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.
Welcome to the 484th edition of the Food for Agile Thought newsletter, shared with 42,673 peers. This week, Ethan Mollick examines vibe coding, where AI-native teams blend human expertise and AI to iterate and collaborate rapidly. Maarten Dalmijn critiques rigid planning, advocating for elastic teams that thrive in complexity. In anticipating engineering’s shift toward AI management roles, Jasper Gilley quit his FAANG job, seeing automation redefine technical careers. Michael Küsters likens middle management to Rock-Paper-Scissors, where unpredictability is key to success. At the same time, Fred Hebert dissects AI integration, emphasizing thoughtful human-in-the-loop design to avoid automation pitfalls.
Next, Itamar Gilad argues that product success is rare due to underestimated complexity and misaligned forces, advocating for strategic clarity and intentional culture-building. Then, David Pereira highlights how pilot testing helps PMs validate assumptions, reduce risks, and iterate faster, and Brian Balfour predicts AI will redefine product teams—transforming methodologies, roles, monetization, and distribution—urging AI-native strategies. In an interview with Peter Yang, Anthropic’s Scott White details how Claude 3.7 Sonnet accelerates product development through AI-generated PRDs, evals, and agentic coding tools.
Lastly, we critique overly blameless post-mortems, advocating for balanced accountability to prevent mediocrity, and Johanna Rothman champions rolling-wave planning to reduce pressure and improve adaptability. Ash Maurya stresses that successful pivots require rapid business model testing and external accountability. Additionally, Zvi Mowshowitz dissects Manus, a Chinese AI agent hyped as groundbreaking but revealed as a Claude wrapper, and, finally, Andrew Chen explores “vibe coding,” where AI reshapes software creation, predicting a shift toward intuitive, GUI-driven design, fragmented UX, and adaptive, self-improving applications.
Effective product development requires both strategic alignment and healthy Product Backlog management. Misalignment leads to backlog bloat, trust erosion, and building the wrong products. By implementing proper alignment tools, separating discovery from delivery, and maintaining appropriate backlog size (3-6 sprints), teams can build products that truly matter. Success depends on trust, collaboration, risk navigation, and focusing on outcomes over outputs. Learn more about how to embrace the alignment-to-value pipeline and create your product operating model.
TL; DR: Leadership Blindspots, AI Prototyping — Food for Agile Thought #483
Welcome to the 483rd edition of the Food for Agile Thought newsletter, shared with 42,689 peers. This week, Luona Lin and Kim Parker reveal U.S. workers’ anxiety around AI’s workplace impacts, while Lennart Meincke, Ethan Mollick, Lilach Mollick, and Dan Shapiro stress context-dependent complexities in prompt engineering. Charlie Guo sees AI hallucinations becoming manageable, Claire Lew identifies overlooked leadership blindspots, Joost Minnaar showcases Haier’s entrepreneurial employee model, and Arjun Shah champions ambitious “maximum thinking” over incrementalism.
Next, Will Larson explains how anyone, including engineers, can meaningfully shape organizational strategy through practical influence, while Ian Vanagas shares PostHog’s lessons on team autonomy, rapid iteration, and customer-driven development. Scott Sehlhorst highlights the power of clear problem statements, and Roman Pichler emphasizes intentional team design as key to sustained product success.
Lastly, Dan Shipper highlights Michael Taylor’s AI tool, Rally, which transforms customer research through risk-free audience simulations, while Aakash Gupta and Colin Matthews showcase rapid, code-free AI prototyping with Bolt, Lovable, v0, Replit, and Cursor. Johanna Rothman clarifies ranking versus prioritization for limiting WIP, and Jeff Gothelf outlines effective OKR retrospectives emphasizing preparation and evidence-based adjustments.
I have been interested in how artificial intelligence as an emerging technology may shape our work since the advent of ChatGPT; see my various articles on the topic. As you may imagine, when OpenAI’s Deep Research became available to me last week, I had to test-drive it.
I asked it to investigate how AI-driven approaches enable agile product teams to gain deeper customer insights and deliver more innovative solutions. The results were enlightening, and I’m excited to share both my experience with this research approach and the key insights that emerged. (Download the complete report here: AI in Agile Product Teams: Insights from Deep Research and What It Means for Your Practice.)
TL; DR: No Place to Hide from AI — Food for Agile Thought #482
Welcome to the 482nd edition of the Food for Agile Thought newsletter, shared with 42,693 peers. This week, with no place to hide from AI, Ethan Mollick explores the rapid rise of Claude 3.7 and Grok 3, urging leaders to rethink AI’s role beyond simple automation, while Dan Shipper and Alex Duffy assess GPT-4.5’s improved emotional intelligence but persistent hallucinations. Maarten Dalmijn warns against distrust’s bureaucratic cycle, advocating for trust-driven leadership, while Swizec Teller critiques failed rewrites, emphasizing iterative refactoring over disruptive rebuilds. Moreover, Eduardo Baptista, Julie Zhu, and Fanny Potkin highlight DeepSeek’s AI ambitions, raising regulatory concerns, and Ben Thompson examines AI’s evolution, semiconductor risks, and U.S. policy shifts.
Next, Marty Cagan envisions AI-driven product teams shrinking to three key roles—PM, designer, and engineer—as automation reshapes discovery and delivery. Leah Tharin and Tal Raviv discuss AI’s evolving role in product management, urging PMs to experiment with contextual AI tools. Des Traynor outlines essential product review questions, emphasizing AI reliability, impact assessment, and iteration. Jenny Wenger shares lessons from building the Product Culture Blueprint Drafter, highlighting AI automation, prompt engineering, and product operations’ evolving role.
Lastly, Zvi Mowshowitz analyzes Grok 3’s strengths, including speed and Twitter integration, while addressing its hallucinations, biases, and xAI’s struggle to patch its vulnerabilities. Paweł Huryn introduces Deep Market Researcher, an AI agent streamlining product managers’ market research and strategic planning, while Avantika Gomes advocates adaptability in product roadmaps, highlighting Figma’s iterative approach. Finally, Ash Maurya challenges the obsession with experimentation, arguing that strong explanations and thought experiments are essential for validating business models before costly testing.
