Modern AI Engineering

Lesson 12.2 · 26 min · video lesson

Loop Engineering: Designing Reliable Agentic Loops

Why does an agent that is clever on every single step still sometimes run in circles for fifty steps, or stop after two and proudly announce a job it never did?

In short: An AI agent works by running a loop: look at the situation, pick an action, run it, look at the result, and repeat. Loop engineering is the practice of designing that repeating cycle on purpose: what the model sees each turn, how actions run, how progress is tracked, and above all when and how the loop stops. Most agent failures are loop failures, and a handful of techniques (budgets, verification, loop detection, context trimming, recovery) fix most of them.

What you will learn

  • What is Loop Engineering?
  • Loop Engineering = Loop + Engineering
  • Why do we need Loop Engineering?
  • What is a loop in an AI agent?
  • The simplest loop and its problems
  • The parts of the loop that we must engineer
  • Prompt Engineering vs Context Engineering vs Loop Engineering
  • Common ways a loop breaks
  • Techniques of Loop Engineering
  • A complete example
  • Where it works well and where it fails

In this lesson

  1. What is loop engineering? Loop + engineering
  2. Why do we need loop engineering?
  3. What is a loop in an AI agent?
  4. The simplest loop and its problems
  5. The parts of the loop that we must engineer
  6. Prompt vs context vs loop engineering
  7. Common ways a loop breaks
  8. Techniques of loop engineering
  9. A complete example
  10. Worked example, step by step
  11. Practice: try it yourself
  12. Where it works well and where it fails

Key terms in this lesson

Agent loop
The repeating cycle in which a model picks an action, the harness runs it, and the result is fed back for the next decision.
Loop engineering
Deliberately designing an agent's loop: turn inputs, actions, progress tracking, recovery and stop conditions.
Stop condition
A rule that ends the loop: verified success, an exhausted budget, or detection that the agent is stuck.
Verified stopping
Ending the loop only when an independent check, not the model's claim, confirms the goal is met.
Budget
A hard limit on steps, time, tokens or money for one run.
Goal drift
When an agent, after many turns, starts working on something other than the original task.

This lesson is part of the Generative AI Engineering and Complete AI Engineer tracks. See plans and pricing to open the full lesson, its video, labs and quiz, or try the free lesson first (free account needed).

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