Episodes

  • AI Assisted Coding: How Spending 4x More on Code Quality Doubled Development Speed With Eduardo Ferro

    AI Assisted Coding: How Spending 4x More on Code Quality Doubled Development Speed With Eduardo Ferro
    Feb 18 2026
    AI Assisted Coding: How Spending 4x More on Code Quality Doubled Development Speed What happens when you combine nearly 30 years of engineering experience with AI-assisted coding? In this episode, Eduardo Ferro shares his experiments showing that AI doesn't replace good practices—it amplifies them. The result: doubled productivity while spending four times more on code quality. Vibe Coding vs Production-Grade AI Development "Vibe coding is flow-driven, curiosity-based way of building software with AI. It's less about meticulously reviewing each line of code, and more about letting the AI steer the process—perfect for quick experiments, side projects, MVPs, and prototypes." Edu draws a clear distinction between vibe coding and production AI development. Vibe coding is exploration-focused, where you let AI drive while you learn and discover. Production AI coding is goal-focused, with careful planning, spec definition, and identification of edge cases before implementation. Both use small, safe steps and continuous conversation with the AI, but production code demands architectural thinking, security analysis, and sustainability practices. The key insight is that even vibe coding benefits from engineering discipline—as experiments grow, you need sustainable practices to maintain flexibility. How AI Doubled My Productivity "I was investing four times more in refactoring, cleanup, deleting code, introducing new tests, improving testability, and security analysis than in generating new features. And at the same time, globally, I think I more or less doubled my pace of work." Edu's two-month experiment with production code revealed a counterintuitive finding: by spending 4x more time on code quality activities—refactoring, cleanup, test improvement, and security analysis—he actually doubled his overall delivery speed. The secret lies in fast feedback loops. With AI, you can implement a feature, run automated code review, analyze security, prioritize improvements, and iterate—all within an hour. What used to be a day's work happens in a single focused session, and the quality improvements compound over time. The Positive Spiral of Code Removal "We removed code, so we removed all the features that were not being used. And whenever I remove this code, the next step is to automatically try to see, okay, can I simplify the architecture." One of the most powerful practices Edu discovered is using AI to accelerate code removal. By connecting product analytics to identify unused features, then using AI to quickly remove them, you trigger a positive spiral: removing code makes architecture changes easier, easier architecture changes enable faster feature development, which leads to more opportunities for simplification. This creates a self-reinforcing cycle that humans historically have been reluctant to pursue because removal was as expensive as creation. Preparing the System Before Introducing Change "What I want to generate is this new functionality—how should I change my system to make it super easy to introduce this one? It's not about making the change, it's about making the change easy." Edu describes a practice that was previously too expensive: preparing the system before introducing changes. By analyzing architecture decision records, understanding the existing design, and adapting the codebase first, new features become trivial to implement. AI makes this preparation cheap enough to do routinely. The result is systems that evolve cleanly rather than accumulating technical debt with each new feature. AI as an Amplifier: The Double-Edged Sword "AI is an amplifier. People who already know how to develop software well will continue to develop it well and faster. People who did not know how to develop software well will probably get in trouble much faster than they would otherwise." Edu's central metaphor is AI as an amplifier—it doesn't replace engineering judgment, it magnifies its presence or absence. Teams with strong practices will see accelerated improvement; teams without them will generate technical debt faster than ever. This has implications beyond individual productivity: the market will be saturated with solutions, making product discovery and distribution channels more important than implementation capability. In this episode, we refer to Edu's blog post Fast Feedback, Fast Features: My AI Assisted Coding Experiment and Vibe Coding by Gene Kim. About Eduardo Ferro Edu Ferro is Head of Engineering and Data Platform at ClarityAI, with nearly 30 years' experience. He helps teams deliver value through Lean, XP, and DevOps, blending technical depth with product thinking. Recently he explores AI-assisted product development, sharing insights and experiments on his site eferro.net. You can connect with Edu Ferro on LinkedIn.
    Show more Show less
    33 mins
  • AI Assisted Coding: Stop Building Features, Start Building Systems with AI With Adam Bilišič

