Thoughtworks Technology Podcast

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.

At the end of June, Thoughtworks and Martin Fowler convened an unconference-style event in Switzerland with a range of industry leaders. The aim was to reflect on the current challenges and learning around AI assisted and agentic software engineering and discuss the implications for the future — and, most importantly, what actions need to be taken today.
To review the event and dive into some of the topics that surfaced, host Ken Mugrage is joined by Thoughtworks colleagues Kief Morris (author of Infrastructure as Code) and Andrew Harmel-Law (author of Facilitating Software Architecture). They highlight some of the key ideas and issues that emerged from the event's conversations, ranging from developer identity to harness engineering.
Learn more about the European edition of the Future of Software Engineering Retreat: https://www.thoughtworks.com/about-us/events/the-future-of-software-development-europe-2026
Read Andrew's recent blog post that asks whether non-functional requirements could be a vital guardrail for AI-generated code: https://www.thoughtworks.com/insights/blog/architecture/non-functional-requirements-missing-guardrail-ai-generated-code

What is code? It might sound obvious, but if you scratch the surface it becomes more difficult to articulate precisely what we mean. AI is complicating the picture further and changing the relationship developers have with code: when large amounts of executable code can be generated from high-level descriptions, what does it even mean to write code? 
On this episode of the Technology Podcast, host Alexey Boas is joined by Thoughworks Distinguished Engineer Unmesh Joshi to discuss what code actually is and what it means to write, test, review and maintain code today. Building on Unmesh's recent article 'What is Code?' for martinfowler.com, this discussion dives into one of the fundamental building blocks of software while also thinking through the implications for 2026's engineering challenges.
Read Unmesh's article on martinfowler.com: https://martinfowler.com/articles/what-is-code.html

Database branching has, for a long time, been a troublesome piece in the modern developer workflow puzzle: a good idea in principle but in practice a slow and often expensive challenge. Get it right and you can accelerate productivity and remove bottlenecks; get it wrong and you're potentially creating all sorts of trouble for yourself, from privacy risks to additional complexity.
However, things are changing. Thanks to the emergence of new platforms such as Neon, Supabase and Databricks Lakebase, branching a database can become as familiar to developers as managing code branches and multiple environments with, say, Git and Terraform. 
On this episode of the Technology Podcast, host Ken Mugrage is joined by his Thoughtworks colleague Cam Casher and Databricks' Kevin Hartman to discuss the work Thoughtworks and Databricks have been doing together on Lakebase. They discuss the platform, their experience using it with Spotify's Backstage and the opportunities database branching can offer software engineering teams in an increasingly AI-assisted and agentic world.
Read Cam and Kevin's recent series on using Databricks Lakebase with Backstage: https://www.thoughtworks.com/insights/blog/data-engineering/backstage-lakebase-databricks