AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → errors compound multiplicatively. At 95% accuracy per step, a 10-step task lands at 60% and a 100-step task at 0.6%. That arithmetic, not ambition, is why agents need stronger models than single-shot prompting does.
An agent is pinned down by two things that limit each other: the environment it perceives and acts on, and its tool inventory. A chess agent's only possible actions are legal moves; a robot whose one action is swimming is confined to water.
Sort tools by whether they read the environment or change it. Read-only tools gather context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →; write actions let the agent act on the world, which is where both the payoff and the severe failures live.
More tools is not strictly better. Each added tool makes selection and correct use harder for the model, so the right inventory is found by experiment rather than by handing the agent everything available.
Web browsing stops a model from going stale, but every internet tool also pipes the open web straight into your agent's context. Choose those APIs deliberately rather than wiring in whatever is easiest.
Terms in this piece · Glossary
grounding — Tying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
Why it matters
A rigorous, literature-groundingTying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.Full definition → framework for defining, building, and evaluating AI agents—covering tools, planning, and the new failure modes agents introduce—rather than a surface-level overview. Useful if you're moving past prompt chaining and need structured thinking on agent architecture and evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition →.
Key quotes
“An agent is anything that can perceive its environment and act upon that environment.”
“If the model's accuracy is 95% per step, over 10 steps, the accuracy will drop to 60%, and over 100 steps, the accuracy will be only 0.6%.”
“Just as you shouldn't give an intern the authority to delete your production database, you shouldn't allow an unreliable AI to initiate bank transfers.”
“Personally, I would trust a self-driving car more than the average stranger to give me a lift.”
