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ForgeAcademy · Advanced Tier

Your team already pays for Copilot.
Now make it show up in every sprint.

A 12-session course on running Agile ceremonies with AI — built by a working Senior Scrum Master in healthcare, not by a consultant who read the docs.

$39 $79
Founding price · Ships October 15, 2026 · Full refund anytime, no questions asked
Reserve my spot — $39
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Who this is for

You've used ChatGPT. Maybe you've asked Copilot to summarize a meeting. It worked, sort of, and then you went back to doing it by hand.

This course is for Scrum Masters, RTEs, and delivery leads who want the next thing: not better prompts, but repeatable machinery — the sprint report that builds itself every Friday, the refinement prep that takes ten minutes instead of an hour, the agent that drafts your retro questions from what actually happened.

It is not for you if you're brand new to AI. Start with the free AI for Scrum Masters course instead — it's free, it always will be, and this course assumes it.

What makes this "Advanced"

Most course ladders are a scam: the advanced tier is the beginner tier with longer prompts.

Here's the actual difference. Beginner teaches you to do one task with AI. Advanced teaches you to build systems your team runs on — and, just as importantly, to know when not to.

Three things you won't find in the free tier:

1. Workflows, not prompts. Chained, repeatable pipelines across Jira, Copilot and Rovo. You finish each session with an artifact you keep and reuse, not a chat transcript.

2. Agents. The fastest-moving thing in this space and the biggest gap in the free catalogue. What an agent actually is, how to build a scoped one for Agile work, what's already sitting inside the enterprise licence your company pays for — and where they fail.

3. Judgment. How to tell when the output is confidently wrong. What to never delegate. How to defend an AI-assisted artifact when a stakeholder, an auditor, or a skeptical engineer asks who wrote it. Written from healthcare, where that question has teeth.

The 12 sessions

Part One — Systems, Not Prompts
  1. From Prompts to Pipelines
  2. Your Ceremony Operating System
  3. The Friday Machine — sprint reporting end to end
  4. Backlog Hygiene at Scale
Part Two — Agents
  1. What an Agent Actually Is
  2. Your First Agile Agent
  3. Agents Inside Your Stack — Copilot, Rovo, Jira automation
  4. Chaining It Together — intake → backlog → report
  5. When Agents Go WrongFREE PREVIEW BELOW
Part Three — Judgment & Defensibility
  1. Metrics That Don't Lie
  2. Defending AI-Assisted Work
  3. Rolling It Out to a Team That Didn't Ask For It

Roughly 20–25 minutes per session. Self-paced, no cohort, no deadlines. Certificate on completion.

Coming later: an Expert tier on leading AI enablement across multiple teams. Founding buyers get the founding price on that too, whenever it ships.

The free preview

I'm asking you to pay for something that doesn't exist yet. So here's a whole session, free, right now — session 9, the one about everything going wrong.

I picked that one deliberately. It's the least flattering session in the course. If you're going to trust me with $39, you should see the part where I tell you what doesn't work.
9
When Agents Go Wrong⚠️ Guardrails
Judgment · The failure modes nobody demos
Free preview
🏆
Your win this session
You'll be able to spot the three ways agents fail in Agile work — and you'll leave with a review step that actually survives a busy sprint, instead of 'be careful' as a principle.
🔄
Every vendor demo shows you the agent succeeding. None of them show you the sprint report that read beautifully and quietly dropped the one blocker that mattered. That's the session. If you only take one thing from this course, take this one — it's the difference between using agents and being used by them.
What this session covers
  • The real failure mode: not being wrong, but being wrong fluently — output that reads like a competent colleague wrote it
  • Failure 1, omission: the summary that's accurate about everything it mentions and silent about what mattered most
  • Failure 2, fabrication: agents inventing tickets, releases and names when the data doesn't contain what you asked for
  • Failure 3, stale confidence: answers drawn from a view of the board that was true on Tuesday
  • Why 'tell me what you're unsure about' doesn't work — self-reported confidence doesn't track actual error
  • Four prompt-level guardrails: scope hard, cite ticket IDs, declare absence, flag inference vs. read
  • Blast radius: sorting your workflows by what a wrong answer actually costs
  • Review steps that survive a busy sprint — and why the diligent ones never do

The demo problem

Every agent demo you have ever seen was a success case. The agent reads the board, writes the summary, everyone nods. That's not dishonest — it's just not the part you need to prepare for.

