Professional Certificate in Loop Engineering
Stop babysitting your coding agent. Engineer loops that self-correct, verify their own output, stop on time, and run unattended — without burning tokens.
Why this instead of a traditional degree?
- You sit and watch the agent, re-prompting it every time it stalls
- It stops a step too early — or runs 40 steps past done, burning tokens
- When it goes wrong, you re-read the whole transcript guessing what happened
- It confidently returns broken work and calls it finished
- You can't leave it alone for five minutes, let alone overnight
- Design the loop once; it prompts itself until the job is actually done
- Termination logic that stops at the right step, every run
- The loop critiques and fixes its own mistakes before you ever see them
- It runs its own tests and refuses to exit on red
- Trust it to run unattended — overnight, to a verified result
What you'll learn
Construct and instrument a single-agent ReAct loop that iterates on a real coding task and exposes its per-step reasoning, actions, and cost.
Implement termination logic — max-steps, budget caps, confidence thresholds, fixed-point detection — that stops a loop at the right time.
Implement a self-correction sub-loop (generate → critique → revise) and judge when reflection helps versus wastes iterations.
Implement verification inside the loop so the agent validates its own output with tests, linters, and checks before it exits.
Distinguish loop failure modes — oscillation, thrashing, premature termination, semantic drift, hallucinated success — from step-level signals in a trace.
Examine a loop's step-level signals to determine whether it is converging, stalling, or drifting.
Justify a loop's cost–quality–iterations trade-off, deciding where to cut steps, switch model tiers, or early-exit.
Design and ship a production-grade, hardened autonomous loop that runs unattended to a verified result.
Curriculum
Orientation — From Prompt to Loop
Place loop engineering in the prompt → context → loop → harness progression, set up your loop lab in Claude Code, and watch your first loop iterate to a result on its own.
Orientation — From Prompt to Loop
Portfolio Deliverable: A configured loop lab and your first observed, instrumented loop run.
Start ModuleAnatomy of a Working Loop
Take the loop apart one iteration at a time — what the model sees each turn, what each turn costs, when extra iterations stop helping — and build a real, instrumented ReAct loop on a coding task.
Anatomy of a Working Loop
Portfolio Deliverable: A working, instrumented ReAct loop that fixes a real failing test and shows its per-step cost.
Start ModuleTermination & Convergence
Teach a loop when to stop. Hard stops (max steps, budget caps), soft stops (confidence thresholds, fixed-point detection), and the line between quitting early and running away.
Termination & Convergence
Portfolio Deliverable: Your loop with engineered termination that stops at the right step instead of the default.
Start ModuleReflection & Self-Correction
Build loops that catch and fix their own mistakes. Generate → critique → revise, internal versus tool-validated feedback, and the over-reflection trap where critique just burns iterations.
Reflection & Self-Correction
Portfolio Deliverable: Your loop with a self-correction sub-loop that detects and repairs its own errors in-trajectory.
Start ModuleVerification-in-the-Loop
Stop trusting the agent's word for it. Wire real verification — tests, linters, type-checks — into the loop so it validates its own output before it exits, with rollback when a check fails.
Verification-in-the-Loop
Portfolio Deliverable: Your loop with a verification gate that refuses to exit until its own checks pass green.
Start ModuleFailure Modes & Debugging
Name every way a loop breaks — oscillation, thrashing, drift, hallucinated success — instrument the loop with step-level signals, and learn to diagnose and repair each failure from its trace.
Failure Modes & Debugging
Portfolio Deliverable: A diagnostic report plus a loop hardened against the common failure modes.
Start ModuleCost & Control
Make cost a first-class design constraint. Early-exit strategies, iteration budgets, model-tier-per-step, and caching — cutting a loop's bill without losing the result.
Cost & Control
Portfolio Deliverable: Your loop re-tuned to hit the same result at a fraction of the token cost.
Start ModuleLoops That Run While You Sleep
Cross the autonomy bar. Checkpoints, resumption, and durable progress; composing termination, correction, verification, guards, and budgets into a loop you can trust unattended — and knowing when to hand off to a harness.
Loops That Run While You Sleep
Portfolio Deliverable: Your loop running unattended across interruptions with checkpointed, resumable progress.
Start ModuleCapstone — Engineer a Production-Grade Autonomous Loop
Design, build, instrument, and harden one real autonomous loop end-to-end — integrating termination, self-correction, verification, failure-guards, and cost control — and prove it runs unattended to a verified result.
Capstone — Engineer a Production-Grade Autonomous Loop
Portfolio Deliverable: A production-grade autonomous loop, a loop-design doc, and a trace proving an unattended, verified run.
Start ModuleYour AI Toolkit
Loop engineering is tool-agnostic, but you'll practice in a real coding agent. Everything here works in Claude Code; the patterns port to Cursor, the raw API, or your own harness.
