What is Agent Loop?
Agent InternalsThe repeating cycle an agent runs: perceive the environment, reason about options, plan the next step, act through tools, and learn from the outcome. Perception, reasoning, planning, action, and learning are its five components.
Why It Matters
A single model call is a function: input in, text out. The loop is what makes it an agent. Each iteration feeds the last step’s outcome back into the next decision, so the system adapts to what actually happened instead of executing a script written in advance.
The loop is also where the risk compounds. One bad decision is contained; a bad decision that feeds ten more iterations is an incident.
One Iteration
Perception. Gathers what the environment looks like right now: API responses, retrieved documents, tool results, user messages.
Reasoning. Interprets what perception gathered and weighs the options against the goal.
Planning. Picks the next step: which tool, with which arguments, in service of which part of the plan.
Action. Emits the tool call; the agent’s code executes it and returns the result.
Learning. Writes the outcome back into context so the next iteration starts informed by this one.
Then the loop repeats until the goal completes, a budget trips, or the agent hands control back to a human.
Where It Breaks
The loop has no natural stopping point. An agent that cannot satisfy its goal keeps trying: re-planning, re-prompting, re-calling tools, each iteration costing money and none of them failing loudly enough to stop the run. Loop bounds (iteration caps, spend caps, time caps) are not optional parts of the loop; they are the part that makes it safe to run.
Errors also compound across iterations. A wrong observation in step two becomes the premise of every step after it, and each subsequent decision is confidently built on a corrupted foundation.
How Flytebit Handles It
Loop bounds are part of the operating envelope we define in the first architecture review: iteration caps, cost bounds, and escalation triggers that end the run deliberately rather than letting it end accidentally. Every iteration lands in the trajectory so the loop is inspectable after the fact. The mechanics are in How AI Agents Work, and the envelope design is in how we build agentic systems.