Agents 101
Most of the jargon you hear (“LLM”, “system prompt”, “MCP server”, “tool use”, “skills”) collapses into one definition and four concepts. Here they are, in the order they matter when you sit down to use an agent.
What is an agent
An agent is an AI entity that can take actions and make decisions on its own. That is what separates it from a plain chat with a large language model. An LLM gives you an answer; an agent goes off and does things with it.
Most agents you will meet are powered by an LLM under the hood, but the agent is not the LLM. The LLM is the brain making the calls. The agent is the thing wrapped around it that can actually act on those calls: open a file, run a piece of code, search the web, send a message, kick off the next step.
The other distinguishing feature is agency. Inside the room you give it, the agent decides what to do, in what order, and how long to keep going until the goal is reached. The first time you hand a task to a capable agent and watch it loop on its own for ten minutes without needing a follow-up, the difference clicks.
Context
Context is everything the model is looking at right now. The prompt you just typed, the documents you shared, the back-and-forth so far. You will hear “context engineering”: that just means making sure the model is looking at the right things at the right time for the task in front of you.
Two practical limits. Models have a context window: they can only look at so much at once. And bigger is not better. Stuff a model with everything you can find and quality goes down, not up, because the relevant signal gets buried. More context also costs more, both in time and in money.
So treat context the way you would treat a smart colleague: as much relevant information as possible, as little irrelevant information as possible. The “system prompt” people talk about is just a slug of context that loads at the top of every session. Optimising what is in it is one of the highest-leverage things you can do.
Prompt
A prompt is the instruction you give the model in the moment. What you want, what you would like it to do, what “done” looks like. The recipe for a good outcome is that simple: a good prompt plus good context. When the answer comes back generic or off, it is almost always one of the two missing.
There are more structured ways to bake instructions into your prompts so you stop retyping them every session. We get to those when we look at specific tools, in the next level of the bootcamp.
Skills
A skill tells your agent how to do something. Under the hood, a skill is a prompt that loads automatically when it is relevant: the steps to follow, the tools to call, the references to pull in, the shape the output should take.
You write a skill the moment you notice you are walking the agent through the same recipe a third time. The retyping is the signal. Once you start writing them, the same handful of skills end up running most of your week.
Tools and connectors
Tools and connectors are how an agent reaches out to other software. The same way you use the apps on your computer, an agent uses tools and connectors to act on systems beyond the chat box.
A connector hooks the agent up to a separate system: your calendar, your inbox, a shared document store, a database. A tool is one discrete action the agent can take. Tools can come bundled with a connector (“create event”, “find a free slot” arriving with a calendar connector), or they can be local (“look up today’s date”, “run this piece of code”). The range goes from trivial to genuinely complex.
You will hear MCP a lot. For now, treat it as a synonym for connector. The acronym names a standard for how connectors are built, but in conversation people use it interchangeably with the thing it does.
4 quick scenario questions. Pick the best fit, see why.
Hands-on
Pick one AI tool you used this week.
For each of the four ideas above (context, prompt, skills, tools and connectors), find where it shows up in that tool. Where does context live? Is there a place to set persistent instructions? Are there skills? What about connectors?
Note the one idea you could not locate. That is what to look up the next time you open the tool.
Reflect
- Which of the four ideas felt most familiar, and which felt new? The new ones are usually the ones doing the most work in the tools you already use.
- Where in your week could a skill replace a prompt you keep retyping by hand?
View as plain markdown for LLMs and copy-paste