This year, we've seen a real-time experiment playing out across the technology industry, one in which AI's software engineering capabilities have been put to the test against human technologists. And although 2025 may have started with AI looking strong, the transition from vibe coding to what's being termed context engineering shows that while the work of human developers is evolving, they nevertheless remain absolutely critical. This is captured in the latest volume of the " Thoughtworks Technology Radar," a report on the technologies used by our teams on projects with clients. In it, we see the emergence of techniques and tooling designed to help teams better tackle the problem of managing context when working with LLMs and AI agents. Taken together, there's a clear signal of the direction of travel in software engineering and even AI more broadly. After years of the industry assuming progress in AI is all about scale and speed, we're starting to see that what matters is the ability to handle context effectively.

This means software developers are going to become more important to how the world builds and maintains software. Yes, there are many ways their practices will evolve thanks to AI coding assistance, but in a world of proliferating machine-generated code, developer judgment and experience will be vital. Research done by GitClear earlier this year indicates that with AI coding assistants (like GitHub Copilot) going mainstream, code churn -- which GitClear defines as "changes that were either incomplete or erroneous when the author initially wrote, committed, and pushed them to the company's git repo" -- has significantly increased. GitClear also found there was a marked decrease in the number of lines of code that have been moved, a signal for refactored code (essentially the care and feeding to make it more effective). In other words, from the time coding assistants were introduced there's been a pronounced increase in lines of code without a commensurate increase in lines deleted, updated, or replaced.

Technology use often goes wrong, Parsons notes, "because we're too focused on either our own ideas of what good looks like or on one particular audience as opposed to a broader audience." That may look like an app developer building only for an imagined customer who shares his geography, education, and affluence, or a product team that doesn't consider what damage a malicious actor could wreak in their ecosystem. "We think people are going to use my product the way I intend them to use my product, to solve the problem I intend for them to solve in the way I intend for them to solve it," says Parsons. "But that's not what happens when things get out in the real world." AI, of course, poses some distinct social and ethical challenges. Some of the technology's unique challenges are inherent in the way that AI works: its statistical rather than deterministic nature, its identification and perpetuation of patterns from past data (thus reinforcing existing biases), and its lack of awareness about what it doesn't know (resulting in hallucinations).

The reason we can't just wish away or "fix" complexity is that every solution--whether it's a technology or methodology--redistributes complexity in some way. When microservices emerged (a software architecture approach where an application or system is composed of many smaller parts), they seemingly solved many of the maintenance and development challenges posed by monolithic architectures (where the application is one single interlocking system). However, in doing so microservices placed new demands on engineering teams; they require greater maturity in terms of practices and processes. This is one of the reasons why we cautioned people against what we call "microservice envy" in a 2018 edition of the Technology Radar, with CTO Rebecca Parsons writing that microservices would never be recommended for adoption on Technology Radar because "not all organizations are microservices-ready." We noticed there was a tendency to look to adopt microservices simply because it was fashionable.

What do technology leaders and professionals need to do to help their organizations achieve the holy grail of continuous intelligence? Look to artificial intelligence and machine learning to pave the way. However, achieving a state of continuous intelligence isn't an overnight sprint by any means -- many organizations aren't quite ready to bring together the adroit data management, automation, processes and skills needed to make things happen. That's the word from a three-part series published by ThoughtWorks, which advocates an approach it calls Continuous Delivery for Machine Learning (CD4ML), "a software engineering approach in which a cross-functional team produces machine learning applications based on code, data, and models in small and safe increments that can be reproduced and reliably released at any time, in short adaptation cycles." Employing data "to produce tangible outcomes for business is the real value driver and for that, we are seeing the world moving more towards intelligence," write Ken Collier, Mark Brand and Pramod N, all with ThoughtWorks.

ABOUT: You want to include a machine learning component in your IT systems? The process is a little more involved than clicking through an AI tutorial on your laptop. It's not just the first working model you run that you need to consider; you also need to think about things like integration, scaling, and testing. What's more, postlaunch, you'll want to continuously adapt your model to respond to the changing environment. Christoph and Arif will give an introduction into Continuous Delivery for Machine Learning (CD4ML) - a set of tools and processes that ensure that software under development in Machine Learning can be reliably released to production at any time and with high frequency.

Business today is more than simply matching traditional competitors, it's about exploiting digital technologies to create new opportunities, and being able to repeat this. The economy is quickly going digital and Australian businesses must evolve into Modern Digital Businesses (MDBs) which strategically use intelligence assets to improve operations and deploy new products and services, in order to stay competitive and create value for their customers. A group of digital business leaders recently gathered at ThoughtWorks Live in Sydney and Melbourne, to share their insights into how organisations can take advantage of data to adapt and thrive in the digital economy. This report includes strategic and practical advice taken from the event for any business leader – regardless of their organisation's digital maturity – on best practices for taking advantage of data and driving change. A Continuous Intelligence (CI) framework starts with the process of acquiring data and, with the help of analytics and machine learning, derive insights from it to be able to make confident decisions and actions – which are in turn reviewed and validated, to ensure the organisation continuously improves its decision-making capabilities. Steps organisations can take to apply CI to building an MDB, which is agile and technology-driven are also covered.

With 2017 fast approaching, technology trends that will keep gathering steam in the new year range from augmented and virtual reality to machine intelligence, Docker, and microservices, according to technology consulting firm ThoughtWorks. The data is based on reports ThoughtWorks' consultants are seeing out in the field. ThoughtWorks sees natural language processing tools like Nuance Mix and hardware providing for natural interactions having a "huge" impact on AR and VR adoption. AR differs from VR in that users still can see the world around them rather than being completely immersed in a virtual space; of the two, AR is likely to be most interesting to businesses. "One excellent application is remote expert systems," said Mike Mason, technology activist at ThoughtWorks.