Here's the part you need to prepare for. An agent writes your sprint summary. It's well structured. The tone is right. Every sentence in it is true. And it doesn't mention that the integration work is blocked, because the blocker was recorded as a comment on a subtask rather than as a status change, and the agent didn't look there.

You skim it. It reads fine. You send it to your stakeholders.

Nothing about that output looks wrong, because nothing in it is wrong. That's the failure mode. Not error — omission, delivered fluently.

Three ways it actually breaks

Omission. The most dangerous and the least discussed. Wrong sentences get caught; missing ones don't. Nobody proofreads for absence. And the things most likely to go missing are exactly the things recorded off the happy path — a blocker in a comment, a dependency in a linked ticket, a concern someone raised verbally in standup.

Fabrication. Ask for something the data doesn't contain and you may get it anyway, complete with plausible names and dates. Practitioners in the Atlassian community have reported agents producing entirely fictional releases when the underlying data had nothing to offer. The invented material is not flagged as invented. It looks exactly like the real material.

Stale confidence. The agent answers from whatever slice of the board it pulled, with no sense that the world moved. In a live sprint, "as of some point earlier today" and "now" are different answers, and only one of them is useful in a stakeholder meeting.

One Rising Star in the Atlassian community described this class of tool as being like an intern — brilliant and high-energy, "terrible at taking final responsibility." That's the most accurate summary of the current state of things I've read.

Why asking it doesn't help

The instinct is to add "tell me what you're unsure about" to the prompt. It feels responsible. It doesn't work — the model's self-reported uncertainty doesn't reliably track where it's actually wrong. You get a confident answer with a confident-sounding caveat attached, and you've learned nothing.

You cannot ask the thing that was confidently wrong to notice it was confidently wrong. Verification has to come from outside the output.

Four guardrails that do work

These don't make the agent right. They make checking cheap, which is the thing that determines whether you actually check.

1. Scope hard. Current sprint only. This board. This label. A narrow question has a narrower space to be wrong in.

2. Make it cite. Every claim tagged with the ticket ID it came from. This is the highest-leverage one — it turns verification from "reread everything and hope" into "click three links." Anything that can't be sourced becomes visible immediately.

3. Make it declare absence. "List anything you looked for and could not find." This is the direct counter to the omission failure, and almost nobody does it.

4. Make it flag inference. "Mark anything you inferred rather than read." Separates what's in Jira from what merely sounded plausible.

Four guardrails that make checking cheap Four steps applied to a prompt: scope hard, so there is less ground to be wrong on; make it cite, so verification is clicking three links; make it declare absence, which is the direct counter to omission; make it flag inference, separating what is in Jira from what merely sounded plausible. They don't make the agent right. They make checking cheap. 1 Scope hard Current sprint. This board. Less ground to be wrong on. 2 Make it cite Every claim tagged with its ticket ID. Verify = click 3 links. 3 Declare absence “What did you look for and not find?” Counters omission. 4 Flag inference Marks what it guessed versus what it read straight from Jira. You cannot ask the thing that was confidently wrong to notice it was confidently wrong. Verification has to come from outside the output.

Blast radius: stop reviewing everything

"Review all AI output" is a policy that lasts about three sprints. Then a release goes sideways, everyone's busy, and it quietly stops. Reviewing everything is how you end up reviewing nothing.

Sort by what a wrong answer costs instead:

  • Cheap to be wrong — internal drafts, first-pass refinement notes, discussion prompts. Ship it, glance at it, move on.
  • Expensive to be wrong — anything leaving your team, anything a stakeholder plans against, anything touching governance, compliance, or an audit trail. Always human eyes.

