Scrum Master Toolbox Podcast: Agile storytelling from the trenches

BONUS: AI Is Not Just Typing Faster With Marcus Hammarberg
In this BONUS episode, Vasco Duarte and Marcus Hammarberg explore what changes when AI agents move from writing small functions to creating whole features, running through tickets, and even improving their own instructions. The conversation keeps coming back to a simple but difficult question: if agents make software delivery faster, what should humans use that speed for?
When the Agent Writes the Feature
"What I realized was that the agent could now write the code from a prompt that I gave it, and it was good enough that I didn't have to touch a lot of it."
 
Marcus starts with the moment that changed his own practice: in December 2025, he used an AI agent to build a feature for a flow metrics app based on Jira change logs. Before that, AI coding felt like asking for small functions and then cleaning up the result. This time, the agent could take a feature-level prompt and produce something useful. That shifted the question from "can AI help me type code?" to "what happens to planning, quality, and ownership when implementation becomes dramatically faster?"
Slowing Down Enough To Think
"You end up not only not having seen the code, but not maybe even know what you wrote."
 
Vasco connects Marcus' experience to an earlier episode where Marcus had to learn to program after breaking his hand. Back then, the lesson was that typing was not the bottleneck. Thinking was. Now AI has made "typing" even faster, and Marcus warns that this creates a new danger: teams can approve suggestions, specs, and generated code without really understanding the intent they gave the system. In that world, slowing down becomes part of the human job.
From Developer To Robot Instructor
"With the robot in place, what are you doing now? You're giving the robot instructions, and you're verifying the result. This is exactly what we are doing with the AI now."
 
After rereading The Goal, Marcus started to see AI agents as the software equivalent of factory robots. A welder used to make the weld directly. With robots, the human writes instructions and verifies the result. Software is entering the same kind of transition. The open question is uncomfortable: can someone reliably verify AI-generated software without being a strong developer first? Marcus does not pretend to know the answer, and that uncertainty is part of the value of the conversation.
Harnesses, Feedback Loops, And Agent Teams
"A really good harness should also have some deterministic checks in the feedback mechanism."
 
Marcus describes teams experimenting with "lights-out" agent factories: a project manager agent receives intent, delegates to sub-agents, the agents move work across a Kanban board, and they even run retrospectives to improve their own instructions. That sounds futuristic, but Marcus brings it back to Agile basics. If prompts and guardrails are non-deterministic, then the harness needs deterministic feedback: tests, linting rules, functional checks, non-functional checks, architectural rules, and explicit slices of work that humans can test before moving on. The lesson is not to abandon Agile. It is to make feedback loops more concrete because the work is moving faster.
The Human Side Of Losing The Easy Wins
"I don't feel the same kind of ownership when I have prompted my way to a good application as I did when I wrote it."
 
The episode also names a quieter transformation: developers may lose the small dopamine hits that came from solving tiny coding problems, seeing tests pass, and refactoring by hand. If agents handle the easy work, humans may be left with harder, slower problems: intent, architecture, product value, safety, and trade-offs. That changes the emotional shape of development. Marcus and Vasco explore what happens when a developer is no longer only a coder, but the manager of an agent team.
Stop Measuring AI By Output
"Awesome, are you selling percentages?"
 
Marcus brings the conversation back to Theory of Constraints: throughput is not how busy the robots are, how many pull requests were merged, or how many tokens were consumed. Throughput is economic value. Vasco and Marcus connect this to product thinking: if AI lets teams produce more features, that only matters when those features create better outcomes for customers and the business. Otherwise, AI becomes a faster feature factory. As Marcus puts it, if people do not understand the "why" and the metric they are optimizing, AI is truly just typing faster.
What To Read Next
"It starts and stops with The Goal."
 
Marcus recommends starting with The Goal by Eliyahu Goldratt, then applying its factory lessons to software, flow, and AI agents. Vasco also mentions Ari-Pekka Skarp's Scrum Master Toolbox episode, Philip Su's work on dark factories, and Spec Kit. Marcus points listeners to his writing at marcusoft.net, where he is exploring AI as software development's new set of robots.
 
About Marcus Hammarberg
 
Marcus Hammarberg is a Swedish agile consultant, Kanban practitioner, and author of Kanban in Action and Salvation: The Bungsu Story, about using Lean, Agile, and Kanban to help an Indonesian hospital recover from crisis. Previous Scrum Master Toolbox episodes: Marcus page. LinkedIn. Today he explores agentic software development in practice.
 
You can link with Marcus Hammarberg on LinkedIn, read his writing at marcusoft.net, and find his previous Scrum Master Toolbox episodes on the Marcus Hammarberg guest page.

