Thoughtworks Technology Podcast

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 (now VP of Ecosystem and Product Engineering at RelationalAI) and Tim Cochran (recently at AWS and now CTO at Praxa Inc) to discuss how we need to rethink the way we collaborate and structure our work in a world of AI agents. Cassie and Tim have been grappling with these issues in their own work this year, and have both been thinking through how the industry can help developers successfully leverage agents while retaining the necessary autonomy to deliver quality software.
Listen to this conversation for grounded perspectives on spec-driven development and agentic software engineering.

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

Cloud was one of the main drivers of the early waves of AI adoption. However, as AI has become more and more embedded in systems and devices — in both consumer and enterprise contexts — it's becoming a bottleneck. This isn't just about costs (although yes, that issue is certainly surging up the agenda), it's also about how we optimize our architectures and improve device performance.
This is why conversation is turning to hybrid AI: embracing a hybrid approach that combines proprietary cloud services with local or on-device AI can help organizations deliver better experiences for users, whether they're consumers, other businesses or internal teams.
One company exploring this space is electronics giant Lenovo. In this episode of the Technology Podcast, host Prem Chandrasekaran is joined by Girish Hoogar, Lenovo's Global Head of Technology for Cloud and Software, to discuss why the company is embracing hybrid AI, how it's approaching implementation and what the implications are for other technologists.
As industry attention turns to local AI, listen for a first-hand perspective on what the trend actually means for engineering teams and their organizations.