    AI Assisted Coding: Stop Building Features, Start Building Systems with AI With Adam Bilišič
    Feb 17 2026
    AI Assisted Coding: Stop Building Features, Start Building Systems with AI What separates vibe coding from truly effective AI-assisted development? In this episode, Adam Bilišič shares his framework for mastering AI-augmented coding, walking through five distinct levels that take developers from basic prompting to building autonomous multi-agent systems. Vibe Coding vs AI-Augmented Coding: A Critical Distinction "The person who is actually creating the app doesn't have to have in-depth overview or understanding of how the app works in the background. They're essentially a manual tester of their own application, but they don't know how the data structure is, what are the best practices, or the security aspects." Adam draws a clear line between vibe coding and AI-augmented coding. Vibe coding allows non-developers to create functional applications without understanding the underlying architecture—useful for product owners to create visual prototypes or help clients visualize their ideas. AI-augmented coding, however, is what professional software engineers need to master: using AI tools while maintaining full understanding of the system's architecture, security implications, and best practices. The key difference is that augmented coding lets you delegate repetitive work while retaining deep knowledge of what's happening under the hood. From Building Features to Building Systems "When you start building systems, instead of thinking 'how can I solve this feature,' you are thinking 'how can I create either a skill, command, sub-agent, or other things which these tools offer, to then do this thing consistently again and again without repetition.'" The fundamental mindset shift in AI-augmented coding is moving from feature-level thinking to systems-level thinking. Rather than treating each task as a one-off prompt, experienced practitioners capture their thinking process into reusable recipes. This includes documenting how to refactor specific components, creating templates for common patterns, and building skills that encode your decision-making process. The goal is translating your coding practices into something the AI can repeatedly execute for any new feature. Context Management: The Critical Skill For Working With AI "People have this tendency to install everything they see on Reddit. They never check what is then loaded within the context just when they open the coding agent. You can check it, and suddenly you see 40 or 50% of your context is taken just by MCPs, and you didn't do anything yet." One of the most overlooked aspects of AI-assisted coding is context management. Adam reveals that many developers unknowingly fill their context window with MCP (Model Context Protocol) tools they don't need for the current task. The solution is strategic use of sub-agents: when your orchestrator calls a front-end sub-agent, it gets access to Playwright for browser testing, while your backend agent doesn't need that context overhead. Understanding how to allocate context across specialized agents dramatically improves results. The Five Levels of AI-Augmented Coding "If you didn't catch up or change your opinion in the last 2-3 years, I would say we are getting to the point where it will be kind of last chance to do so, because the technology is evolving so fast." Adam outlines a progression from beginner to expert: Level 1 - Master of Prompts: Learning to write effective prompts, but constantly repeating context about architecture and preferences Level 2 - Configuration Expert: Using files like .cursorrules or CLAUDE.md to codify rules the agent should always follow Level 3 - Context Master: Understanding how to manage context efficiently, using MCPs strategically, creating markdown files for reusable information Level 4 - Automation Master: Creating custom commands, skills, and sub-agents to automate repetitive workflows Level 5 - The Orchestrator: Building systems where a main orchestrator delegates to specialized sub-agents, each running in their own context window The Power of Specialized Sub-Agents "The sub-agent runs in his own context window, so it's not polluted by whatever the orchestrator was doing. The orchestrator needs to give him enough information so it can do its work." At the highest level, developers create virtual teams of specialized agents. The orchestrator understands which sub-agent to call for front-end work, which for backend, and which for testing. Each agent operates in a clean context, focused on its specific domain. When the tester finds issues, it reports back to the orchestrator, which can spin up the appropriate agent to fix problems. This creates a self-correcting development loop that dramatically increases throughput. In this episode, we refer to the Claude Code subreddit and IndyDevDan's YouTube channel for learning resources. About Adam Bilišič Adam Bilišič is a former CTO of a Swiss company with over 12 ...
    Show more Show less
    37 mins
  • When AI Decisions Go Wrong at Scale—And How to Prevent It With Ran Aroussi

