Technology Radar | Guide to technology landscape | Thoughtworks
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Technology Radar Vol 34
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Volume 34 | April 2026
Technology Radar
An opinionated guide to today's technology landscape
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Insights<br>Back
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Thoughtworks Technology Radar is a twice-yearly snapshot of tools, techniques, platforms, languages and frameworks. This knowledge-sharing tool is based on our global teams’ experience and highlights things you may want to explore on your projects.
Adopt<br>Trial<br>Assess<br>Caution
Adopt<br>Trial<br>Assess<br>Caution
Adopt<br>Trial<br>Assess<br>Caution
Adopt<br>Trial<br>Assess<br>Caution
Adopt<br>Trial<br>Assess<br>Caution
Adopt<br>Trial<br>Assess<br>Caution
Adopt<br>Trial<br>Assess<br>Caution
Adopt<br>Trial<br>Assess<br>Caution
New
Moved in/out
No change
No blips
No blips
No blips
techniques
platforms
tools
languages-and-frameworks
Each insight we share is represented by a blip. Blips may be new to the latest Radar volume, or they can move rings as our recommendation has changed.
The rings are:
Adopt . Blips that we think you should seriously consider using.
Trial . Things we think are ready for use, but not as completely proven as those in the Adopt ring.
Assess . Things to look at closely, but not necessarily trial yet — unless you think they would be a particularly good fit for you.
Caution . The industry should consider alternative options or even proactively avoid these things. We’ve had negative experiences that have impacted our work.
Explore the interactive version by quadrant, or download the PDF to read the Radar in full. If you want to learn more about the Radar, how to use it or how it’s built, check out the FAQ.
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Themes for this volume
For each volume of the Technology Radar, we look for patterns emerging in the blips that we discuss. Those patterns form the basis of our themes.
The challenge of evaluating technology in an agentic world
AI is changing not only technology but also how we assess and evaluate it. While assembling this volume of the Technology Radar, we noticed that evaluating technology is becoming harder as the industry adopts AI. One contributing factor is semantic diffusion: the rapid emergence of new terms for evolving practices, often before their meanings have stabilized. For example, terms such as spec-driven development and harness engineering are sometimes used inconsistently or overlap in meaning. Without shared definitions, it’s difficult to determine whether we’re seeing distinct techniques or simply different labels for similar ideas. Distinguishing between a mature, standalone engineering methodology with clear guardrails from the everyday use of AI tools such as coding assistants is an ongoing difficulty.
The challenge extends beyond semantics. The pace of change compounds this uncertainty. We encountered several tools that were less than a month old — some promising, but ultimately too young to assess. In many cases, these tools were maintained by a single contributor working with a coding agent. AI has lowered the barrier to building developer tooling, creating a constant stream of new tools. This stretches the traditional rhythm of the Radar: if we allow tools time to mature, our guidance risks becoming outdated; if we move too quickly, we risk highlighting trends that disappear just as fast. It also raises questions about sustainability: when something can be created quickly and with relatively little effort, what ensures continued investment in maintaining and evolving it?
This environment also risks creating codebase cognitive debt. As more code is generated by AI, it’s easier to adopt solutions without developing the mental models needed to understand how they work. Over time, this gap in understanding accumulates, making systems harder to reason about, debug and evolve.
Retaining principles, relinquishing patterns
An interesting consequence of AI in software development is that it’s not only forcing us to look to the future; it’s also pushing us to revisit the foundations of our craft. While assembling this edition, we found ourselves returning to many established techniques, from pair programming to zero trust architecture, and from mutation testing to DORA metrics. We also revisited core principles of software craftsmanship, such as clean code, deliberate design, testability and accessibility as a first-class concern. This is not nostalgia, but a necessary counterweight to the speed at which AI tools can generate complexity. We also observed a resurgence of the command line: After years of abstracting it away in the name of usability, agentic tools are bringing developers back to the terminal as a primary...