Skip to content
Velaris

Concept

What is Agent Loop?

Also known as: reasoning loop, execution loop

An agent loop is the cycle an AI agent repeats to make progress — decide the next step, call a tool, observe the result, update its state — running until the goal is met or a limit stops it.

The loop is what separates an agent from a single model call. One turn in, one answer out becomes: look at the goal and what's known, choose the next action, execute it, fold the result into state, repeat. A booking agent might loop six times — check the calendar, find a slot, look up an address, draft an invite, hit approval, send. Loops need exits: a completion condition, a step or token budget, and a rule for what to do when a tool keeps failing.

Velaris runs agents as LangGraph graphs, which makes the loop a real structure rather than a while-statement around a chat call: state is explicit, steps are nodes, and a run can suspend and resume. That's what allows an approval to interrupt mid-loop — a destructive-tier tool call halts the graph, waits for a human decision, then continues from exactly where it stopped. Credentials are scoped to the run, so the loop's reach is bounded no matter how many iterations it takes.

Related terms

See it in action

Velaris puts agent loop to work inside a real AI Operating System.

Get early access