by Stefan Wolpers|FeaturedAgile 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!
by Stefan Wolpers|FeaturedAgile and ScrumAgile Transition
TL; DR: AI Thinking Skills for Agile Practitioners
Most agile practitioners use AI to produce outputs more quickly. Few use it to think better. This free download gives you three AI thinking skills (Socratic Explorer, Brutal Critic, Pre-Mortem) that turn Claude into a partner for diagnosing problems, stress-testing plans, and anticipating failures before they happen.
by Stefan Wolpers|FeaturedAgile 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.
by Stefan Wolpers|FeaturedAgile and ScrumAgile Transition
TL; DR: Claude Cowork
AI agents have long promised productivity gains, but until now, they demanded coding skills that most agile practitioners lack or are uncomfortable with. In this article, I share my first impressions on how Claude Cowork removes that barrier, why it is a watershed moment, and how you could integrate AI Agents into your work as an agile practitioner.
by Stefan Wolpers|FeaturedAgile 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.
by Stefan Wolpers|FeaturedAgile 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.
TL; DR: Bridging Agile and AI with Proper Prompt Engineering
Agile teams have always sought ways to work smarter without compromising their principles. Many have begun experimenting with new technologies, frameworks, or practices to enhance their way of working. Still, they often struggle to get relevant, actionable results that address their specific challenges. Regarding generative AI, there is a better way for agile practitioners than reinventing the wheel team by team—the Agile Prompt Engineering Framework.
Learn why it solves the challenge: a structured approach to prompting AI models designed specifically for agile practitioners who want to leverage this technology as a powerful ally in their journey.
TL; DR: The Scrum Master Interview Guide to Identify Genuine Scrum Masters
In this comprehensive Scrum Master Interview guide, we delve into 83 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.
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.
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.
TL; DR: Vibe Coding Best Practices — Food for Agile Thought #538
Welcome to the 538th edition of the Food for Agile Thought newsletter, shared with 35,647 peers. This week, Teresa Torres shows non-engineers how to embrace vibe coding best practices, for example, by upfront Markdown planning and a separate review agent, while Ethan Mollick identifies interface design, not model capability, as AI’s real bottleneck. Lenny Rachitsky interviews Simon Willison on why November 2025 marks AI’s inflection point and why mid-career engineers face the greatest displacement risk. David Pereira and Jason Knight, Janna Bastow, and Saeed Khan all warn that product managers lean on AI to ship faster while abandoning discovery and strategy, and Maarten Dalmijn reminds us that deliberately chosen constraints are what make great products possible.
Next, Martin Eriksson warns that strategy drift is silent, with executives believing they are 82% aligned, while actual alignment sits at just 23%, and Kyle Poyar adds that AI agents are becoming B2B buyers who will actively disqualify products with opaque pricing and “contact sales” CTAs. Viktor Cessan argues that LLMs are exposing large codebases and headcounts as liabilities. Also, Jack Dorsey, Roelof Botha, and Joost Minnaar each propose that AI makes traditional hierarchies obsolete in favor of small, autonomous, radically transparent teams. Jeff Gothelf rounds things out by offering a four-dimensional rubric to make product judgment visible and teachable as AI collapses execution costs.
Lastly, Nate Herk extracts eight practical insights from the leaked Claude Code source code, from hidden slash commands to multi-agent architecture, and Sachin Rekhi outlines ten AI-powered discovery workflows that accelerate customer research without replacing human judgment. Janelle Teng sees Physical AI approaching an inflection point as six catalysts converge, and Tristan Kromer frames the gap between AI deployment and business impact as a practice problem, mapping six maturity levels from passive consumer to multi-agent architect. Finally, Tracy St.Dic shows how Zapier has already operationalized that shift by raising its hiring bar to require embedded AI use and provable adoption leadership.
Welcome to the Sign-up Page of the ‘AI4Agile Foundational Assessment’
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!
