All insights

Series

AI Agents Explained

From first principles to production, in 12 parts. Each part answers one question in plain language, names the sources it relies on, and is checked by a second person before it goes live.

It is written for business owners, managers and junior developers. You do not need a technical background to start with Part 1. Parts that are not published yet are marked Coming soon.

Basic

Start here. What agents are, how they work, what they can and cannot do, and whether you need one.

  1. Part 1 of 12

    What is an AI agent, and how is it different from a chatbot?

    An AI agent uses a language model to decide its own steps and use tools to finish a task. Here is how that differs from a chatbot, in plain language.

  2. Part 2 of 12

    How does an AI agent actually work, step by step?

    An AI agent works in a loop: the model picks a step, a tool runs it, the result comes back, and the model decides what to do next. Here is each step.

  3. Part 3 of 12

    What can AI agents do today, and where do they still fail?

    AI agents now solve many real coding tasks but still slip on multi-step customer tasks, especially when repeated. Benchmark results as of October 2026.

  4. Part 4 of 12

    Do I need an AI agent, or is a simple automation enough?

    Many jobs need only a plain automation or a single AI call. Here is a simple way to tell when a task really needs an AI agent, with a worked example.

Intermediate

How agents use tools, what the Model Context Protocol is, how they remember, and how to build a first one.

  1. Part 5 of 12

    How do AI agents use tools and call functions?

    The model never runs a tool itself. It asks for one in a structured request, and your code usually does the work. See the round trip, step by step.

  2. Part 6 of 12

    What is the Model Context Protocol (MCP), and why does everyone mention it?

    MCP is an open standard that lets AI apps connect to tools and data the same way every time. Learn how it works, who runs it and where the risks are.

  3. Part 7 of 12

    How do AI agents remember things? Context windows, memory and RAG

    An AI agent only sees its context window. Learn why it forgets, how notes and memory tools help, and what retrieval-augmented generation (RAG) adds.

  4. Part 8 of 12

    How do I build a simple AI agent myself?

    How a simple AI agent is built: tool descriptions, a loop, a step limit and a stop condition, shown as a plain sketch, plus what a first version leaves out.

Advanced

Several agents together, testing, security, and what it takes to run an agent in a real business.

  1. Part 9 of 12

    When do several AI agents work better than one?

    Several AI agents can beat one on big jobs that split into parts, but they cost more and are harder to run. See the designs, the costs and how agents talk.

  2. Part 10 of 12

    How do I test whether an AI agent is any good?

    Test an AI agent with evals: realistic tasks, clear graders and repeated runs. Learn pass@k versus pass^k and how to build a first test set.

  3. Part 11 of 12

    How do I keep an AI agent safe? Prompt injection, permissions, human approval

    Keep an AI agent safe: what prompt injection is, why it has no complete fix, and how permissions and human approval limit the damage.

  4. Part 12 of 12

    What does it take to run an AI agent in a real business? Cost, monitoring, rollout

    Running an AI agent for real: how to estimate cost per task, handle rate limits, monitor it once live, and roll it out in stages, with a start-here checklist.

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