AI Agents
AI Agents
Models that decide what to do next, call tools, and act in a loop rather than answering once.
Grasp
A prompt produces one response. An agent runs a loop: observe the state, decide on an action, execute it, observe the result, and repeat until the task is done or the budget runs out.
That loop is the entire difference, and it changes the engineering problem completely. A single call either succeeds or fails and you see it immediately. An agent can take twenty steps, drift gradually off course, and produce a confident final answer built on a mistake at step three. Errors compound rather than surface.
Four capabilities separate a working agent from a demo. Tool use - reliably calling external functions with valid arguments. Memory - carrying relevant state across steps without exhausting the context window. Planning - decomposing a goal into steps that can actually be executed. Evaluation - knowing whether the loop is working, which is genuinely harder here than anywhere else in the Atlas. Build them in that order, and cap the loop long before you think you need to.