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

by Stefan Wolpers | 2026-07-05 Featured 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.

No More Cheap Claude: Four First Principles of Token Economics in 2026

by Stefan Wolpers | 2026-04-19 Featured Agile and Scrum

TL;DR: Token Economics in the Era of Scarcity

Your Claude Pro subscription hits limits faster than it did in January, as Anthropic quietly re-priced the ceiling, and every AI provider is rationing compute. If you keep working with Claude the way you did six months ago, you are in for a rude awakening. This article gives you four principles that explain how Token Economics actually works, so you can stop accepting the black box and start using your budget deliberately.

No More Cheap Claude: Four First Principles of Token Economics in 2026, Separating Professionals from Amateurs - Age-of-Product.com

The AI4Agile Foundational Assessment: A Free Practical Judgment Benchmark for Agile Practitioners

by Stefan Wolpers | 2026-03-28 Featured Agile and ScrumAgile Transition

Using AI at Work Does Not Mean You Understand It

Many agile practitioners use ChatGPT at work. That does not mean they understand AI well enough to trust your own judgment. The problem is not that agile practitioners ignore AI. The problem is that many already use it confidently without knowing where their judgment breaks down. The free AI4Agile Foundational Assessment measures precisely this skill gap. (Download your access file below.)

The assessment comprises 40 scenario-based questions. It does not ask for definitions, but puts you into situations that agile coaches, product managers, and Scrum Masters face every week: weak prompting producing generic output, misleading data analysis, questionable agent output, and, possibly, organizational pressure to treat AI output as “good enough” to go with it.

Most people who use AI do not fail because they lack knowledge, but because they cannot distinguish between plausible outputs and trustworthy judgment. But see for yourself!

AI4Agile Foundational Assessment: A Free Practical Judgment Benchmark for Agile Practitioners - Age-of-Product.com

The A3 Handoff Canvas: Six Questions That Turn AI Delegation Into a Repeatable Workflow

by Stefan Wolpers | 2026-02-15 Featured Agile and ScrumAgile Transition

TL;DR: The A3 Handoff Canvas

The A3 Framework helps you decide whether AI should touch a task (Assist, Automate, Avoid). The A3 Handoff Canvas covers what teams often skip: how to run the handoff without losing quality or accountability. It is a six-part workflow contract for recurring AI use: task splitting, inputs, outputs, validation, failure response, and record-keeping. If you cannot write one part down, that is where errors and excuses will enter.

The Handoff Canvas closes a gap in a useful pattern: from an unstructured prompt to applying the A3 framework to document decisions with the A3 Handoff Canvas, to creating transferable Skills, potentially leading to building agents.

The A3 Handoff Canvas: Six Questions That Turn AI Delegation with the A3 Framework Into a Repeatable Workflow — Age-of-Product.com

The A3 Framework: Assist, Automate, Avoid — A Decision System for AI Delegation

by Stefan Wolpers | 2026-01-11 Featured Agile and ScrumAgile Transition

TL; DR: The A3 Framework

The A3 Framework categorizes AI delegation before you prompt: Assist (AI drafts, you actively review and decide), Automate (AI executes under explicit rules and audit cadences), or Avoid (stays entirely human when failure would damage trust or relationships). Most AI training teaches better prompting. The A3 Framework teaches the prior question: Should you be prompting at all? Categorize first, then prompt.

The A3 Framework: Assist, Automate, Avoid — A Decision System for AI Delegation to Preserve Professionalism — Age-of-Product.com

Agile Is Dead, Long Live Agility

by Stefan Wolpers | 2025-12-07 Featured Agile and ScrumAgile Transition

TL; DR: Why the Brand Failed While the Ideas Won

Your LinkedIn feed is full of it: Agile is dead. They’re right. And, at the same time, they’re entirely wrong.

The word is dead. The brand is almost toxic in many circles; check the usual subreddits. But the principles? They’re spreading faster than ever. They just dropped the name that became synonymous with consultants, certifications, transformation failures, and the enforcement of rituals.

You all know organizations that loudly rejected “Agile” and now quietly practice its core ideas more effectively than any companies running certified transformation programs. The brand failed. The ideas won.

So why are we still fighting about the label?

