AI Agent Engineering Loops, Graphs & Production Safety

Design agent loops, multi-agent graphs, verification gates, budgets and guardrails that survive production. Hands-on.
What you'll learn

---

Design single-agent loops with deterministic stopping conditions, so a run halts on real evidence instead of a model claiming it is finished.
Enforce token and dollar budgets at a gateway your agents cannot bypass, terminating in a spend cap that lives outside your own code.
Run long unattended agent tasks with fresh-context loops that avoid context decay and cost an order of magnitude less than one long session.
Architect multi-agent graphs with isolated contexts, single-writer ownership, and typed handoffs the receiver verifies rather than trusts.
Requirements

---

Comfortable reading and writing code in any language. Examples use TypeScript, Python and Bash, and every snippet is explained line by line.
Access to at least one AI coding agent CLI (Claude Code, Codex, Cursor, Goose or similar). Everything taught is tool-agnostic by design.
Description

---

This course contains the use of artificial intelligence.Most AI agent courses teach you how to make an agent do something. This one teaches you how to stop it.
That difference is the whole course. Getting an agent to write code was solved a while ago. What is not solved - what breaks teams, budgets and production systems - is everything that happens when the agent is wrong, and nobody notices because the tests are green.
This is a practitioner course for engineers moving from chat-based prompting to autonomous, long-running agent systems. It covers three paradigms in depth
Spec-driven development - making intent the source of truth, with atomic Requirements

---

and traceability that fails your build when something goes unverified
Loop engineering - single-agent control cycles with heartbeats, durable state, and stopping conditions that terminate on evidence rather than on a model's opinion
Graph engineering - multi-agent organisations with isolated contexts, capability grants, typed handoffs and convergence at graph scope
Then it does the part most material skips entirely: production, governance and adversarial safety. Cost controls enforced outside your own process. Trajectory tracing and statistical evaluation. Human approval placed by reversibility rather than by cost. Instruction bleed between prompt modules. Reward hacking. Cross-vendor verification.
What makes this course different
Every lesson ends with a test you run against your own system, not a multiple-choice quiz. By the end you will have measured, on your own build
your checker's catch rate against planted defects, and its false-positive rate
whether your loop actually survives kill -9 mid-run
whether an agent can bypass your gateway and reach the model provider directly
the correlation between your two reviewers, and what a second opinion is really buying you
the cheapest path to making each of your gates pass, and whether that is the intended path
your blast radius, as a sentence you can complete and controls you have verified
Twenty-five hands-on drills in total, collected into a verification dossier you can put in front of a security reviewer or a hiring manager.
Structure
Six modules, twenty-one lessons, and a capstone in which you build a complete loop operating system - specification, planning, bounded execution, gateway budgets, human sign-off - and score it against a six-dimension rubric that grades evidence rather than features.
Every lesson includes a video explainer, an audio podcast version, full written notes, slides, an infographic, and a mind map. Module quizzes and a downloadable knowledge base are included.
One continuous worked example runs through the whole course: a fintech ledger and payouts service. Every artefact you build compounds into the next module, and all of it composes into the capstone.
Tool-agnostic by design
Nothing here is a walkthrough of one vendor's product. The patterns work with Claude Code, Codex, Cursor, Goose or a harness you write yourself, and the course is explicit about which details are version-specific and will drift.
What this course refuses to do
It will not tell you AI is magic, that agents replace engineers, or that a green checkmark means anything on its own. It assumes these systems are high-leverage and fallible, that they optimise against whatever you measure, and that your job is to build controls that hold when they do.
If you have ever asked "what actually stops this thing?" and found that nobody could answer - this course is the answer, and the measurement to prove it.
Who this course is for

---

Software engineers who already use AI coding assistants and now want to build systems that run unattended without breaking things.
AI engineers and platform engineers building multi-agent systems, orchestration layers, or an internal agent platform for their org.
Homepage

---