    When AI Decisions Go Wrong at Scale—And How to Prevent It With Ran Aroussi
    Feb 16 2026
    BONUS: When AI Decisions Go Wrong at Scale—And How to Prevent It We've spent years asking what AI can do. But the next frontier isn't more capability—it's something far less glamorous and far more dangerous if we get it wrong. In this episode, Ran Aroussi shares why observability, transparency, and governance may be the difference between AI that empowers humans and AI that quietly drifts out of alignment. The Gap Between Demos and Deployable Systems "I've noticed that I watched well-designed agents make perfectly reasonable decisions based on their training, but in a context where the decision was catastrophically wrong. And there was really no way of knowing what had happened until the damage was already there." Ran's journey from building algorithmic trading systems to creating MUXI, an open framework for production-ready AI agents, revealed a fundamental truth: the skills needed to build impressive AI demos are completely different from those needed to deploy reliable systems at scale. Coming from the EdTech space where he handled billions of ad impressions daily and over a million concurrent users, Ran brings a perspective shaped by real-world production demands. The moment of realization came when he saw that the non-deterministic nature of AI meant that traditional software engineering approaches simply don't apply. While traditional bugs are reproducible, AI systems can produce different results from identical inputs—and that changes everything about how we need to approach deployment. Why Leaders Misunderstand Production AI "When you chat with ChatGPT, you go there and it pretty much works all the time for you. But when you deploy a system in production, you have users with unimaginable different use cases, different problems, and different ways of phrasing themselves." The biggest misconception leaders have is assuming that because AI works well in their personal testing, it will work equally well at scale. When you test AI with your own biases and limited imagination for scenarios, you're essentially seeing a curated experience. Real users bring infinite variation: non-native English speakers constructing sentences differently, unexpected use cases, and edge cases no one anticipated. The input space for AI systems is practically infinite because it's language-based, making comprehensive testing impossible. Multi-Layered Protection for Production AI "You have to put in deterministic filters between the AI and what you get back to the user." Ran outlines a comprehensive approach to protecting AI systems in production: Model version locking: Just as you wouldn't randomly upgrade Python versions without testing, lock your AI model versions to ensure consistent behavior Guardrails in prompts: Set clear boundaries about what the AI should never do or share Deterministic filters: Language firewalls that catch personal information, harmful content, or unexpected outputs before they reach users Comprehensive logging: Detailed traces of every decision, tool call, and data flow for debugging and pattern detection The key insight is that these layers must work together—no single approach provides sufficient protection for production systems. Observability in Agentic Workflows "With agentic AI, you have decision-making, task decomposition, tools that it decided to call, and what data to pass to them. So there's a lot of things that you should at least be able to trace back." Observability for agentic systems is fundamentally different from traditional LLM observability. When a user asks "What do I have to do today?", the system must determine who is asking, which tools are relevant to their role, what their preferences are, and how to format the response. Each user triggers a completely different dynamic workflow. Ran emphasizes the need for multi-layered access to observability data: engineers need full debugging access with appropriate security clearances, while managers need topic-level views without personal information. The goal is building a knowledge graph of interactions that allows pattern detection and continuous improvement. Governance as Human-AI Partnership "Governance isn't about control—it's about keeping people in the loop so AI amplifies, not replaces, human judgment." The most powerful reframing in this conversation is viewing governance not as red tape but as a partnership model. Some actions—like answering support tickets—can be fully automated with occasional human review. Others—like approving million-dollar financial transfers—require human confirmation before execution. The key is designing systems where AI can do the preparation work while humans retain decision authority at critical checkpoints. This mirrors how we build trust with human colleagues: through repeated successful interactions over time, gradually expanding autonomy as confidence grows. Building Trust Through Incremental Autonomy "Working ...
    Show more Show less
    41 mins
  • BONUS: Why Embedding Sales with Engineering in Stealth Mode Changed Everything for Snowflake With Chris Degnan

