Pathway · a route through lessons that already exist
AI Engineering: After the Demo Works
The part of an AI engineering roadmap that begins once the demo works: everything between a demo that impresses and a system other people depend on, ending in a release somebody signs and an action somebody authorized. It teaches no Python and no API integration, and says so first. Every lesson already ships inside live trainings in the Meridian sequence; this is the route through them.
- 1 The exits before the build
- 2 Retrieval as a contract, then grounding as a claim
- 3 Name the system before you judge it
- 4 Evaluate on your data, behind a gate
- 5 The pager and the record
- 6 Only now, let it act
6 stages · 8 whole modules · 32 existing lessons · ~9.5 hours · Engineers who can already build an AI demo and now have to make it something other people depend on
Every lesson on this route is free to read and needs no account. The only budget is the hours above.
Nothing in this route is new. Every lesson below already ships inside 4 live trainings in the Meridian sequence, and every link goes to the lesson where it already lives; no URL moves for this page. This route takes 8 of the 24 modules in those trainings, 32 of their 90 lessons. It is a sampler with an argument, not a substitute: each training's own page has the rest, and the boundary section at the end says what was left out and why.
- Grounded Answers From Documents 3 of 6 modules · 12 of 22 lessons
- AI Assurance: System Risk and Release Decisions 2 of 6 modules · 8 of 23 lessons
- Operating in Production: On-Call, Incident Command, and Reporting Clocks 2 of 6 modules · 8 of 23 lessons
- Guarded Automation: Agents That Run Operations 1 of 6 modules · 4 of 22 lessons
What this route does not teach — and nothing in this catalog does
A roadmap you found elsewhere probably starts with these. They are stated here first so you can decide in the first minute whether this is the route you were looking for.
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Python, or any programming language.
This catalog teaches you to direct AI command-line tools, which is a different skill from writing the code yourself. If you also write code, nothing below assumes you do not: the stages are about what has to surround the code, in any language. A roadmap that starts with Python is not wrong; it is answering a different question, and nothing here answers that one.
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Calling a model from your own code — SDKs, structured outputs, function calling, token budgets.
No training here teaches it, and this route will not pretend otherwise. What it does teach is everything that has to be true around that code before anyone depends on it.
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MLOps as the term is usually used — training pipelines, feature stores, model serving, and which vector database to pick.
The flagship course, AI-Powered Development, says it in its first lesson: it never trains a model. The retrieval lessons in this route are about the contract — a question goes in, authorized passages with openable provenance come back — which outlives whichever index you choose. Module 1, lesson 1: which kind of AI work this is
Before you start
You drive an AI command-line tool already — installed, authenticated, used on real work. Nothing in this route teaches the tools themselves.
That floor is Module 6: CLI Tools Setup — 5 lessons, 70 minutes, and the link opens its first one. Every one of the 4 trainings this route walks lists it first among its recommended preparation, with the same note on each: “Every lesson drives an AI CLI - install one first”. It is not counted in the 32 above, because it is the floor this route stands on rather than part of it. Skip it if you already run one of those tools on real work and can authenticate it from a fresh shell without looking anything up.
The route
6 stages in dependency order: the question before the build, the proof before the claim, the register before the evaluation, the pager before the agent. Every stop is a whole module — you cannot take half of one here, because the lessons inside each assume the ones before them. Each stage names the test for skipping it: skip only if every line is true of you.
If your roadmap budgeted one "supporting skills" stage, this is what that stage decomposes into. Where a stage's training lists an earlier stage's training as what it builds on, the catalog's own field is printed under the stage, so the order is something you can check rather than take on trust.
- Stage 01
The exits before the build
1 module · 4 lessons · 71 minA roadmap's retrieval stage starts with a build. This one starts with four named ways to answer the question that need no build at all, and what it costs to take each. If the question survives that table, you write the evaluation questions — and name who knows the right answers — before anything retrieves.
Skip this stage if all of these are true- you have written down the questions your system must answer and the ones it must refuse
- you have named who scores them and what the pass bar is
- you wrote all of that before the first retrieval ran
- Decide the Answering Method Module 1 of 6 in Grounded Answers From Documents · all 4 lessons · 71 min
A bounded class of questions, not a chatbot
Why here First because everything after it is judged against the question set this module makes you write — and because its exits can end the route here, honestly, before any build.
- Stage 02
Retrieval as a contract, then grounding as a claim
2 modules · 8 lessons · 152 minA question goes in; authorized passages with openable provenance come back — that contract outlives whichever index you pick. Then the part the demos skip: a citation proves where text came from, never that it supports the claim.
Skip this stage if all of these are true- your retriever enforces permissions where the evidence is fetched
- retrieval is evaluated on its own before any answer exists
- you sample-check that each cited passage actually contains the claim
- Retrieve the Evidence Module 3 of 6 in Grounded Answers From Documents · all 4 lessons · 76 min
Find it, or say so
Why here Retrieval comes before proof because the hand check in the next module needs passages with provenance to check against — and permissions have to be enforced here, or the proof is of the wrong corpus.
- Prove the Answer Module 4 of 6 in Grounded Answers From Documents · all 4 lessons · 76 min
Support is not truth
Why here Placed before any evaluation of the whole system: until you can tell a supported answer from a merely cited one, an evaluation score is measuring the wrong thing.
- Stage 03
Name the system before you judge it
1 module · 4 lessons · 75 minEvaluation cannot authorize a system nobody has named. The register, the consequence tier — how much harm a wrong output can do, and so how hard the system must be tested — that a second reviewer must be able to reproduce, and what a release decision actually is. The tier is also what sizes every test in the next stage.
