Xmas Special: Recovering the Essence of Agile – What’s Already Working in Software-Native Organizations

In this BONUS Xmas Special episode, we explore what happens when we strip away the certifications and branded frameworks to recover the essential practices that make software development work. Building on Episode 2’s exploration of the Project Management Trap, Vasco reveals how the core insights that sparked the Agile revolution remain valid – and how real organizations like Spotify, Amazon, and Etsy embody these principles to thrive in today’s software-driven world. The answer isn’t to invent something new; it’s to amplify what’s already working.

Agile as an Idea, Not a Brand

“The script (sold as the solution) will eventually kill the possibility of the conversation ever happening with any quality.”

We establish a parallel between good conversations and good software development. Just as creating “The Certified Conversational Method™” with prescribed frameworks and certification levels would miss the point of genuine dialogue, the commodification of agile into Agile™ has obscured its essential truth. The core idea was simple and powerful: build software in small increments, get it in front of real users quickly, learn from their actual behavior, adapt based on what you learn, and repeat continuously. This wasn’t revolutionary – it was finally recognizing how software actually works. You can’t know if your hypothesis about user needs is correct until users interact with it, so optimize for learning speed, not planning precision. But when the need to certify and validate “doing Agile right” took over, the idea got packaged, and often the package became more important than the principle.

Four Fundamental Practices That Enable Living Software

“Every deployment was a chance to see how users actually responded.”

Software-native organizations distinguish themselves through core practices that align with software as a living capability. In this episode, we review four critical ones: First, iterative delivery means shipping the smallest valuable increment possible and building on it – Etsy’s transformation from quarterly releases in 2009 to shipping 50+ times per day by 2012 exemplifies this approach, where each small change serves as a learning opportunity. Second, tight feedback loops get software in front of real users as fast as possible, whether through paper prototypes or production deployments. Third, continuous improvement of the process itself creates meta-feedback loops, as demonstrated by Amazon’s “You Build It, You Run It” principle introduced by Werner Vogels in 2006, where development teams running their own services in production learn rapidly to write more resilient code. Fourth, product thinking over project thinking organizes teams around long-lived products rather than temporary projects, allowing teams to develop deep expertise and become living capabilities themselves, accumulating knowledge and improving over time.

Spotify’s Evolutionary Approach

“The Spotify model has nothing to do with Spotify really. It was just a snapshot of how that one company worked at the time.”

Spotify’s journey reveals a critical insight often missed in discussions of their famous organizational model. Starting with standard Scrum methodology pre-2012, they adopted the squad model around 2012 with autonomous teams organized into tribes, documented in Henrik Kniberg and Anders Ivarsson’s influential white paper (direct PDF link). But post-2016, internal staff and agile coaches noted that the “Spotify model” had become mythology, and the company had moved on from original concepts to address new challenges. As Kniberg himself later reflected, the model has taken on a life of its own, much like Lean’s relationship to Toyota. The key insight isn’t the specific structure – it’s that Spotify treated their own organizational design as a living capability, continuously adapting based on what worked and what didn’t rather than implementing “the model” and declaring victory. That’s software-native thinking applied to organization design itself.

Amazon’s Two-Pizza Teams and Massive Scale

“Amazon deploys code every 11.7 seconds on average. That’s over 7,000 deployments per day across the organization.” (see this YouTube video of this talk)

Amazon’s two-pizza team principle goes far deeper than team size. Teams small enough to be fed with two pizzas (roughly 6-10 people) gain crucial autonomy and ownership: each team owns specific services and APIs, makes their own technical decisions, runs their services in production, and manages inter-team dependencies through APIs rather than meetings. This structure enabled Amazon to scale massively while maintaining speed, as teams could iterate independently without coordinating with dozens of other teams. The staggering deployment frequency – over 7,000 times per day as of 2021 – is only possible with a software-native structure for the company itself, demonstrating that this isn’t just about managing software delivery but touches everything, including how teams are organized.

Why These Practices Work

“These practices work because they align with what software actually is: a living, evolvable capability.”

