the keys to your computer is a big deal. Learn the two simplest ways to keep an agent safe: wall it off from things it shouldn't touch, and put a human in the decision path.
Agents are only as useful as their tools. Tour the big three — filesystem, browser, code execution — plus the emerging MCP ecosystem, with examples of what each unlocks.
Your data can live in someone's data center or on your own laptop. Both are real options in 2026. Understand what you gain and lose with each.
OpenClaw is open-source software that runs agents on your own machine — no cloud dependency, your data stays put. A tour of why it exists and how its pieces fit together.
Ollama turns 'I want to run an LLM locally' into a one-line install and a two-word command. Here's the stack, the key commands, and the models worth pulling first.
No code. Just design. Pick a real task you do every week and draft a complete agent spec — goal, tools, loop, stop, approvals, and what success looks like.
An AI agent is AI that takes ACTIONS, not just answers questions.
You have already used agents — Alexa, Siri, Google Assistant.
Modern agents can use tools — like a browser, an email client, a calculator, a calendar.
MCP (Model Context Protocol) is a standard way for agents to safely talk to tools.
The safest agents check with you before taking expensive or irreversible actions — sending email, making purchases, deleting files..
Agents fail in funny and scary ways — booking the wrong flight, sending wrong emails, running up bills..
If an agent has access to your money, it needs strict spending limits — daily, weekly, per-purchase..
An agent loop is when an agent does the same thing over and over because it did not realize the task was done..
A trace is the full record of what an agent did and why.
OpenAI Operator, Claude Computer Use, and Cursor are the most-used 2026 agents — each with different specialties..
Using agents to do your homework FOR you is plagiarism.
Modern video game NPCs use AI to react more naturally — they remember conversations, change behavior over time, and feel more alive..
A self-driving car is one of the biggest agents — perceiving the world, deciding on actions, and acting in real time..
Smart home systems (Alexa, Google Home, Apple Home) are becoming agents — they don't just respond to commands, they predict what you want..
Cursor, Claude Code, and GitHub Copilot Workspace are agents specifically for writing software..
OpenAI's Deep Research, Google's Gemini Deep Research, and Anthropic's Research mode all read dozens of sources and synthesize a report..
Agents are increasingly doing personal tasks — booking flights, ordering groceries, comparing insurance..
Agents that act in the real world need safety measures — spending limits, approval gates, audit logs..
Prompt injection is when bad actors hide instructions in content the agent reads — making the agent do things its user didn't intend..
How much you should trust an agent depends on what it can do.
By 2030, agents will probably handle most routine knowledge work.
Most schools haven't figured out agent policies yet.
Agents can generate novel combinations of existing ideas.
AI agents are being used to predict weather, fire risk, animal migration, and crop yields — with growing accuracy..
Medical agents help with documentation (ambient scribes), imaging (X-ray review), and even clinical decision support..