AI tools are reshaping how Scrum Teams work, and Scrum Masters who cannot coach their teams through this shift are not ready for 2026. This article presents ten Scrum Master interview questions that test whether a candidate can facilitate AI adoption without losing self-management. As usual, each question includes guidance on answers and red flags. The questions are drawn from the seventh edition of the 97 Scrum Master Interview Questions guide.
Most micromanagement is not a control problem; it is a clarity failure in disguise. This article introduces Commander’s Intent: a five-part briefing model that replaces prescriptive instructions with shared purpose, hard constraints, and room to adapt.
Bonus: As a Claude user, you can download the Commander’s Intent1 Skill.
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!
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.
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.
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.
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.
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?
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.
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.
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.
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.
Welcome to the 539th edition of the Food for Agile Thought newsletter, shared with 35,623 peers. This week, Ronan Farrow and Andrew Marantz investigate Sam Altman’s leadership at OpenAI, exposing a pattern of deception and safety trade-offs, while Ajeya Cotra predicts AI research parity could arrive by 2028, and Annie Vella reports from a retreat where nobody had AI figured out yet. On the product side, Stephanie Leue questions why leaders borrow other people’s styles instead of finding their own, and Janna Bastow warns that backlog-driven roadmaps create an illusion of progress. Also, Maarten Dalmijn proposes that fewer rules and better guardrails unlock real team performance.
Next, Packy McCormick warns against lazily comparing AI labs to Amazon, since these companies face direct competitors running nearly identical strategies. Tomasz Tunguz offers a 2×2 matrix that helps leaders sort AI strategy work by demand ceiling and loop type, and Itamar Gilad explores why product discovery keeps breaking down internally and proposes that teams time-box it but never skip it. On the technical side, Zvi Mowshowitz covers Anthropic’s Claude Mythos model, which found critical security vulnerabilities across every major OS and browser. Paweł Huryn breaks down Claude Code pricing after Anthropic cut third-party access, and Gergely Orosz hosts Kent Beck and Martin Fowler as they trace parallels between past tech disruptions and today’s AI shift, including why TDD matters more now than ever.
Lastly, John Cutler suggests that AI won’t fix broken product funnels but will amplify whatever dynamics already exist, and Jeff Gothelf warns that SAFe’s rigid ceremonies become catastrophic because AI development demands rapid hypothesis testing. Yoni Rechtman envisions AI-native companies organizing around four archetypes instead of traditional roles, and Kyle Poyar identifies five storytelling archetypes that cut through AI-generated noise. Finally, Jenny Wanger reminds us that offshore team frustrations run both ways and that trust, not process documentation, is the actual fix.
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.
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!
Take the AI4Agile Foundational Assessment now:
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