A retro summary that's slightly off wastes five minutes. A capacity forecast that's confidently wrong plans your next sprint into a wall.

Blast radius: sorting AI work by what a wrong answer costs A single axis from cheap to expensive. Cheap: internal drafts, first-pass refinement notes, discussion prompts — ship it, glance at it, move on. Expensive: anything leaving your team, anything a stakeholder plans against, anything touching governance, compliance or an audit trail — always human eyes. Sort by what a wrong answer costs — not by how much output there is CHEAP TO BE WRONG • Internal drafts • First-pass refinement notes • Discussion prompts Ship it. Glance at it. Move on. EXPENSIVE TO BE WRONG • Anything leaving your team • Anything a stakeholder plans against • Governance, compliance, audit trail Always human eyes. cost of a wrong answer a retro summary wastes five minutes · a capacity forecast plans your sprint into a wall
"Review all AI output" lasts about three sprints. Reviewing everything is how you end up reviewing nothing.

Review steps that actually survive

Three properties separate the review habits that last from the ones that die in March.

Structural, not diligent. If it depends on remembering to be careful, it decays. If it's a step in something that already happens, it survives.

Attached to an existing ceremony. The sprint report gets checked in the five minutes before review, because that slot already exists and has a forcing function. A standalone "AI review checkpoint" on your calendar will be gone by the third sprint.

Author separated from verifier. Whoever ran the prompt is the worst person to catch its errors — they already believe it. Where the stakes justify it, someone else looks.

What to never delegate

Short list, and it's short on purpose.

Anything you'd have to defend under scrutiny without being able to say where the number came from. Anything about a specific person — performance, capability, blame. Anything where the failure is discovered by someone other than you, months later.

The test isn't "can AI do this?" It's the one put well in that same community thread: the real shift is from can it do this to how would I know it did it well? If you can't answer the second question, you're not ready to delegate the first.
⚡ Try it with your team
Take a sprint summary an AI has already written for you — or generate one now from your current board. Then run the omission check below against it. You're not looking for wrong sentences. You're looking for the thing that isn't there.
"Here is a sprint summary you generated: [paste it]. Now do three things. First, list every claim in it and the specific ticket ID that supports it — mark any claim you can't source. Second, list anything you looked for in the data and could not find. Third, list anything you inferred rather than read directly. Do not rewrite the summary."
Check yourself
1. What makes agent output in Agile work most dangerous?
2. Why doesn't 'tell me what you're unsure about' work as a safeguard?
3. Which guardrail directly counters the omission failure mode?
4. What makes a review step survive a busy sprint?

Prefer to read it on its own, or send it to someone? This session has its own page →

Why me

I'm a SAFe Senior Scrum Master, currently contracting at a large healthcare organization. I've spent the last year making Copilot and Rovo genuinely useful in real ceremonies — and finding out where they aren't.

I also built the 21 free courses on this site. They're free, and they'll stay free. This is the first thing I've ever charged for.

I'm not an AI expert. I'm a practitioner who does this job every week and writes down what works.

Honest FAQ

Why should I pay when your other 21 courses are free?

The free courses are broad — one course per role, aimed at getting anyone started. This one goes deep in a single direction and takes twelve sessions to do it. If the free Scrum Masters course didn't earn your trust, don't buy this one.

What if it's not ready on October 15?

You get an email telling you the truth and a refund if you want one. I'd rather tell you it slipped than ship something thin.

What if I don't like it?

Full refund, any time, no questions. Email me. You don't have to explain.

Do I need Copilot or Rovo to take this?

It helps, and the examples use them because that's what I use. The methods work with Claude, ChatGPT, or Gemini — the workflows are the point, not the vendor.

Is this SAFe-specific?

No. SAFe context shows up where it's relevant, but the workflows are Scrum-first and apply to any team running sprints.

Will there be a certificate?

Yes, distinct from the free ones.

$39 founding price. $79 when it ships October 15.
Full refund anytime, no questions asked.
Reserve my spot — $39
Not ready? Read session 9 free — it's the honest one.