Cohort 1 — Limited to 10 seats
You've shipped the model. Ran some prompts. It looked good in the demo. Now it's in production — and you have no idea if it's actually working. That's the Eval Trap. Here's the way out.
A financial services firm deployed an AI document processing agent. Thousands of hours of engineering. Board-level buy-in. Global rollout planned.
Six months in, a compliance audit found the agent had been silently misclassifying a category of regulatory documents — not every time, just often enough to matter. The remediation cost: $25 million. The lesson: they were testing for what the model said. Not for what it was supposed to do.
They had no evals. They had vibes.
Of agentic AI initiatives will fail to meet their objectives by 2027 — due to inadequate testing and governance.
— Gartner Research, 2024
The cost of doing nothing
Every week you ship AI without evals is a week you're accumulating silent technical debt in the one place you can't see it: the model's behavior in production. By the time it surfaces, the damage is already done.
Traditional software is deterministic. Input A always produces Output B. Agentic AI is probabilistic. Input A might produce B, or C, or D — depending on context, temperature, retrieval quality, and a dozen other variables you don't control. The operating model that worked for the old world doesn't work here.
Product Owner
Before a line of code is written, the PO defines what "good" means — quantifiably. The eval is not a QA artifact. It is the product spec. If you can't write it on Day 1, you can't build it.
Phase: Ideation
Enterprise Architect
The eval becomes automated auditing that runs before every deployment. No model goes to production without clearing its behavioral thresholds. Compliance is built in, not bolted on.
Phase: Architecture / Design
Engineering Leader
In production, 1-2% of live traffic is continuously graded against your Golden Dataset. The eval is a CI/CD pipeline for intelligence — catching drift before it becomes a $25M incident.
Phase: Build / Deploy / Operate
Each week is 3–4 hours of live instruction, a hands-on code lab, and one deliverable you keep. By Week 6 you have a complete Eval Strategy Charter — the governance document that answers every question your board will ask.
Why deterministic software intuitions are actively dangerous in agentic systems. The shift from "did it run?" to "did it behave?" The AI Triumvirate operating model.
Deliverable → AI Ambition Statement
Building a Behavioral Scorecard before writing a prompt. The Day One Eval Worksheet. How to construct a Golden Dataset from real production logs — not hypotheticals.
Deliverable → Behavioral Scorecard + Golden Dataset (50 examples)
Evaluating multi-step agents, RAG systems, and tool-calling trajectories. The Testing Pyramid. LLM-as-a-Judge — calibration, pitfalls, and when to trust it.
Deliverable → Agentic Reference Architecture diagram
Wiring evals into CI/CD. Online evals: sampling 1-2% of live traffic. pass@k vs pass^k — the innovation metric vs. the trust metric. Circuit Breaker Protocol.
Deliverable → Continuous Intelligence Pipeline spec
Structuring the Strategy Realization Office. EU AI Act compliance by design. AI FinOps: managing your Intelligence Budget. Vendor selection framework.
Deliverable → Governance Guardrails Charter
Scaling eval culture across teams. The SRO as organizational design. The Evaluation Paradox — cognitive conditions for high-quality human review. Making AI trust a board-level metric.
Deliverable → Eval Strategy Charter (board-ready)
This is not a course for people who want to learn Python. It's for the three roles that determine whether your organization's AI investments succeed or fail — and who are currently operating without a shared framework.
Enterprise Architect
You're designing the systems. You need a governance model that doesn't collapse under regulatory scrutiny or production load.
Product Owner
You're defining requirements for AI products with no prior playbook. You need to write specs that the model can actually be held to.
Engineering VP
You're accountable for delivery. You need a CI/CD framework that catches model failure before it becomes an incident report.
This is NOT for you if:
Every lesson, every template, every worksheet in this course was produced by a seven-agent AI team — a Product Manager, Writer, Editor, Marketer, SEO Agent, Sales Agent, and Operations Agent — all coordinated through Claude.
We documented the entire build process on YouTube. You can watch the evals fail in real time. You can watch us iterate. The course isn't just about eval engineering — it's a live demonstration of it.
Watch the Build Series →Lead Magnet
Chapter 1 of the book — "You're Shipping AI Blind" — plus a 5-email course on why traditional testing fails in probabilistic systems.
The Book
The complete 100-page practitioner guide. Hero's Journey structure. Every framework, template, and checklist — no course required.
Premium Cohort
The full 6-week live cohort. You build the complete Eval Strategy Charter. 10 seats per cohort.
→ Founders pricing may still be available — book a call and ask.
Limited to 10 seats per cohort
If you can't complete the Day One Eval Worksheet after Week 1 — or if the framework doesn't apply to your specific AI context — tell us within 7 days of the first session. Full refund. No questions. No hoops.
We're confident enough in the framework to put money on it.
This course was designed for the space between product and engineering — not for pure developers. If you can read a CI/CD pipeline diagram and write a user story, you're technical enough. The code labs are optional but designed for engineers who want to go deeper. PMs and architects get everything they need without writing a line.
That's exactly why you need this now. The teams who are ahead of this problem built the framework before they deployed, not after. You have a six-month window before your organization ships something it can't govern. Use it.
Unit tests verify deterministic behavior: does the function return the right value? Eval engineering measures probabilistic behavior: does the AI act the way it was specified to act, across thousands of real-world inputs, continuously? Testing is a snapshot. Evals are a signal. You need both.
One agentic AI incident costs orders of magnitude more. The $25M case study in this course is real. At $1,999 (or $999 for founders), you're buying the operating model that prevents it. If you're already running AI in production, you're already exposed. The question is whether you're prepared.
The commitment is 3–4 hours per week. One live session plus one code lab and one deliverable. Everything is recorded. If you can't find 3 hours a week to build the governance framework for your most strategically important technology category, that's a prioritization problem worth examining.
Both — but the frame is leadership. We cover the code because leaders need to understand what they're governing. But the deliverables are designed for VP presentations, board decks, and architecture reviews. The Eval Strategy Charter is a document you take to a business case, not a GitHub repo.
Cohort 1 — 10 seats total
Every week your team ships AI without evals is a week you're accumulating invisible risk. Cohort 1 is limited to 10 people. When those seats are gone, the founders rate goes with them.