83% of Agile practitioners use AI, but most spend 10% or less of their time with it because they do not know where it fits. Our survey of 289 Agile practitioners identifies the real adoption barriers and shows where AI creates value you can act on. Learn more by downloading the free AI4Agile Practitioners Report 2026.
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?
The Mastering AI with the AI 4 Agile Online Course launches this week, and I am proud that I avoided another delay. Scope creep happened despite my supposed expertise in preventing exactly that. The course expanded from a simple prompt collection to over 8 hours of video, custom GPTs, and materials that I’ll apparently continue to update indefinitely, as I’m still not satisfied that it’s comprehensive enough. (Also, the field is advancing so rapidly.)
At least the $129 lifetime access means you will benefit from my urge to fight my imposter syndrome with perfectionism and from my inability to call a project “done.” I guess we are in for the long term. 🙂
🎓 🛒 The AI 4 Agile Online Course Is Available — Join Now at $ 129 until October 20, 2025: AI 4 Agile — Master AI Integration for Agile Practitioners.
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.
ChatGPT can be an excellent tool for those who know how to create prompts. The simplest form of prompting ChatGPT is to feed it the task and ask for results. However, this approach is unlikely to trigger the best response from the model.
Instead, invest more time in prompt engineering, and provide ChatGPT with a better context of the situation, desired outcomes, data, constraints, etc. The following article offers a primer to creating ChatGPT prompts for Scrum practitioners to get you started running. You will learn:
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.
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 531st edition of the Food for Agile Thought newsletter, shared with 35,736 peers. This week, Aruna Ranganathan and Xingqi Maggie Ye found that AI intensifies work rather than reduces it, and Siddhant Khare describes how they paradoxically increase engineer exhaustion. Cleo Lant explores how product velocity overwhelms user adoption capacity, while Maarten Dalmijn argues that PUSH planning systems fail for complex work. Teresa Torres explains context rot in AI models, while Paweł Huryn shares architectural lessons from building Agent One as a secure alternative to OpenClaw.
Next, Jim Highsmith reflects on Agile’s 25 years, noting it won by reshaping delivery but lost by hardening into a set of ceremonies. David Pereira interviews John Cutler on product operating models and messy transformations, and Aakash Gupta and Caitlin Sullivan demonstrate AI discovery workflows that compress 10+ hours into 30 minutes. Also, Grant Harvey argues that AI collapsed execution, making taste and judgment critical. Azeem Azhar and Nathan Warren conclude that AI faces a capacity stampede as compute demand outpaces infrastructure.
Then, Reddit user morsofer describes how a new board dismantled 10 years of Agile transformation in 6 months. Michael Lopp examines three bad but successful managers and why adapting your approach matters. Additionally, Jenny Wanger explores how AI’s variable response latency fragments attention and breaks flow, and Andi Roberts shares mechanics for creating living team charters through observable behaviors. Finally, the DORA AI Capabilities Model identifies seven capabilities that amplify AI benefits.
Welcome to the 530th edition of the Food for Agile Thought newsletter, shared with 35,729 peers. This week, Dwarkesh Patel and John Collison press Elon Musk on orbital data centers, power limits, space solar for cheaper AI within three years, while Itamar Gilad urges experiments without hype, since discovery, constraints, maintenance, and outcomes still rule. Roman Pichler outlines a product operating model with long-lived products and empowered teams. Also, Deb Liu shares charts on adoption, costs, and uneven gains, and Tom Geraghty ties status to busyness rather than impact.
Next, Stephanie Leue shows how a “clear” strategy still burns teams out when priorities multiply, and she urges explicit trade-offs, surfaced hidden work, and ranked outcomes with visible de-scoping. Aakash Gupta describes Mike Bal’s AI native PM system using Cursor or Claude Desktop, MCP tools, and vetted research, and Arvind Narayanan challenges Moravec’s Paradox and calls for a diffusion-minded policy. Additionally, Benedict Brady automates AI-generated feedback into PRs, and Teresa Torres and Petra Wille reframe hiring as discovery.
Then, Scott Alexander reports on Moltbook’s first weekend, asking whether AI posts cause real effects, then maps influencers, spam, crypto manipulation, micro religions, builders, and fragile self-moderation. Greg Satell debunks change myths and urges committed minorities plus resistance planning, while Maarten Dalmijn warns that post-failure rules kill competence and trust. Also, Jayshree Seth and Amy C. Edmondson frame AI adoption as team learning with reviews and overrides. Lastly, Victor Yocco refocuses UX on trust, consent, and accountability.
AI initiatives fail for the same reasons Agile transformations did: The majority of failures result from people, culture, and processes, not technology. This article gives you a diagnostic checklist of 10 AI transformation anti-patterns to spot where your organization’s initiatives are coming off track.
Welcome to the 529th edition of the Food for Agile Thought newsletter, shared with 35,753 peers. This week, Jing Hu and Klaas Ardinois unpack OpenClaw/Clawdbot and the real tradeoffs and risks of always-on self-hosted agents, while Stephanie Leue shows how AI exposes broken product operating models and why builder teams beat bolt-on AI. Maarten Dalmijn reframes roadmaps as a choice between Red predictability and Blue adaptability, and Dario Amodei sketches near-term AI risks and safeguards. John Cutler calls out metrics theater and pushes outcome signals.
Next, Ant Murphy suggests product-tech teams can drop roles like BAs and Scrum Masters by pulling engineers into discovery, reducing dependencies, and shipping small batches with decoupled deploy and release. Wes Bush frames product-led growth as table stakes for AI software, with fast time-to-value, agents as users, and per-task pricing. Zvi Mowshowitz reviews Claude’s Constitution and its values-first stance, and Ethan Mollick treats management as the most critical AI skill. Also, Casey Newton repeats a crucial truth: AI creates work slop, so measure outcomes.
Then, Federico Viticci shows OpenClaw/Clawdbot, an LLM-based agent on a Mac mini that chats via Telegram, stores Markdown memory, adds MCP skills, and runs shell tasks, while raising app store policy questions. Mike Fisher links speed to focus, trust, and psychological safety, not pressure; Sean Goedecke treats estimates as political and replaces dates with options and risks, and Aakash Gupta, interviewing Sachin Rekhi, pushes AI prototyping to validate problem solution pairs fast. Lastly, Kieran Klaassen suggests that AI coding fails when planning disappears.
The paradigm shift is here. Andrej Karpathy, former Tesla AI director and OpenAI co-founder, recently admitted he has never felt this far behind as a programmer. If Karpathy feels overwhelmed, how should the rest of us feel?
This article maps the shift across three levels: strategic, product, and individual. Each level demands different responses, while “good enough Agile” no longer provides an income or perspective. The question is where you are on the journey.
