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How the A3 Delegation System Helps to Avoid AI Debt Borrowing from Agile Artifacts

by Stefan Wolpers | 2026-07-26 Agile and Scrum

TL; DR: Reusing Agile Artifacts to Avoid Accumulating AI Debt

In this video from the 76th Hands-on Agile Meetup, I walk you through the A3 Delegation System and show how it helps avoid AI debt by borrowing artifacts and practices from Agile, such as the Definition of Done and Retrospectives. If you’d like to download the corresponding canvases (the artifacts of the A3 Delegation System), you can do so below.

You will get a full set of PDFs, along with the guide to the A3 Delegation System, so that you can run the system with your own teams. Enjoy the video and let me know whether you consider the A3 Delegation System useful.

How the A3 Delegation System Helps to Avoid AI Debt Borrowing from Agile Artifact - Age-of-Product.com

📺 Watch the video now: How the A3 Delegation System Helps to Avoid AI Debt — Hands-on Agile Meetup 76.

A3 Delegation System Founding Workshop — September 28-29, 2026

by Stefan Wolpers | 2026-07-22 Agile and Scrum

Turn An AI Workflow Into an Explicit Delegation

Your team already delegates work to AI: reports, research, customer feedback analysis, stakeholder communication, or parts of operational workflows.

But can you answer these questions without improvising?

What may AI decide, and what must remain a human decision?
What does “good enough” mean for this particular work?
Who verifies the result before somebody acts on it?
Who checks whether the delegation still works after the model or workflow changes?

If those answers live in one person’s head, or nowhere, your problem is no longer prompting. You have a delegation problem.

The A3 Delegation System gives you a practical way to decide what AI may do, hand over the work clearly, define acceptable results, and inspect the delegation over time.

During two hands-on sessions, you will apply the system to a workflow. You will leave with a clear understanding of how to apply the A3 Delegation System to your workflows so that team members or stakeholders can understand, challenge, and continue your AI delegation work. Everything you learn is directly applicable to your situation the next day. The class is in English.

A3 Delegation System Founding Workshop — September 28-29, 2026 — by Stefan Wolpers Berlin-Product-People.com

From Prompting to Operating Capability: The AI4Agile Online Course Release v3

by Stefan Wolpers | 2026-07-19 Agile and Scrum

TL; DR: AI4Agile v3 Is Live

You learned to prompt, and your organization learned to spend. Unfortunately, few organizations have learned to connect the two. That is where the AI4Agile online course comes in.

An AI operating capability exists when delegated work can be reproduced without its original creator, meets an explicit quality standard, follows a defensible execution path, has a named owner, and is inspected often enough to detect drift. The AI4Agile Online Course V3 teaches practitioners how to build one, independent of a particular AI model, and still, there is no coding required. The course is in English. 🇬🇧

📺 Watch me walk you through the AI4Agile online course, pointing to the new modules.

AI4Agile Course v3: Master AI for Agile Practitioners — Berlin-Product-People.com

👉 AI4Agile V3 launches today, July 20, 2026, at the introductory price of $149, including a 14-Day Money Back Guarantee. (On July 28, it will be $249.)

You Already Have an AI Working Agreement. Write It Down.

by Stefan Wolpers | 2026-07-12 Agile and Scrum

TL;DR: The AI Working Agreement

Your team already has rules for using AI. Some live in templates, some in habits, exceptions, and one person’s memory. The AI Working Agreement puts the decisions that matter in one place: what the team delegates to AI, what stays human, what must be reviewed, what never enters a model, who owns which workflow, and how the agreement changes. Write it, and a new colleague can read your team’s AI decisions on their first day, while the decisions stay when someone leaves.

You Already Have an AI Working Agreement. Write It Down to Turn Scattered AI Decisions into an Inspectable Artifact - Age-of-Product.com

Thesis: Team-level AI governance fails more from uncodified judgment than from missing policies. The AI Working Agreement turns scattered AI decisions into one inspectable artifact, so a team can onboard people, survive departures, and challenge its own habits before those habits harden into risk.

If You Can Write Acceptance Criteria, You Can Write an AI Routing Policy

by Stefan Wolpers | 2026-07-05 Agile and Scrum

TL;DR: The AI Routing Policy

You moved your routine AI work to a cheaper model, so you think the cost question is handled; however, often, that is not the case. The decision lives in one person’s head and produces nothing that the person accountable for the invoices can read. Worse, it is an architectural choice nobody documented. The AI Routing Policy is the missing artifact of Stage 2 of the Delegation Lifecycle: it records which execution path, from a cheaper model to a frontier model to plain code, handles each class of work, what counts as good enough output to meet the AI Definition of Done, and who owns the call. The skill it needs to work is one you already have: You write acceptance criteria.

If You Can Write Acceptance Criteria, You Can Write an AI Routing Policy — The AI Delegation Lifecycle by Age-of-Product.com

Thesis: An AI routing policy is not about picking a cheaper model at the moment of executing an AI task. It is a written, repeatable team decision that assigns each task class to the cheapest sufficient execution path: a model, human review, deterministic code, or no automation. Paired with a minimal routing log, it creates the spend-by-task-class record that your finance team will eventually request. You can draft the first three lines in twenty minutes.

If You Can Facilitate a Retrospective, You Can Audit Your AI

by Stefan Wolpers | 2026-06-28 Agile and Scrum

TL;DR: The AI Delegation Audit

Scrum teams inspect how the last Sprint went during the Retrospective. They are much less likely to inspect the work they have handed to AI, because no meeting on the calendar owns it. That gap is where a working AI automation quietly turns into risk: it keeps producing fluent, on-brand output long after the decision to trust it has expired. The AI Delegation Audit closes the gap by leveraging the facilitation skills teams already use in a Retrospective.

If You Can Facilitate a Retrospective, You Can Run the AI Delegation Audit of the A3 Framework - Age-of-Product.com

Thesis: The Delegation Audit is the missing inspection cadence for delegated AI work. It checks four things: whether the work still meets the standard, whether the model still fits the task, whether the team can still stop the automation, and whether reviewed assistance has quietly become unreviewed automation. You can try it on one workflow in fifteen minutes.