Free Ebook: 97 Scrum Master Interview Questions to Identify Suitable Candidates

by Stefan Wolpers | 2022-06-17 Featured Agile and Scrum

TL; DR: The Scrum Master Interview Guide to Identify Genuine Scrum Masters

In this comprehensive Scrum Master Interview guide, we delve into 97 critical questions that can help distinguish genuine Scrum Masters from pretenders during interviews. We designed this selection to evaluate the candidates’ theoretical knowledge, practical experience, and ability to apply general Scrum and “Agile “principles effectively in real-world scenarios—as outlined in the Scrum Guide or the Agile Manifesto. Ideal for hiring managers, HR professionals, and future Scrum teammates, this guide provides a toolkit to ensure that your next Scrum Master hire is truly qualified, enhancing your team’s agility and productivity.

If you are a Scrum Master currently looking for a new position, please check out the “Preparing for Your Scrum Master Interview as a Candidate” section below.

So far, this Scrum Master interview guide has been downloaded more than 25,000 times.

Scrum Master Interview — How to Prepare Yourself to Stand Out — Age-of-Product.com

Hiring: 82 Scrum Product Owner Interview Questions to Avoid Agile Imposters

by Stefan Wolpers | 2021-09-26 Featured Agile and Scrum

TL; DR: 82 Product Owner Interview Questions to Avoid Imposters

If you are looking to fill a position for a Product Owner in your organization, you may find the following 82 interview questions useful to identify the right candidate. They are derived from my sixteen years of practical experience with XP and Scrum, serving both as Product Owner and Scrum Master and interviewing dozens of Product Owner candidates on behalf of my clients.

So far, this Product Owner interview guide has been downloaded more than 10,000 times.

82 Product Owner Interview Questions to Avoid Imposters — Age-of-Product.com

📅 Upcoming Scrum Training Classes, Liberating Structures Workshops, and Events

by Stefan Wolpers | 2020-10-10 Featured Workshops

TL; DR: Scrum Training Classes, Liberating Structures Workshops, and Events

Age-of-Product.com’s parent company — Berlin Product People GmbH — offers Scrum training classes authorized by Scrum.org, Liberating Structures workshops, and hybrid training of Professional Scrum and Liberating Structures. The training classes are offered both in English and German.

Check out the upcoming timetable of training classes, workshops, meetups, and other events below and join your peers.

Upcoming Scrum and Liberating Stuctures training classes and workshops — Berlin Product People GmbH

Food for Agile Thought 551: AI Confidence Theater, GitHub for PMs, Product Alignments, We Tried Agile; Didn’t Work

by Stefan Wolpers | 2026-07-03 News

TL; DR: AI Confidence Theater — Food for Agile Thought #551

Welcome to the 551st edition of the Food for Agile Thought newsletter, shared with 35,473 peers. This week, Elena Verna calls out AI confidence theater and asks teams to show real workflows, which pairs well with Teresa Torres and Petra Wille’s advice to start AI adoption with one messy to-do item. Also, Janna Bastow brings the same discipline to alignment meetings: clarify decisions before vague input becomes commitment. Anthropic frames Fable 5’s return as governance, while Alberto Romero questions the safety bargain, and Mike Cohn redirects failed Agile blame toward broken conditions.

Next, Aakash Gupta and Shubham Saboo treat PM work like code, while Hamel Husain extends that discipline to AI evaluation: track changes, show provenance, and make review paths obvious, and Tomasz Tunguz adds the cost pressure that will force selective adoption. Joost Minnaar reminds teams that rituals without shared power rot into theater, and Anthropic frames Claude Fable 5 as a teammate needing clearer boundaries.

Lastly, Ethan Mollick sees AI work shifting toward agent management, while Peter Yang expects model portfolios to reshape software economics. Charity Majors argues that leaders must support learning rather than demand unpaid adaptation, and Gergely Orosz reminds us that reinvention beats nostalgia. Finally, Abraham Thomas ties lasting progress to data quality that delivers real business outcomes by matching fitness for purpose with measurable value rather than relying solely on checklists.

Food for Agile Thought 551: AI Confidence Theater, GitHub for PMs, Product Alignments, We Tried Agile; Didn’t Work - Age-of-Product.com

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.

