Thoughtworks Technology Podcast

The way we think about and build with data is changing. Data lakehouses have become an industry standard, and conversation is turning to semantic and governance layers as organizations seek to optimize and leverage data for the AI era. However, the concept of 'data products' — treating data as something that needs to be deliberately managed and packaged in a way that's easily usable and consumable for the people that need it — remains crucial.
On this episode of the Technology Podcast, host Nate Schutta is joined by Thoughtworks colleague and Data and AI Lead Lorisa Perdoci and Lauren Hartman, Cloud Solution Architect at Microsoft, to discuss everything from data products to data agents. They also explore Microsoft Fabric, discussing how its evolution in 2026 to include new features and capabilities built for AI agents and today's data integration and governance challenges, reflects the direction of travel in this field.

The agentic era of software engineering began almost a year ago. And while much has changed in 2026, there's still a lot that hasn't been settled. One of the most important issues is how teams should collaborate. The idea of working with a spec may have considerable allure, but many questions remain: How should it be defined? Who owns it? How do we iterate with it? The fear of retreating back into waterfall is very real and requires vigilance.
On this episode of the Technology Podcast, hosts Ken Mugrage and Caer Sanders are joined by former Thoughtworkers Cassie Shum (formerly VP of Ecosystem and Product Engineering at RelationalAI) and Tim Cochran (recently a Principal at AWS) to discuss their new company Praxa Inc., a venture shaped by the industry's need to rethink the way we collaborate and structure our work in a world of AI agents. They discuss today's challenges, how software engineering is changing and how Praxa can help organizations navigate this period of evolution.
Learn more about Praxa: https://teaser.getpraxa.ai/

We've seen a huge amount of increase in interest around open-weight models in 2026. The reasons for this are multifaceted, ranging from the switch to per token billing, the evolving privacy landscape and improvements in open-weight model capabilities.
To unpack open-weight models and explore why and when we might want to use them, host Ken Mugrage is joined by Thoughtworkers Caer Sanders and Andre Almar. They discuss everything from the precise definition of the term and how they compare to other kinds of models, to governance and hardware challenges.

We've been thinking a lot recently about the status of code in a world where it's increasingly written and read by AI. Could the spec, for instance, become the primary way we interface with software systems? And if it does, will that mean humans no longer need to look at code at all? 
At present, the idea of not looking at our code seems fanciful, possibly dangerous, but even within Thoughtworks there are conflicting perspectives about whether this might change. To some, code will always remain the primary artifact; to others, its role in how we build software is about to undergo a significant transformation.
On this episode of the Technology Podcast, Thoughtworks' Caer Sanders and Razin Memon join host Ken Mugrage for a debate about how the relationship between developers and code might — or might not — change in the final years of this decade. For Caer, despite the clear capabilities of AI, paying attention to code will always matter; for Razin, meanwhile, code's relevance is likely to decline as harnesses become more sophisticated and specification techniques evolve. 
Whatever your view, listen for a frank and open discussion about an issue that will ultimately determine what the future of software engineering actually looks like.
Listen to our episode on harness engineering from May 2026: https://www.thoughtworks.com/insights/podcasts/technology-podcasts/what-harness-engineering
Read a recent blog post on thoughtworks.com arguing we still need to design code for humans: https://www.thoughtworks.com/insights/blog/programming-languages/should-still-design-code-humans

Harness engineering is still a new concept, but already we've noticed a challenge being consistently faced by technology leaders: how can a harness be used at scale across an organization to ensure consistency and controllability without undermining autonomy? To some extent it's a discussion that's reminiscent of platform engineering and, before that, DevOps, but, given the nature of AI agents, it's also unique to this particular moment.
On this episode of the Technology Podcast, host Ken Mugrage is joined by guests Thomas Squeo (Chief Technology Officer and Head of Advisory for the Americas) and Matt Kamelman (Innovation Choreographer) to explore why it's so important to scale harnesses effectively and the steps and processes that will allow you to do just that. Thomas and Matt recently wrote an article outlining how organizations should think about what they call 'enterprise harness engineering'; here, they unpack their ideas and explore what's required of engineering leaders and their teams.
Read Thomas' and Matt's article: https://www.thoughtworks.com/insights/articles/operating-system-enterprise-ai