    BONUS: Why Embedding Sales with Engineering in Stealth Mode Changed Everything for Snowflake With Chris Degnan
    Feb 14 2026
    BONUS: Why Embedding Sales with Engineering in Stealth Mode Changed Everything for Snowflake In this episode, we talk about what it really takes to scale go-to-market from zero to billions. We interview Chris Degnan, a builder of one of the most iconic revenue engines in enterprise software at Snowflake. This conversation is grounded in the transformation described in his book Make It Snow—the journey from early-stage chaos to durable, aligned growth. Embedding Sales with Engineering While Still in Stealth "I don't expect you to sell anything for 2 years. What I really want you to do is get a ton of feedback and get customers to use the product so that when we come out of stealth mode, we have this world-class product." Chris joined Snowflake when there were zero customers and the company was still in stealth mode. The counterintuitive move of embedding sales next to engineering so early wasn't about driving immediate revenue, it was about understanding product-market fit. Chris's job was to get customers to try the product, use it for free, and break it. And break it they did. This early feedback led to material changes in the product before general availability. The approach helped shape their ideal customer profile (ICP) and gave the engineering team real-world validation that shaped Snowflake's technical direction. In a world where startups are pressured to show revenue immediately, Snowflake's investors took the opposite approach: focus on building a product people cannot live without first. Why Sales and Marketing Alignment Is Existential "If we're not driving revenue, if the revenue is not growing, then how are we going to be successful? Revenue was king." When Denise Persson joined as CMO, she shifted the conversation from marketing qualified leads (MQLs) to qualified meetings for the sales team. This simple reframe eliminated the typical friction between sales and marketing. Both leaders shared challenges openly and held each other accountable. When someone in either organization wasn't being respectful to the other team, they addressed it directly. Chris warns founders against creating artificial friction between sales and marketing: "A lot of founders who are engineers think that they want to create this friction between sales and marketing. And that's the opposite instinct you should have." The key insight is treating sales and marketing as a symbiotic system where revenue is the shared north star. Coaching Leaders Through Hypergrowth "If there's a problem in one of our organizations, if someone comes with a mentality that is not great for us, we're gonna give direct feedback to those people." Chris and Denise maintained tight alignment at the top level of their organizations through four CEO transitions. Their partnership created a culture of accountability that cascaded through both teams. When either hired senior people who didn't fit the culture, they investigated and addressed it. The coaching approach wasn't about winning by authority—it was about maintaining partnership and shared accountability for results. This required unlearning traditional management approaches that pit departments against each other and instead fostering genuine collaboration. Cultural Behaviors That Scale (And Those That Don't) "We got dumb and lazy. We forgot about it. And then we decided, hey, we're gonna go get a little bit more fit, and figure out how to go get the new logos again." Chris describes himself as a "velocity salesperson" with a hyper-focus on new customer acquisition. This focus worked brilliantly during Snowflake's growth phase—land customers, and the high net retention rate would drive expansion. However, as Snowflake prepared to go public, they took their foot off the gas on new logo acquisition, believing not all new logos were equal. This turned out to be a mistake. In his final year at Snowflake, working with CEO Sridhar Ramaswamy, they redesigned the sales team to reinvigorate the new logo acquisition machine. The lesson: the cultural behaviors that fuel early success must be consciously maintained and sometimes redesigned as you scale. Keeping the Message Narrow Before Going Platform "Eventually, I know you want to be a platform. But having a targeted market when you're initially launching the company, that people are spending money on, makes it easier for your sales team." Snowflake intentionally positioned itself in the enterprise data warehousing market—a $10-12 billion annual market with 5,000-7,000 enterprise customers—rather than trying to sound "bigger" as a platform play. The strategic advantage was accessing existing budgets. When selling to large enterprises that go through annual planning processes, fitting into an existing budget means sales cycles of 3-6 months instead of 9-18 months. Yes, competition eventually tried to corner Snowflake as "just a cute data warehouse," but by then they had captured significant market share and ...
    Show more Show less
    27 mins
  • The Art of Coaching Product Owners on What vs. How | Prabhleen Kaur