The effectiveness of software-native approaches stems from their alignment with software’s true nature. Traditional project approaches assume we can know requirements upfront, estimate accurately, build it right the first time, and reach a meaningful “done” state. Software-native approaches recognize that requirements emerge through interaction with users, estimation is less important than rapid learning, “right” is discovered iteratively rather than designed upfront, and “done” only happens when we stop evolving the software. When Etsy ships 50 times per day, they’re optimizing for learning where each deployment is a hypothesis test. When Amazon’s teams own services end-to-end, they’re creating tight feedback loops where teams feel the pain of their own decisions directly. When Spotify continuously evolves their organizational model, they’re treating their own structure as software that should adapt to changing needs.

The Incomplete Picture and the Question of Universal Adoption

“If these approaches work, why aren’t they universal?”

We’re not trying to paint a unrealistically rosy picture – these organizations aren’t perfect. Spotify has had well-documented challenges with their model, Amazon’s culture has been criticized as demanding and sometimes brutal, and Etsy has gone through multiple strategic shifts. But what matters is that they’re practicing software-native development at scale, and it’s working well enough that they can compete and thrive. They’re not following a playbook perfectly but embodying principles and adapting continuously. This raises the critical question that will be explored in the next episode: if these approaches work, why do so many organizations still operate in project mode, and why do “agile transformations” so often fail to deliver real change? Understanding the resistance – what we call The Organizational Immune System – is essential to overcoming it.

References for Further Reading

About Vasco Duarte

Vasco Duarte is a thought leader in the Agile space, co-founder of Agile Finland, and host of the Scrum Master Toolbox Podcast, which has over 10 million downloads. Author of NoEstimates: How To Measure Project Progress Without Estimating, Vasco is a sought-after speaker and consultant helping organizations embrace Agile practices to achieve business success.

You can link with Vasco Duarte on LinkedIn.

AI Assisted Coding: Augmented AI Development – Software Engineering First, AI Second

In this special episode, Dawid Dahl introduces Augmented AI Development (AAID)—a disciplined approach where professional developers augment their capabilities with AI while maintaining full architectural control. He explains why starting with software engineering fundamentals and adding AI where appropriate is the opposite of most frameworks, and why this approach produces production-grade software rather than technical debt.

The AAID Philosophy: Don’t Abandon Your Brain

“Two of the fundamental developer principles for AAID are: first, don’t abandon your brain. And the second is incremental steps.”

Dawid’s Augmented AI Development framework stands in stark contrast to “vibecoding“—which he defines strictly as not caring about code at all, only results on screen. AAID is explicitly designed for professional developers who maintain full understanding and control of their systems. The framework is positioned on the furthest end of the spectrum from vibe coding, requiring developers to know their craft deeply. The two core principles—don’t abandon your brain, work incrementally—reflect a philosophy that AI is a powerful collaborator, not a replacement for thinking. This approach recognizes that while 96% of Dawid’s code is now written by AI, he remains the architect, constantly steering and verifying every step.

In this segment we refer to Marcus Hammarberg’s work and his book The Bungsu Story.

Software Engineering First, AI Second: A Hill to Die On

“You should start with software engineering wisdom, and then only add AI where it’s actually appropriate. I think this is super, super important, and the entire foundation of this framework. This is a hill I will personally die on.”

What makes AAID fundamentally different from other AI-assisted development frameworks is its starting point. Most frameworks start with AI capabilities and try to add structure and best practices afterward. Dawid argues this is completely backwards. AAID begins with 50-60 years of proven software engineering wisdom—test-driven development, behavior-driven development, continuous delivery—and only then adds AI where it enhances the process. This isn’t a minor philosophical difference; it’s the foundation of producing maintainable, production-grade software. Dawid admits he’s sometimes “manipulating developers to start using good, normal software engineering practices, but in this shiny AI box that feels very exciting and new.” If the AI wrapper helps developers finally adopt TDD and BDD, he’s fine with that.

