Governing agent actions
Turn one tool-using agent action into a reviewable decision: role, resource, scope, confirmation, evidence, and an explicit return path.
Explore the courseAn AI product can give a polished answer and still be wrong, unsafe, inconsistent, or unsupported; an AI agent can take the wrong action. Learn to identify the failure, choose a relevant check, read conflicting results, and explain why a release should proceed or stop.
One Lumina course organizes a repeatable evaluation process into eight tracks: the Full Judge-Driven Harness. No programming is required for the core learning path. CORE is the anchor: you first practise with cases and release decisions. Seven applied tracks then use the same method for systems and products that answer, retrieve, generate, or act. If you want to implement it, optional Python labs show how a judge — a check with an explicit criterion — and a harness — the repeatable process that runs those checks — work as code.
Course hero under final review
AI-generated fictional presenter
The anchor course teaches the shared method. Each applied track uses it for a different AI system or product: one that answers, retrieves, generates, takes actions, or supports a decision. Start at the anchor, then choose the application closest to your work.
Three things no marketplace course bundles together.
Every claim traces to published, public research — OWASP LLM & Agentic Top 10, MITRE ATLAS, RAGAS, MT-Bench, DORA, NIST AI 600-1, C2PA — cited and credited, never hand-waved.
The 58 lessons are paired with 57 Python labs that run offline, stdlib-only, with the same output every time. Not slides — code you run, then point at your own system.
Every lab metric — a numeric result from an illustrative synthetic example — is labelled. We teach you to measure your own system, never claim that ours “hits X%.”
Start with the Harness anchor, then add the application track that matches your work — or take the whole Harness.
The domain-agnostic spine: discover failures, write judges as code, validate them against a compact labeled set, gate, retry, calibrate, wire into CI.
Start from trace logs (OpenTelemetry): inspect retrieved sources and answers with the RAG triad, keep a reference set, then use reproducible CI checks. A Pareto frontier makes the cost–quality trade-off visible instead of hiding it in one score.
Claude Code in production: AGENTS.md as contract, specialized subagents, hardening & determinism, red-team your own agents, judge-driven review — governance, not a feature tour.
Turn prompt-injection risks into repeatable adversarial tests, then into detection rules for a SIEM: the system that centralizes logs and raises alerts. OWASP and MITRE ATLAS provide the public risk vocabulary.
Validate your own product: run it as the eval, orthogonal quality dimensions, calibrated gates, the living question bank, and dogfooding wired into CI — ship on evidence, not "looks fine."
Goal-directed synthetic-user simulation (an NPC is a simulated user with a goal), multi-turn exploration, and the honest breadth/mass validity boundary: personas widen the behavior space, but do not replace calibration with real users.
Build an evaluation bank from real failures: cover important cases, remove duplicates, version the set, and retain regressions. κ agreement is the check that independent reviewers label the same case similarly.
Make the quality, cost, and latency trade-off visible. Compare latency percentiles rather than only averages, route simple requests economically, and use a Pareto frontier to show which cost–quality options are genuinely different.
This course is outside the eight-track Harness offer. Its public preview opens the course map and GOV.1; enrollment and checkout are not available yet.
Turn one tool-using agent action into a reviewable decision: role, resource, scope, confirmation, evidence, and an explicit return path.
Explore the courseThe prices below describe three planned offers: the anchor method, the complete program, and a later anchor-plus-track bundle. Offers are localized in BRL for Brazil and USD internationally. Enrollment remains unavailable until the Hotmart purchase and delivery journey passes preflight.
Study time is not published as a promise yet. We will state a range only after observing beginner sessions, rather than guessing from the author’s pace.
This route shows the international USD offer. The checkout total is authoritative; local taxes, payment-method costs, or conversion into another currency may change the amount shown before confirmation. The Level-0 primer remains free.
The AI Coding OS pack → is a separate companion toolkit with 18 install-ready skills — not a sixth course or a ninth Harness track.
.zip with an index.html launcher.
Open it in any browser, read on any device, and run the labs on a desktop. The course itself does not phone home;
package updates remain available in the purchase library for 12 months.