    The Art of Coaching Product Owners on What vs. How | Prabhleen Kaur
    Feb 13 2026
    Prabhleen Kaur: The Art of Coaching Product Owners on What vs. How 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. The Great Product Owner: Master of Stakeholder Relationships and the Power of No "The best PO is the person who has the superpower of saying no, and they can deal with the stakeholders with the same prowess." - Prabhleen Kaur Prabhleen describes working with a Product Owner who managed multiple stakeholders—not just a handful, but a significant number with competing priorities. What made him exceptional was his deep understanding of each stakeholder's pulse and motivations. He knew when to push back and how to frame the "no" in a way that stakeholders could accept. This wasn't random resistance—it came from thorough preparation manifested in clear roadmaps that made most incoming work predictable for the team. His user stories stood out for their richness in context: beyond the business requirements, they included information about who would be impacted, which proved invaluable for a team dealing with multiple interconnected systems. He leveraged JIRA's priority field effectively, ensuring the moment anyone opened the board, they could immediately understand what mattered most. Prabhleen emphasizes that this PO understood his role as the "what" while respecting the team as the "how." By maintaining strong stakeholder relationships built on mutual understanding, he created space for the team to prepare, plan, and deliver without constant firefighting. Self-reflection Question: Does your Product Owner have the preparation and stakeholder relationships needed to confidently say "no" when priorities compete, or does every request become an emergency? The Bad Product Owner: Technical Experts Who Manage the Sprint Backlog "The PO is the what, and the team is the how. When POs start directing the team about how to do things, the sprint goal gets compromised." - Prabhleen Kaur Prabhleen addresses a common anti-pattern she's observed repeatedly: Product Owners with technical backgrounds who cross the line from "what" into "how." When POs come from developer or technical roles, their expertise can become a liability if they start prescribing solutions rather than defining problems. They direct the team on implementation approaches, suggest specific technical solutions in user stories, and effectively manage the sprint backlog instead of focusing on the product backlog. The consequences are predictable: stories keep getting added or removed mid-sprint, the sprint goal becomes meaningless, and the team ends up delivering nothing because focus is constantly shifting. Prabhleen's solution starts in backlog refinement, where she ensures conversations about technical approaches happen openly with the whole team during estimation. When a PO suggests a specific implementation, she facilitates discussion about alternatives, allowing the team to voice their perspective. The key insight: everyone comes from a good place—the PO suggests solutions because they believe they're helping. The Scrum Master's role is to create space for the team to own the "how" while helping the PO see the value in stepping back. Self-reflection Question: When your Product Owner has technical expertise, how do you help them contribute their knowledge without directing the team's implementation choices? [The Scrum Master Toolbox Podcast Recommends] 🔥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 [The Scrum Master Toolbox Podcast Recommends] About Prabhleen Kaur Prabhleen is a Certified Scrum Master with 7+ years of experience helping teams succeed with SAFe, Scrum and Kanban. Passionate about clean backlogs, powerful metrics, and dashboards that actually mean something. She is also known for making JIRA behave, driving Agile transformations, and helping teams ship value consistently and confidently. You can link with Prabhleen Kaur on LinkedIn.
    Show more Show less
    14 mins
  • When Team Members Raise Concerns with Clarity, Not Anger | Prabhleen Kaur

    When Team Members Raise Concerns with Clarity, Not Anger | Prabhleen Kaur
    Feb 12 2026
    Prabhleen Kaur: When Team Members Raise Concerns with Clarity, Not Anger

    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.