Your main loop lab — run, instrument, and harden agentic loops against a real repo (Modules 0–8)
Claude Pro $20/mo, or API creditsDrive loops programmatically — control temperature, model tier, and per-step budgets
Pay-per-token (a few dollars covers the program)The codebase your loops act on and verify against with tests and linters
FreeRead the inside of a loop — per-step reasoning, actions, tokens, and drift
Free (built into Claude Code) or Langfuse free tierWrite a thin loop harness when you want full control over the cycle
FreeYou can complete the whole program on a $20/mo Claude Pro plan plus a few dollars of API credits for the programmatic exercises. No paid observability tooling required.
About this program
About This Degree
Every agent course teaches you how to start an agent. Almost none teach you how to make its loop trustworthy. That’s the gap. You’ve probably watched a coding agent stall halfway, declare victory on broken code, or churn for forty steps past the point it was done — and you’ve sat there re-prompting it by hand, because the loop wasn’t engineered to do better. The “loop” — the cycle where the agent acts, observes the result, decides what to do next, and repeats until the job is met — is the real unit of work in agentic AI, and it’s the one thing the courses skip. This program is about nothing else.
You build one loop, and it grows with you. It starts in Module 1 as a bare ReAct loop fixing a failing test. By Module 2 it stops at the right step instead of the default. In Module 3 it critiques and repairs its own mistakes; in Module 4 it runs your test suite and refuses to exit on red. Module 5 hardens it against the ways loops break — oscillation, thrashing, drift, hallucinated success — using signals you learn to read from the trace. Module 6 cuts its cost without losing the result, and Module 7 takes it across the autonomy bar so it runs unattended, checkpointed and resumable. The capstone is that loop, finished: production-grade, documented, and proven to run while you sleep.
What you become is a loop engineer — the person who can hand an agent a goal and actually trust it to finish. Not because the model got smarter, but because you engineered the cycle around it: where it stops, how it checks itself, what it does when a step fails, and what it costs. That’s the skill that compounds as models improve instead of being erased by them. By the end, “let the agent handle it overnight” stops being a gamble and becomes something you designed.
Prerequisites
This is an advanced, hands-on degree. It assumes you've already met ReAct, reflection, and tool use as concepts — these courses build that foundation so we can skip the basics and engineer the loop itself.
Establishes ReAct, reflection, and agent anatomy — the patterns this program engineers at depth instead of re-teaching.
Covers what agents are, tool use, and decision points — the foundation we assume from the first lesson.
Introduces the evaluator-optimizer pattern and quality gates — the seeds of self-correction and in-loop verification.
Frequently asked
Who is this program for?
Developers and technical builders who've already built an agent or two but whose loops are unreliable — they stop too early, run away, repeat mistakes, or can't be trusted unattended. If you can read code and run a coding agent, you're ready.
Do I need to write code?
Yes. This is a builder's degree. You'll run a coding agent (Claude Code) against a real repo every module, and write light Python when you want full control over the loop. It is hands-on from Module 1.
Will AI replace developers — or loop engineers?
The opposite pressure applies here. As models get stronger, the bottleneck moves from writing code to designing the loop that lets an agent work autonomously and correctly. Loop engineering is the skill that compounds as agents improve, not the one they erase.
What prerequisites do I need?
Three courses: AI Agents Deep Dive, Building AI Agents & Workflows, and Prompt Chaining & Workflows. Together they cover ReAct, reflection, tool use, and quality gates as concepts — this program assumes all of that and engineers it.
What do I get when I finish?
A portfolio-grade capstone: a production-grade autonomous loop for a real coding task, a loop-design doc, and a trace proving it ran unattended to a verified result — plus the Professional Certificate in Loop Engineering certificate.
How long does it take?
About four weeks at a steady pace — nine modules, 39 lessons, roughly 22 hours including the hands-on exercises. It's self-paced, so you can go faster or slower.
Is this program recognized by employers?
The certificate signals the skill; the capstone proves it. An autonomous loop that self-corrects, self-verifies, and runs unattended — with a design doc explaining your choices — is a concrete artifact you can show in an interview or PR review.
Do I need a specific tool?
We teach in Claude Code because it's the cleanest place to run and inspect real agentic loops, but loop engineering is tool-agnostic. Every pattern ports to Cursor, the raw API, or a harness you write yourself.
What AI tools will I use?
Claude Code as your loop lab, a model API (Claude, GPT, or Gemini) for the programmatic exercises, a scratch Git repo with a test suite, and a trace view to read the inside of your loops.
How is this different from the prerequisite courses?
The courses teach you that ReAct, reflection, and quality gates exist and how to use them once. This program engineers them: when a loop diverges, how to make it converge, how to verify mid-loop, how to diagnose failure from a trace, and how to run it unattended. Less than 10% overlap by design.
How is this different from the AI Agent Harness and AI Agent Builder programs?
Loop Engineering is about what happens inside one agent's iterate-until-done cycle — how it thinks, checks, corrects, and stops. Agent Harness is about the system around the loop (frameworks, multi-agent topology, evaluation infrastructure). Agent Builder is about running fleets of agents safely in an organization. They're complementary; this is the deepest dive on the loop itself.
Is loop engineering only for coding agents?
The loop is the unit, and coding agents are where it's sharpest because the agent can run tests to verify its own work. But the principles — termination, self-correction, verification, failure-diagnosis, cost control — apply to research agents, data-processing agents, and any autonomous loop.