BONUS: AI in the SDLC Puts Customer Feedback Before Faster Typing
AI is already changing software development, but David Mantica argues that the real shift is not only in code generation. In this BONUS episode, Vasco and David talk about what happens when AI enters the whole software development life cycle: executive decision-making, Scrum Master work, product feedback, data quality, agentic systems, and the human skill of learning to adapt.
From Executive Curiosity to Real AI Use Cases
"You have two directions you can go with AI. One is productivity. The other part of AI that organizations haven't quite tapped into yet is how do you make money with it?"
 
David describes AI adoption as something that, like agile and AWS before it, often starts in the middle of the organization before executives fully understand what is happening. Early conversations were about policy, regulation, and tool choices. Now, the conversation has moved toward use cases: where GenAI helps personal productivity, where agentic systems fit, and how organizations turn the tools into actual business value. David also points out a gap that matters for leaders: many executives know their people are using AI, but they are not using it deeply themselves, which makes it harder for them to see what is possible inside their own business model.
AI Should Move Teams Closer to the Business
"If a team does this right, they're going to keep getting closer and closer to the business. They're going to keep spending more time on what it is that we should be building, not on just building it."
 
For David, faster coding is only one part of the SDLC change. Scrum Masters can use AI to improve backlog work, user story mapping, retrospective follow-up, and team data, but the point is not better administrative output. The point is to free time for coaching, communication, and helping teams return to the agile way of working instead of being trapped by artifacts and process. If developers become faster while product discovery, testing, security, and customer feedback stay unchanged, the organization has only accelerated part of the system.
The Bottleneck Is Value, Not Typing Speed
"Can I test more before I go to production? And can I talk to the client more before I actually lock in?"
 
David and Vasco connect AI in the SDLC to product discovery. Coding tools such as Factory may help teams produce software faster, but David argues that the bigger opportunity is to test more ideas, prototypes, classes, webinars, or internal tools with real customers. This links directly to The Black Swan by Nassim Nicholas Taleb: only the market can tell us what is valuable. In practical terms, AI should help teams bring product thinking closer to the coder and bring customer feedback into the SDLC instead of treating it as something that happens after delivery.
Governance Has To Catch Up With AI Productivity
"Organization uses AI, they see the productivity increases, they hire 10-20% less staff because they're getting that productivity increase, and then they create no real new value."
 
David is blunt that many organizations are not yet changing governance, portfolio decisions, budgeting, or success metrics fast enough. Measuring more pull requests per developer is not the same as measuring more value for customers. Vasco and David explore the need for internal product marketing, lightweight feedback loops, and ways for internal and external customers to touch, test, and react to what is being built. Without that, companies risk using AI to reduce cost while missing the larger opportunity: learning faster than competitors.
Agentic Systems Are Only As Good As Their Data
"The agent is only as good as the pool of information by which it's pulling from to answer the questions by which you're asking it to answer."
 
Data quality becomes much more concrete when agents move from answering questions to taking action. David frames agentic systems as coded agents: they behave like a person using GenAI, but they still depend on the information they can access. That means teams need to think carefully about grounding, retrieval, trusted documentation, and the boundaries around what an agent is allowed to use. A weak data pool creates weak action, and a poorly grounded customer service agent will act like a poorly trained human agent.
Lightweight Testing Beats Big AI Programs
"What is your lightweight way of testing your agents without boiling the ocean?"
 
David warns against treating AI adoption as a top-down program that must be perfect before anyone uses it. Agentic systems will make mistakes, just like people do, so teams need controlled environments, visible feedback, and repeated adjustment. Vasco connects this to startup odds: most new initiatives fail, so the answer is not to plan harder, but to test more ideas and learn faster. David's advice is to keep the experiments lightweight, fund learning, and build the capability to test agents without trying to build an entire AI universe first.
The Human Skill Is Adaptation
"The human skill needs to be teaching people to change and adapt, and be comfortable with getting rid of skills that they thought made them hundreds of thousands of dollars that are totally unnecessary now."
 
David closes with the human side of AI in software organizations. He connects AI adoption to adaptive leadership and the fear people feel when skills that once made them valuable may no longer be enough. For Scrum Masters, leaders, and product people, the work is to help teams learn, test, and adapt without pretending that the future is already clear. The customer remains the final judge of value, and AI makes that truth more urgent, not less.
About David Mantica
David Mantica is Managing Director at SoftEd, where he helps organizations build practical skills for AI, agile, leadership, and business analysis. With 30+ years of experience and 700+ speaking engagements, he makes emerging technology accessible for working teams.  LinkedIn
 
You can link with David Mantica on LinkedIn and find role-specific AI resources at SoftEd.

