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.
Vibes, antipatterns, and new innovations
In February 2025, Andrej Karpathy coined the term vibe coding. It took the industry by storm. It certainly sparked debate at Thoughtworks; many of us were skeptical. On an April episode of our technology podcast, we talked about our concerns and were cautious about how vibe coding might evolve.
Unsurprisingly given the implied imprecision of vibe-based coding, antipatterns have been proliferating. We’ve once again noted, for instance, complacency with AI generated code on the latest volume of the Technology Radar, but it’s also worth pointing out that early ventures into vibe coding also exposed a degree of complacency about what AI models can actually handle — users demanded more and prompts grew larger, but model reliability started to falter.
Experimenting with generative AI
This is one of the drivers behind increasing interest in engineering context. We’re well aware of its importance, working with coding assistants like Claude Code and Augment Code. Providing necessary context—or knowledge priming—is crucial. It ensures outputs are more consistent and reliable, which will ultimately lead to better software that needs less work — reducing rewrites and potentially driving productivity.
When effectively prepared, we’ve seen good results when using generative AI to understand legacy codebases. Indeed, done effectively with the appropriate context, it can even help when we don’t have full access to source code.
It’s important to remember that context isn’t just about more data and more detail. This is one of the lessons we’ve taken from using generative AI for forward engineering. It might sound counterintuitive, but in this scenario, we’ve found AI to be more effective when it’s further abstracted from the underlying system — or, in other words, further removed from the specifics of the legacy code. This is because the solution space becomes much wider, allowing us to better leverage the generative and creative capabilities of the AI models we use.
Context is critical in the agentic era
The backdrop of changes that have happened over recent months is the growth of agents and agentic systems — both as products organizations want to develop and as technology they want to leverage. This has forced the industry to properly reckon with context and move away from a purely vibes-based approach.
Indeed, far from simply getting on with tasks they’ve been programmed to do, agents require significant human intervention to ensure they are equipped to respond to complex and dynamic contexts.