TL; DR: AutoResearch in Your Sleep — Food for Agile Thought #537
Welcome to the 537th edition of the Food for Agile Thought newsletter, shared with 35,652 peers. This week, Andrej Karpathy and Aakash Gupta explore how AI agents are reshaping digital work through autonomous multi-agent workflows and autoresearch loops that run 100 automated improvement cycles overnight. Shifting to team dynamics, Christina Wodtke sees the friction between frontline teams and management as a perspective problem across abstraction levels, while Stephanie Leue warns that polite CPO-CTO misalignment costs far more than the honest conversation both parties avoid. Maarten Dalmijn adds that autonomy without alignment creates silos, not freedom, and Jerry Neumann challenges the entire startup methodology canon, proposing that widely adopted frameworks like Lean Startup become self-defeating the moment everyone uses them.
Next, Sachin Rekhi maps out 15 AI prototyping skills product managers need to shift how teams prioritize roadmaps, and Tim O’Reilly warns the agentic economy still lacks the infrastructure to prevent single-gatekeeper capture. Anthropic researchers Massenkoff, Lyubich, and McCrory find that experienced Claude users tackle harder tasks with higher success rates as usage diversifies. Also, Margaret-Anne Storey identifies cognitive and intent debt as two underappreciated costs of AI-generated code, and Ruben Hassid demonstrates Claude’s new computer use feature for autonomous multi-step Mac workflows.
Lastly, Paweł Huryn documents 74 Claude releases in 52 days, signaling a widening competitive gap. At the same time, Ian Vanagas shares PostHog’s hard-won lessons on when product context beats flashy agent capabilities. Tristan Kromer warns that synthetic personas sharpen interview guides but cannot replace real customer discovery, and Bandan Singh proposes letting direct reports lead 1:1s before managers add their topics. Finally, Allan Kelly believes Agile’s decline stems from the community’s own retreat from in-person learning.
Jira was named after Godzilla and built to track bugs. It became the default agile tool because it satisfied a deeply human desire: controlling work by putting it in boxes with statuses, assignees, and due dates. That system works for humans scanning dashboards. It does not work for autonomous agents that need to reason about patterns across iterations, detect recurring problems, and forecast what is likely to break next. This article argues that the tool on which 62% of agile teams rely is about to be demoted from knowledge authority to execution interface. We need to move from Jira to AI Agents.
TL; DR: POM Starter Pack — Food for Agile Thought #536
Welcome to the 536th edition of the Food for Agile Thought newsletter, shared with 35,661 peers. This week, Anthropic’s 81,000-person study reveals that hope and alarm about AI coexist within the same individual. Alberto Romero channels that tension into eight practical strategies for AI career anxiety, while Allan Kelly warns that today’s AI hype mirrors the 1990s BPR failures. On the product side, Teresa Torres walks teams through measuring real customer impact rather than shipping features, Janna Bastow proposes that fixing bugs and technical debt is the strategy, and the Dotwork team provides a POM starter pack to operationalize Marty Cagan’s Product Operating Model.
Next, David Pereira suggests that product leadership means creating space for product managers to thrive, not being the smartest person in the room. Steve Blank warns that startups older than 2 years are likely running obsolete playbooks in a world reshaped by AI agents and vibe coding. Also, Ruben Dominguez highlights Claude’s 14x revenue jump and proposes that the real productivity gap lies in learning to co-work with AI. Cedric Chin recommends ignoring AI predictions and studying actual field reports instead, while Dave Snowden reminds us that Boyd’s OODA loop was never meant to be a safe iteration cycle.
Then, Jeff Gothelf proposes that storytelling is now the product manager’s key competitive advantage as AI commoditizes standard PM artifacts. Tristan Kromer addresses the lack of memory in AI agents. He proposes building a RAG-based experiment knowledge base to compound learning rather than repeat it. Martin Eriksson adds that AI agents need the same strategic clarity as human teams or organizations will scale confusion at machine speed. Finally, Sharyph explains how Claude Code Skills 2.0 turns Claude into a personalized, testable workflow system, while Deloitte’s 2026 State of AI report finds that only 34% of organizations truly reimagine their business with AI despite rising access.
