AI Agents
Intermediate 12 min read AI Agents

The Agent Loop

The runtime that keeps agents on track

The agent loop is the core runtime: reasoning turn, tool execution, result ingestion, capped iteration. Getting the loop right — message hygiene, termination, progress checks — determines reliability more than model choice.

agent loopruntimeturnterminationprogressmessage history
Prerequisites: AI Agents Overview
Before we start

What you will learn

  • Build a correct reasoning-act loop
  • Define termination and stop conditions
  • Keep message history healthy and bounded
Introduction

What is this?

The agent loop alternates model turns and tool turns: the model reasons (and maybe emits tool calls), tools execute and return results, histories accumulate, and the loop checks progress against caps. The quality of the loop — not the model — drives reliability: correct roles, trimmed context, explicit terminations, and honest progress checks.

Motivation

Why do you need it?

  • A subtle loop bug re-plays stale results or drops turn context.
  • Termination logic prevents runaway cost, latency, and loops.
  • Healthy histories = coherent long tasks.
  • Most agent failures trace to loop bugs more than model errors.
Think about it

Mental model

Imagine

A relay race with checkpoints

Think of

The agent loop

Runners (turns) pass the baton (context) in a fixed rhythm; marshals (loop code) check each checkpoint (progress). The race ends only at the finish or the marshal's whistle (stop condition) — and the baton state must be exact at every handoff.

Mechanics

How it works

  • Append user goal to a message history.
  • Model turn: may emit tool calls or finish.
  • Execute calls (validated), append tool results.
  • Check caps (turns, tokens, time) and progress.
  • Terminate: success, cap, stall, or explicit stop.

Loop anatomy.

Goal
Reason
Call?
Execute
Check
Finish
Real world

In production

  • Standard SDK agent loops (AI SDK, LangGraph) encode correct turns.
  • Custom loops parse tool results into structured observations.
  • Eval harnesses replay the same loop against golden tasks.
Guidelines

Best practices

  • Preserve role correctness in history (user/assistant/tool).
  • Keep tool results terse; trim history when long.
  • Terminate on explicit completion text, not silence.
  • Log turn-by-turn states for debugging.
Watch out

Common mistakes

  • Appending tool results under wrong roles.
  • No progress check — loops drift over repeated turns.
  • Unbounded history blowing context and confusing grounding.
Root cause analysis

Common issues

Error
Loop runs forever on some tasks
Root cause

Stop condition weak

Fix

Add turn/token caps + stall detection + explicit 'done' marker.

Error
Progress evaporates mid-task
Root cause

Terminal summaries not carried

Fix

Carry a 'progress so far' note into each model turn.

When things break

Debugging tips

  • Repeated identical tool call → stall detection missing.
  • Answer quotes stale data → history trimmed the observation; promote key facts.
  • Loop exits early → completion criterion too loose; require artifact/evidence.
Interviews

Interview checkpoint

Practice

Hands-on lab

Build a Minimal Agent Loop
40 min · intermediate
In progress
Objectives
  • Implement reasoning→tool→observe iteration
  • Enforce turn caps and stall detection
  • Return a final summary with progress

Build a Minimal Agent Loop

40 min · intermediate

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sandbox · Linux sandbox
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Test yourself

Quick quiz

0/2
1

The agent loop terminates on…

2

Tool results can be dropped from history without consequence.

Go deeper

Official documentation

Build an agent (OpenAI)
docs