Food for Agile Thought 550: Make AI Boring, Everyone’s a Product Manager Soon, Fixing Procrastination, Agentic “Team” Topologies

by Stefan Wolpers | 2026-06-26 News

TL; DR: Make AI Boring — Food for Agile Thought #550

Welcome to the 550th edition of the Food for Agile Thought newsletter, shared with 35,481 peers. This week, Charity Majors rejects AI purity theater and urges disciplined workplace experiments, just make AI boring again, while Gojko Adzic warns that faster builders without product judgment will ship polished waste. Dave Hora names the organizational traps that keep teams from seeing reality, and Johanna Rothman brings the fix down to flow data and human judgment. Azeem Azhar and colleagues see AI demand rising, but Satya Nadella argues that a durable advantage comes from owning learning itself.

Next, Paweł Huryn moves AI work from prompt craft to agent loops with goals, guardrails, budgets, and independent checks, while Jeff Gothelf argues that AI pilots fail when firms bolt tools onto stale workflows. Joe Hudson adds that emotional clarity now beats knowledge hoarding, and John Cutler names fear, incentives, and executive fantasies as the real bottlenecks. David Burkus brings the pattern back to procrastination, where stress and ambiguity demand clarity without control.

Lastly, Elena Verna pushes experimentation beyond tiny UI tweaks toward larger monetization bets and longer engagement signals, as Zvi Mowshowitz warns AI policy needs calibrated safeguards rather than theater. Deborah Rim Moiso brings the same discipline to facilitation through communities that review real work, and Olivier Wulveryck applies Team Topologies to agentic platforms before shadow IT hardens. Finally, Itamar Gilad grounds the pattern in value, not misleading productivity counts.

Food for Agile Thought 550: Everyone’s a PM, Make AI Boring, Fixing Procrastination, Agentic "Team" Topologies - Age-of-Product.com

The AI Definition of Done: Human in the Loop Is Not a Quality Standard

by Stefan Wolpers | 2026-06-21 Agile and ScrumAgile Transition

TL;DR: The AI Definition of Done

Your team has a Definition of Done for a product increment. It has none for the 20-plus AI-supported outputs that leave the team each week: status reports, stakeholder emails, release notes, and updates for the C-level. Each one carries your team’s name. “I know quality when I see it” is the standard most teams actually run by, and you cannot audit it, teach it to a new colleague, or defend it when a claim turns out to be wrong. The AI Definition of Done fixes that with one page per task class, agreed by the team, before the output ships.

The AI Definition of Done: The Human in the Loop Is Not a Quality Standard; Check out the new template — Age-of-Product.com

Food for Agile Thought 549: AI in Product 2026, Makers Manifesto, AI POM, Open Knowledge Format

by Stefan Wolpers | 2026-06-19 News

TL; DR: AI in Product 2026 — Food for Agile Thought #549

Welcome to the 549th edition of the Food for Agile Thought newsletter, shared with 35,498 peers. This week, Product Circle and Product Institute share the AI in Product 2026 survey show AI coding tools spreading faster than stronger operating models, while Elena Verna sees cheaper software creation opening a Mom-and-Pop SaaS lane for domain experts. Petra Wille counters AI possibilities with accountable product principles, and Sam McVeety and Amir Hormati tackle agent-ready context. Also, Isabel Juniewicz and Ed Zitron question whether increasing hyperscaler spending and the economics of generative AI can sustain the rush, or bubble?

Next, Janna Bastow warns that Slack loses product feedback once channels move on, and Sarah Guo argues that AI shifts durable advantage toward private data, judgment, and trust. Aakash Gupta and Rohan Varma push the logic further, describing AI-native teams that build before they coordinate as the AI way, while Matthew Hodgson adds that enterprises need persistent funding and governance to make AI product operating models work. Then, Gregor Ojstersek shows that top engineering teams are already reshaping structures around AI.

Lastly, Mark Graban warns that tone policing in teams drives bad news underground, while Barry O’Reilly argues that AI raises the premium on visible, codified judgment that requires transparency, not enforced harmony. Johanna Rothman and Sonya Siderova shift the focus from faster tasks to slower systems, where wait times and flow debt shape delivery. Finally, Matteo Tittarelli extends that logic to GTM, where context, skills, orchestration, and integrations must compound across cycles.