Why TDD is Non-Negotiable with AI

“Every time I prompt an AI and it writes code for me, there is often at least one or two or three mistakes that will cause catastrophic mistakes down the line and make the software impossible to change.” Continue reading AI Assisted Coding: Augmented AI Development – Software Engineering First, AI Second

AI Assisted Coding: Agile Meets AI—How to Code Fast Without Breaking Things, With Llewellyn Falco

In this BONUS episode we explore the practice of coding with AI—not just the buzzwords, but the real-world experience. Our guest, Llewellyn Falco, has been learning by doing, exploring the space of AI-assisted coding from the experimental and intuitive—what some call vibecoding—to the more structured world of professional, world-class software engineering. This is a conversation for practitioners who want to understand what’s actually happening on the ground when we code with AI.

Understanding Vibecoding

“You can now program without looking at code. When you’re in that space, vibecoding is the word we’re using to say, we are programming in a way that does not relate to programming last year.”

The software development landscape shifted dramatically in early 2025. Vibecoding represents a fundamental change in how we create software—programming without constantly looking at the code itself. This approach removes many traditional limitations around technology, language, and device constraints, allowing developers to move seamlessly between different contexts. However, this power comes with responsibility, as developers can now move so fast that traditional safety practices become even more critical.

From Concept to Working App in 15 Minutes

“We wrote just a markdown page of ‘here’s what we want this to look like’. And then we fed that to Claude Code. And 15 minutes later we had a working app on the phone.”

At the Agile 2025 conference in Denver, Llewellyn participated in a hackathon focused on helping psychologists prevent child abuse. Working with customer Amanda, a psychologist, and data scientist Rachel, the team identified a critical problem: clinicians weren’t using the most effective parenting intervention technique because recording 60 micro-interactions in 5 minutes was too difficult and time-consuming.

The team’s approach embodied lean startup principles turned up to eleven. After understanding the customer’s needs through exposition and conversation, they created a simple markdown specification and used Claude Code to generate a working mobile app in just 15 minutes. When Amanda tested it, she was moved to tears—after 20 years of trying to make progress on this problem, she finally had hope. Over three days, the team released 61 iterations, constantly getting feedback and refining the solution.

Iterative Development Still Matters When Coding With AI

“We need to see things working to know what to deliver next. That’s never going to change. Unless you’re building something that’s already there.” Continue reading AI Assisted Coding: Agile Meets AI—How to Code Fast Without Breaking Things, With Llewellyn Falco

BONUS: The Agile-Product Continuum: Phillip Starke’s Blueprint for Becoming a Product Coach

This episode was triggered by Phillip Starke’s blog post titled “Why agile coaches should strive to be product coaches”. You can read that blog post here, and listen in this episode to Phillip’s perspective and why he reached those conclusions.

Agile to Product Coaching Transformation

“Agile Coaches are already product coaches in essence, they just need to realize and embrace it fully.”

Phillip reflects on why agile coaches should also consider themselves product coaches. He argues for a unified approach to coaching that encompasses both agile and product development principles.

A Pivot Towards Product-Centric Coaching

“I had a turning point when I realized, despite our success, how much more impactful we could have been with a stronger focus on user interaction and discovery.”

Phillip shares his experience transitioning from an agile-focused to a product-centric coaching approach, highlighting a project where greater emphasis on user discovery would have led to even more significant outcomes.

Enhancing Product Development Through User Engagement

Continue reading BONUS: The Agile-Product Continuum: Phillip Starke’s Blueprint for Becoming a Product Coach

BONUS: Navigating the Path to SRE, A Guide to Adopting Site Reliability Engineering in Your Enterprise, with Vlad Ukis and Philipp Gündisch

Siemens Healthineers engaged Philipp and Vlad due to growing challenges with their platform. As more users started using the platform, the availability requirements increased, and they wanted to reduce downtime. However, their current operations capabilities did not allow them to achieve the uptime and availability needed, and there was also a problem with the time it took to recover from failures. To address these challenges, Siemens Healthineers decided to adopt SRE as a solution to improve their operations and increase reliability. The adoption of SRE was added to the list of big initiatives, and Vlad and Philipp worked through the organization to get buy-in and support for the change.

Overcoming Challenges in Adopting Agile, Scrum, SRE: A Journey in Change Management and Leadership

Continue reading BONUS: Navigating the Path to SRE, A Guide to Adopting Site Reliability Engineering in Your Enterprise, with Vlad Ukis and Philipp Gündisch

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