BONUS: The Scrum Master's AI Prompt Library, with Joshua McDonald
In this BONUS episode, Joshua McDonald shares how he uses AI in the daily work of a Scrum Master, from team summaries and leadership updates to retrospectives and coaching preparation. The conversation is practical and grounded in one clear warning: AI can give Scrum Masters time back, but it cannot replace their judgment.
When AI Stops Being a Toy and Starts Saving Real Time
"It would have to be when it started to save me about one to two hours per day."
 
Joshua's turning point came when AI moved from curiosity to practical assistant. He began using tools like Microsoft Copilot to analyze reports, prepare communication, and package updates for different audiences. The value was not that AI did the thinking for him. The value was that it helped him reach useful coaching conversations faster, especially when working with reports on predictability, volatility, and cycle time.
Turning Flow Data Into Better Coaching Conversations
"It was able to pinpoint where the bottlenecks were, and where I should look."
 
One of Joshua's first high-value AI workflows combined screenshots of team metrics with exported Jira stories. Instead of spending half a day scanning acceptance criteria, transitions, and bottlenecks, he used AI to identify patterns and suggest where to look next. From there, he would ask follow-up questions from the perspective of a Scrum Master, using AI as a sparring partner to prepare coaching talking points before speaking with the team.
AI as a Communication Coach, Not a Replacement for Judgment
"I like to use AI as my coach to help me in my communication style."
 
Joshua is careful with sensitive communication. When he needs to bring data, feedback, or a difficult pattern to an individual or team, he asks AI to help him test the wording. He might ask how a message could land with someone who has reacted defensively before, or request phrases to avoid. In that sense, AI becomes a preparation space: a place to roleplay, refine tone, and make sure the human conversation starts from care rather than accidental bluntness.
A Weekly AI Workflow for Scrum Masters
"I use it pretty much at everything I create."
 
Joshua's weekly rhythm includes AI-generated summaries from standups, backlog refinement, and retrospectives. He uses templates that identify who shared which updates, what action items emerged, and what the team should be able to review later. For retrospectives, he keeps a prompt library on a whiteboard, with prompts adapted for Scrum teams, Kanban teams, and team interests. A hiking team might get a Big Bend retrospective; a car enthusiast might get a BMW-themed one. The point is not novelty for its own sake, but creating retrospectives that feel more connected to the people in the room.
The Prompt Library as a Scrum Master Asset
"I save them. I reuse them. I refine them."
 
Joshua does not treat prompts as throwaway text. He keeps them visible on a whiteboard, labels them by purpose, and refines them over time. That library helps him preserve the structure of useful workflows while adapting the output to the team and context. For Scrum Masters and agile coaches, this is one of the practical takeaways: a prompt library becomes part of the working system, just like a facilitation kit, retrospective catalog, or coaching journal.
Faster Is Not the Same as Better
"I don't lean on it to do critical thinking for me."
 
Joshua connects AI adoption to a deeper risk: using it to produce more work without asking whether that work matters. He validates AI output, checks the data, and stays responsible for conclusions. AI helps him get to an end point faster, but the Scrum Master still owns the thinking, the interpretation, and the impact on people. That distinction matters when teams are being pushed to adopt AI without clear guidance.
What Scrum Masters Should Try on Monday
"Have it coach you. Say, this is my role. This is what's on my agenda for today."
 
Joshua's recommended first experiment is simple: do a personal standup with AI. Share what you did yesterday, what you want to do today, and the challenges in front of you. Then ask AI to act as your coach and help you identify focus, priorities, and possible next steps. Within a week, a Scrum Master can see whether this creates more clarity, better communication, and more intentional use of time.
Learning AI Without Waiting for Permission
"Start there with basic AI fundamentals."
 
For Scrum Masters who are new to AI, Joshua recommends beginning with practical courses on platforms like Udemy, Coursera, or LinkedIn Learning. Then, connect with peers who are already experimenting with prompt creation. The goal is not to become an AI expert overnight. The goal is to understand what AI can support, what it should not replace, and how to build a prompt library that improves your own work.
About Joshua McDonald
Joshua is endlessly curious about better ways of working. He helps teams grow by blending experimentation, creativity, and AI with practical coaching. When a meeting feels routine, he's already testing a new approach to make it more valuable. Energetic and inventive, he turns everyday collaboration into opportunities for team growth.
 
You can link with Joshua McDonald on LinkedIn.

Kaloyan Chalakov: Great Product Owners Start the Conversation Instead of Becoming the Boss
In this episode, we refer to Product Owner anti-patterns and team and PO collaboration.
The Great Product Owner: Open Communication, Active Listening, and Feasibility First
Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.
 