    "My idea of success as a Scrum Master is when you look around, you see motivated people, and when something goes wrong, they come to you not in anger, but with concern." - Prabhleen Kaur

    Prabhleen offers a refreshing perspective on measuring success as a Scrum Master that goes beyond velocity charts and feature counts. She shares a pivotal moment when her team was in production, delivering relentlessly with barely any time to breathe. A team member approached her—not with frustration or blame—but with thoughtful concern: "This is not going to work out." He sat down with Prabhleen and the Product Owner, explaining that as the middle layer in an API creation team, delays from upstream were creating a cascading problem.

    What struck Prabhleen wasn't just the identification of the issue, but how he approached it: with options to discuss, not demands to make. This moment crystallized her definition of success. When team members feel safe enough to voice concerns early, when they come with ideas rather than accusations, when they see themselves as part of the solution rather than victims of circumstances—that's when a Scrum Master has truly succeeded.

    Prabhleen reminds us that while stakeholders may focus on features delivered, Scrum Masters should watch how well the team responds to change. That adaptability, rooted in psychological safety and mutual trust, is the true measure of a team's maturity.

    Self-reflection Question: When problems emerge in your team, do people approach you with defensive anger or constructive concern? What does that tell you about the psychological safety you've helped create?

    Featured Retrospective Format for the Week: Keep-Stop-Happy-Gratitude

    Prabhleen shares her favorite retrospective format, born from necessity when she joined an established team with dismal participation in their standard three-column retrospectives. She transformed it into a four-column approach: (1) What should we keep doing, (2) What should we stop doing, (3) One thing that will make you happy, and (4) Gratitude for the team. The third column—asking what would make team members happy—opened unexpected doors. Suggestions ranged from team outings to skipping Friday stand-ups, giving Prabhleen real-time insights into team needs without waiting for formal working agreement sessions. The gratitude column proved even more powerful. "Appreciation brings a space where trust is automatically built. When every 15 days you're sitting with the team making a point to say thank you to each other for all the work you've done, everybody feels mutually respected," Prabhleen explains. This ties directly to the trust-building discussed in Tuesday's episode—using retrospectives not just to improve processes, but to strengthen the human connections that make teams resilient.

    [The Scrum Master Toolbox Podcast Recommends]

    🔥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

    [The Scrum Master Toolbox Podcast Recommends]

    About Prabhleen Kaur

    Prabhleen is a Certified Scrum Master with 7+ years of experience helping teams succeed with SAFe, Scrum and Kanban. Passionate about clean backlogs, powerful metrics, and dashboards that actually mean something. She is also known for making JIRA behave, driving Agile transformations, and helping teams ship value consistently and confidently.

    You can link with Prabhleen Kaur on LinkedIn.
    Show more Show less
    12 mins
  • How AI Is Changing the Way Agile Teams Deliver Value | Prabhleen Kaur

    How AI Is Changing the Way Agile Teams Deliver Value | Prabhleen Kaur
    Feb 11 2026
    Prabhleen Kaur: How AI Is Changing the Way Agile Teams Deliver Value

    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.

    "AI's output is not the final output—it's always the two eyes we have that will get us the best results." - Prabhleen Kaur

    Prabhleen brings a timely challenge to the coaching conversation: the impact of AI on teams and how Scrum Masters should navigate this transformation. She frames it as both a challenge and an opportunity—teams are now capable of delivering faster than consumers can absorb, fundamentally changing expectations and dynamics.

    Prabhleen has observed her teams evolve from uncertainty about AI to confidently leveraging it for practical benefits. Developers use AI for writing and understanding code, particularly helpful for onboarding new team members who need to comprehend existing codebases quickly. QA professionals find AI invaluable for generating test cases based on story and epic context already captured in JIRA.

    The next frontier? Agentic AI, where AI systems communicate with each other to produce better outputs. But Prabhleen offers an important caution: AI is learning from many conversations, not all of which are reliable. The human element—critical thinking and verification—remains essential.

