AI Degree 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?
Prompting your agent by hand
- 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
AI Degree in Loop Engineering
- 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.
- Why Loops Are the New Unit of Work
- Your Loop Lab: Setting Up Claude Code
- First Win — Watch a Loop Iterate
Anatomy 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.
- The Four Beats: Act, Observe, Decide, Repeat
- Build Your First Real Loop
- Cost Per Iteration: What Each Turn Really Costs
- Semantic Saturation: When More Iterations Stop Helping
- Reading a Trace Like an Engineer
Termination & 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.
- The Stopping Problem
- Hard Stops: Max Steps and Budget Caps
- Soft Stops: Confidence and Fixed-Point Detection
- Engineer Termination Into Your Loop
- Cumulative Review: Everything Through Termination
Reflection & 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.
- Loops That Critique Themselves
- Internal vs Tool-Validated Feedback
- The Over-Reflection Trap
- Build a Self-Correction Sub-Loop
- Module Synthesis: Reflection That Pays Off
Verification-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.
- Trust, but Verify Each Step
- Real Gates: Tests, Linters, Types
- Deterministic vs Stochastic Control
- Rollback and Confidence Scoring
- Build a Self-Verifying Loop
Failure 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.
- The Loop Failure Taxonomy
- Loop Instrumentation: Seeing Inside
- Diagnosing Failures From Signals
- Break It On Purpose, Then Fix It
- Cumulative Review: Reflection, Verification, Failure
Cost & 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.
- The Real Price of a Loop
- Early-Exit and Iteration Budgets
- Model-Tier-Per-Step and Caching
- Tune the Cost–Quality Trade-off
Loops 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.
- The Autonomy Bar
- Checkpoints, Resumption, and Durable Progress
- Compose the Trustworthy Unattended Loop
- When to Hand Off to a Harness
Capstone — 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.
- The Brief and Your Loop Design Doc
- Build, Instrument, Harden
- Prove It Runs Unattended
AI Degree in Loop Engineering
Proves you can engineer a single agent's autonomous loop end-to-end — termination, self-correction, verification, failure-diagnosis, cost control, and unattended operation — and ship one that runs to a verified result.
Your 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.
You can complete the whole degree 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 Degree
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 degree 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.