"Start the open conversation and listen to the critical discussions." - Kaloyan Chalakov
 
Kaloyan describes the best Product Owner pattern as almost the opposite of control. A strong Product Owner comes to the team with the outcome, the timeline, and the stakeholder context, then starts a feasibility conversation. They ask what is possible, what is risky, what can be decomposed, and what the team needs in order to deliver. In Kaloyan's example, the Product Owner stayed focused on stakeholder communication while the team helped shape the work into something practical. Stakeholders were kept in the loop, risks were visible, and everyone could adjust plans early. The most useful behavior was not a personality trait, because every Product Owner is different. It was open communication and active listening, especially when the team raised uncomfortable technical or delivery constraints. Great Product Owners do not need the team to say yes to everything. They need the team to help them face reality early enough to make better product decisions.
 
Self-reflection Question: How often do your Product Owner conversations start with feasibility and shared reality, rather than commitment pressure?
The Bad Product Owner: Acting Like the Boss of the Team
Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.
 
"Tell me what exactly to do, and I'll do exactly what you're telling me to do." - Kaloyan Chalakov
 
The Product Owner anti-pattern Kaloyan warns us about is the Product Owner as the boss of the team. This shows up when the Product Owner assigns work, manages people, and controls every detail of delivery instead of focusing on product outcomes and customer needs. The consequence is a damaging loop. The team stops bringing their thinking and creativity, waits for instructions, and becomes task handlers. The Product Owner then feels overwhelmed because every decision and question returns to them, so they add more control. That control removes even more initiative from the team. Kaloyan connects this pattern to the Product Owner's real work: difficult conversations with customers, narrowing big requests into practical steps, and protecting focus on product value. When the Product Owner becomes the team's manager, both product discovery and team self-organization suffer.
 
In this segment, we refer to Product Owner collaboration.
 
Self-reflection Question: Where might your Product Owner role be taking decisions away from the team instead of creating clarity for better decisions?
 
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🔥In the ruthless world of fintech, success isn't just about innovation—it's about coaching!🔥
Angela thought she was just there to coach a team. But now, she's caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn't just about the product—it's about the people.
 
🚨 Will Angela's coaching be enough? Find out in Shift: From Product to People—the gripping story of high-stakes innovation and corporate intrigue.
 
Buy Now on Amazon
 
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About Kaloyan Chalakov
 
Kaloyan Chalakov spent years making high-stakes decisions at sea before moving into Agile and organisational change, bringing lessons from safety-critical systems into how modern teams work.
 
You can link with Kaloyan Chalakov on LinkedIn.

Kaloyan Chalakov: Building Scrum Master Success Around Trust and Unspoken Leaders
Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.
 
"Success for me is making myself obsolete in the team." - Kaloyan Chalakov
 
Kaloyan defines Scrum Master success in a way that sounds funny at first: forty hours a week to drink coffee and do nothing. But the point is serious. Success means the team no longer needs the Scrum Master for every decision, question, or conflict. They are self-organizing, self-managed within their context, and able to call for help only when it really matters. When Kaloyan joins a new team, he avoids setting big targets in the first three months. He observes, builds trust, and watches for what the team does not show openly. One of his key signals is the presence of "unspoken leaders," the people the team looks to when something difficult happens. He learned that pattern first in safety-critical maritime work, where trust in the people beside you is not optional. For Kaloyan, Scrum Master success starts with trust, grows through observation, and ends when the team can stand on its own.
 
Self-reflection Question: Who are the unspoken leaders in your team, and how are you helping them strengthen the team's self-management?
Featured Retrospective Format for the Week: Open Room Coffee Conversation
Kaloyan's favorite retrospective format is simple: no heavy gamification, no elaborate setup, just a mature team in a room with coffee or tea and a real conversation. The team uses a ball as the talking object. Whoever wants to raise a problem takes the ball, shares what hurt during the last sprint, and asks for help. The format is unpredictable, so the Scrum Master must pay close attention to small signals: is the discussion becoming personal, is a conflict rising, or is the team finding the root cause together? Kaloyan likes the format because it brings real issues to the surface quickly, and often the third person to speak has already helped others see their own concerns more clearly.
 
In this segment, we refer to Karin Tenelius on self-management.
 
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🔥In the ruthless world of fintech, success isn't just about innovation—it's about coaching!🔥
Angela thought she was just there to coach a team. But now, she's caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn't just about the product—it's about the people.
 
🚨 Will Angela's coaching be enough? Find out in Shift: From Product to People—the gripping story of high-stakes innovation and corporate intrigue.
 
Buy Now on Amazon
 
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About Kaloyan Chalakov
 
Kaloyan Chalakov spent years making high-stakes decisions at sea before moving into Agile and organisational change, bringing lessons from safety-critical systems into how modern teams work.
 
You can link with Kaloyan Chalakov on LinkedIn.