    For Scrum Masters, this means facilitating conversations about how teams want to experiment with AI, exploring edge cases in testing that AI can help identify, and helping teams navigate the evolving landscape of possibilities while maintaining quality and judgment.

    Self-reflection Question: How are you helping your team explore AI as a tool for improvement while ensuring they maintain critical thinking about the outputs AI produces?

    [The Scrum Master Toolbox Podcast Recommends]

    🔥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

    [The Scrum Master Toolbox Podcast Recommends]

    About Prabhleen Kaur

    Prabhleen is a Certified Scrum Master with 7+ years of experience helping teams succeed with SAFe, Scrum and Kanban. Passionate about clean backlogs, powerful metrics, and dashboards that actually mean something. She is also known for making JIRA behave, driving Agile transformations, and helping teams ship value consistently and confidently.

    You can link with Prabhleen Kaur on LinkedIn.
    Show more Show less
    15 mins
  • When Lack of Trust Turns Teams Into Isolated Individuals | Prabhleen Kaur

    When Lack of Trust Turns Teams Into Isolated Individuals | Prabhleen Kaur
    Feb 10 2026
    Prabhleen Kaur: When Lack of Trust Turns Teams Into Isolated Individuals

    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.

    "Teams self-destruct despite best efforts when they lack trust." - Prabhleen Kaur

    Prabhleen observed a troubling pattern while shadowing a team: stand-ups had become a register activity where people reported individual status without any connection to the sprint goal. There was no "we" in the conversation—only "I."

    The team had experienced a missed deadline due to a PR conflict that wasn't merged in time, but instead of addressing it openly, everyone focused on fixing the immediate problem while avoiding the deeper conversation. The discomfort was never voiced, and resentment accumulated silently.

    Prabhleen explains that team destruction is never about one action—it's about the accumulation of unspoken concerns that eventually explode at the worst possible moment. To rebuild trust, she recommends starting with peer reviews that encourage natural collaboration and conversation.

    Scrum Masters must be vocal about challenges in front of the entire team, modeling the openness they want to see. For teams that have completely withdrawn, anonymous feedback and scheduled one-on-ones can create safe spaces for honest communication. The key insight? Trust is rebuilt when people realize they will be heard and understood, not judged.

    In this segment, we talk about how trust is the foundation of effective teams and how its absence leads to working in silos.

    Self-reflection Question: When your team experiences a failure or missed deadline, do you create space for open conversation about what happened, or does everyone quietly move on while resentment builds?

    Featured Book of the Week: Scrum: The Art of Doing Twice the Work in Half the Time by Jeff Sutherland

    Prabhleen recommends Scrum: The Art of Doing Twice the Work in Half the Time by Jeff Sutherland as a foundational read for understanding the spirit behind the framework. "When I actually read the book and understood the nuances of rugby and how the team should be, everything started making sense. I grew beyond the Scrum guide, beyond following rules—it's about how the team operates around you as a collective," she explains. Prabhleen also highly recommends Turn the Ship Around by David Marquet, summarizing its core message as "leaders lead leaders." Both books shaped her understanding that frameworks exist to enable collaboration, not to create compliance. Check out the David Marquet episodes on the Scrum Master Toolbox Podcast for more insights on intent-based leadership.

    [The Scrum Master Toolbox Podcast Recommends]

    🔥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

    [The Scrum Master Toolbox Podcast Recommends]

    About Prabhleen Kaur

    Prabhleen is a Certified Scrum Master with 7+ years of experience helping teams succeed with SAFe, Scrum and Kanban. Passionate about clean backlogs, powerful metrics, and dashboards that actually mean something. She is also known for making JIRA behave, driving Agile transformations, and helping teams ship value consistently and confidently.

    You can link with Prabhleen Kaur on LinkedIn.
    Show more Show less
    16 mins