In the catalog, AI Assurance: System Risk and Release Decisions lists Grounded Answers From Documents under "builds on".
Skip this stage if all of these are true- every AI system you run has a named owner and a version boundary
- each one has a consequence tier a second reviewer could reproduce
- a named signer was assigned before testing began
- List What You Run Module 1 of 6 in AI Assurance: System Risk and Release Decisions · all 4 lessons · 75 min
You cannot assure what you cannot list
Why here Before evaluation because the tier this module assigns is what sizes every test in the next stage — and because a system nobody has registered cannot be authorized by any score.
- Stage 04
Evaluate on your data, behind a gate
1 module · 4 lessons · 81 minThe supporting skill every roadmap lists and few walk. Evaluation is chosen by system type — a classifier needs a threshold and slices, a generator needs planted facts and refusals, an agent needs trajectories — run on a set the provider has never seen, by a gate that exits non-zero.
In the catalog, AI Assurance: System Risk and Release Decisions lists Grounded Answers From Documents under "builds on".
Skip this stage if all of these are true- your evaluation runs as a gate that can fail a release
- it runs on data your provider has never seen
- the method was chosen by system type, not by whatever the demo happened to produce
- Evaluate on Your Terms Module 3 of 6 in AI Assurance: System Risk and Release Decisions · all 4 lessons · 81 min
Your data, your gate
Why here Here because it consumes both earlier stages — the frozen question set from the first and the tier from the third — and produces the gate the pager stage assumes already exists.
- Stage 05
The pager and the record
2 modules · 8 lessons · 131 minThe gap a roadmap's supporting-skills bullet hides: deployment, monitoring, on-call. Not tooling — the human system around a running service. Declare at a low threshold, decide honestly whether you can staff the response, keep one record several different readers can use, and page from a burn rate — how fast the service is spending the errors its service level allows — that has a unit test rather than from a chart.
In the catalog, Operating in Production: On-Call, Incident Command, and Reporting Clocks lists AI Assurance: System Risk and Release Decisions under "builds on".
Skip this stage if all of these are true- your service has a declared incident threshold
- it has a rota (the on-call schedule) someone actually signed up for
- there is one record per incident that every reader writes into
- alerts derive from a service level, not from a CPU graph
- The System Around the Service Module 1 of 6 in Operating in Production: On-Call, Incident Command, and Reporting Clocks · all 4 lessons · 62 min
Built before the first page
Why here Before the page because declaring, staffing, and the one record are what a page is a claim on; without them a burn-rate alert wakes someone with nowhere to write.
- The Page Module 2 of 6 in Operating in Production: On-Call, Incident Command, and Reporting Clocks · all 4 lessons · 69 min
A claim on someone's night
Why here Last of the human-system modules because its burn-rate policy needs the service level the previous module made you state — and because this telemetry is what the agent stage requires to exist first.
- Stage 06
Only now, let it act
1 module · 4 lessons · 60 minThe roadmap shape this route answers — agents as one stage, the supporting skills as the stage after — is reversed here, and the catalog says why in a field printed under this stage: Guarded Automation lists Operating in Production as what it builds on, because an agent cannot safely run an operation whose telemetry and recovery were never established by people first. What this stage adds is the distinction the field keeps blurring: the envelope — the limits an action is pre-approved to run inside — can stand, but the authorization cannot.
In the catalog, Guarded Automation: Agents That Run Operations lists Operating in Production: On-Call, Incident Command, and Reporting Clocks and AI Assurance: System Risk and Release Decisions under "builds on".
Skip this stage if all of these are true- every action your agent can take is classified by impact and reversibility
- that classification is signed by a named owner
- every action leaves a receipt in a store the agent cannot write to
- One Action, One Moment Module 1 of 6 in Guarded Automation: Agents That Run Operations · all 4 lessons · 60 min
Classify before the first run
Why here Last on purpose: its action register assumes the register, the gate, and the pager above already exist. The catalog encodes that order in the builds-on field printed under this stage.
What this route does not cover
This route walks the opening of each of Grounded Answers From Documents, AI Assurance: System Risk and Release Decisions, Operating in Production: On-Call, Incident Command, and Reporting Clocks and Guarded Automation: Agents That Run Operations, never the whole. What it leaves out is owned, and each owner is one click away:
- The rest of the retrieval build — which documents go in and which never do, the extraction step where real corpora die, permissions as corpus design, the answer record and the drift triggers that re-open it. This route walks the opening, the retrieval, and the proof; the corpus and the record stay with the training. Grounded Answers From Documents →
- The organization-level decision. You evaluate; somebody signs. The attack-evidence plan and the attempt curve, the agent deployment addendum, the signed release record that expires, and what renews or withdraws it. AI Assurance: System Risk and Release Decisions →
- Incident command, the reporting clocks that each start at their own recorded trigger, and the post-incident review that changes something. Operating in Production: On-Call, Incident Command, and Reporting Clocks →
- The envelope itself — where a policy is actually enforced, approvals that expire and bind to one request, a kill switch outside the agent's reach, and the hash-chained receipt store. Guarded Automation: Agents That Run Operations →
- The agent as a named identity with an owning team, scopes, expiry, just-in-time elevation, and a queryable audit trail. Zero Trust Implementation →
- Working the agent itself: context engineering, tool servers, skills and hooks, subagents, headless runs in CI. That route already exists and shares no lesson with this one. Agentic Engineering for Software Teams →
Engineers who can already build an AI demo and now have to make it something other people depend on