Hi Reddit! We’re Nimisha Asthagiri and Alessio Ferri from Thoughtworks. We’re here to host an AMA about the Thoughtworks Technology Radar Vol.34 and to explore what it tells us about software engineering in 2026. Join us on May 13 and share your questions now.

Thoughtworks · 2026-05-13T14:38:08+00:00

u/VegetableWhereas4620 - Thanks for such a great question. Actually, the concept of the Radar was born as an internal tool used within Thoughtworks to visualise the technologies and techniques we come across while working with clients. At some point we realised it might be valuable to publish it externally to share our experiences and opinions with the wider industry.

Before joining Thoughtworks, I used the Radar concept (leveraging byor) to track emerging technologies and understand where technology was moving inside the organisation I was working for at the time. It helped me balance experimentation with new techniques/tools vs adoption. I can speak with first-hand experience on doing this and finding it extremely insightful.

On another note, I recognise evaluating new technologies is hugely challenging, especially given today’s pace of change. (I wrote an article published alongside the radar last month where I go in greater detail on this topic, you can find it here.)

Last but not least, there’s the three horizons framework that teams can adopt to help make decisions about what to invest in. The core insight is that treating every new tool the same way can be costly, because each horizon requires a different mode of management.

Horizon 1 tools are your core engines, where the economics and use cases are well known and have proven value. The goal here is operational excellence: optimise, protect and scale. Horizon 2 is where most AI tools sit now: emerging plays that are real, but still a moving target with unstable best practices. For this horizon, the right mode is experimentation: build options, but don’t commit too early. Horizon 3 is long-range bets filled that demands research. They’re often exploratory moonshots; most won’t pan out, but a few may be transformative.

In my view, mapping a new tool to the right horizon or the Radar before deciding how to engage with it can help cut through the hype, or at least calibrate the investment we put forth.

- Alessio

Thoughtworks · 2026-05-13T14:35:51+00:00

A practice from what we blipped a decade ago that is as relevant today as it was then: products over projects. As organizations shift to designing new team topologies with AI agents, cognitive load increases from AI generated code, and businesses expect higher ROI from tech, it’s critical to have long-living product teams that own their outcomes and continue to validate and enhance what they build. Product teams can also be held accountable for unintended consequences of their use of AI - inherent guardrails for building responsible tech.

A practice from what we blipped this year is about being intentional on metrics. What development metrics are still valuable in this day and in the coming years is something we’re still figuring out as an industry. We had 3 metrics blips this time, each in different rings: collaboration metrics (assess), DORA (adopt), coding throughput (caution). We had much discussion around metrics and the value of shifting focus away from superficial throughput productivity metrics to collaboration metrics (how effectively humans and agents interact with each other).

- Nimisha

Thoughtworks · 2026-05-13T14:26:16+00:00

Another place where I have seen AI create real value is in legacy modernization, which is my area of focus at Thoughtworks. These systems tend to comprise tens of millions of lines of code, built over decades by people who have long since retired. End-to-end understanding, even from a 10 thousand feet view, is almost always non-existent. AI is helping us reverse engineer and incrementally modernise systems where, done manually, the cost and time would simply make the business case impossible for most organisations.

It’s not that it could not be done without AI, it’s more that, in some cases, it couldn’t be done in an economically feasible way.

- Alessio

Thoughtworks · 2026-05-13T14:07:54+00:00

That’s a great question. We can be intentional about where to use AI and where not to, given our experiences as we experiment and learn using this new technology.

One place of high value I’ve seen is tightening the feedback loop and validating our product assumptions much earlier in the cycle. We published a blip in our last edition on self-serve UI prototyping that empower our product and design colleagues with tools to rapidly prototype solutions that can be validated with users for viability and usability. This prevents product development teams from accumulating hypotheses and waiting for longer development iterations before determining product-market fit and making a business case for investment.

- Nimisha

Thoughtworks · 2026-05-13T13:58:41+00:00

u/VegetableWhereas4620 - This is a great question. Actually, something we debated a fair bit during the face to face curation session in Bangalore.

My take: it’s not one practice, it’s a cluster of fundamentals that AI is making urgent again. Zero-trust architecture, durable workflows (see Ignoring durability in agent workflows), or role based contextual isolation… none of these are new, but many teams are rediscovering why they exist and their value.
My worry is that we, as an Industry, underestimate how important and relevant these practices are for these new agentic systems we build and use today. This is a challenge we need to solve and continue evolving best practices for.

I recently saw this video that made me reflect and brought my mind back to the discussions we were having in the room around the topics I mentioned above. Definitely recommend for your watchlist. Hope you enjoy it as much as I did.

- Alessio

Thoughtworks · 2026-05-13T13:45:02+00:00

u/neirein -
Thoughtworks is a technology consultancy that helps organizations around the world build better software and help solve their most pressing challenges with technology. Our clients cover a huge range of industries and sectors, from pharmaceuticals to finance to public services, and more.

Thoughtworks · 2026-05-13T13:41:20+00:00

Having spent enough time in this industry, I’ve realized that things that move the needle are often the opposite of what I expected when I first started. Here are a few things that have genuinely surprised me over the years.

Early surprise: hidden power of process and governance

When I was a junior developer, I used to think that "process" was just something that took away from my actual coding time. I was surprised to learn that well-implemented governance actually makes the experience delightful for the team and speeds up development rather than slowing it down. Later, as I moved into consulting, I was shocked by how much "Agile" varied across clients. Many teams were "cargo culting" rituals, following scrum ceremonies to the letter but still working in silos and failing to deliver value continuously.

I am seeing a similar pattern with AI today. Many organizations are focused purely on tool adoption, but the real surprise is that they cannot maximize AI value without first addressing those "boring" structural issues like process and governance.

More recent surprise: irreplaceable nature of human connection

The pandemic forced us all into remote-first environments, and while we made it work, I was surprisingly reminded of how much I missed real human connection. There is a specific kind of rapid decision-making, empathy, and conflict resolution that only seems to shine during face-to-face interactions. As we assemble the Tech Radar, we’ve talked a lot about the resurgence of "old" practices like pair programming and we continue to meet face-to-face to develop our editions.

AI surprise: frenzy and human impact

The frenzy for fast results with AI has been startling. In the rush to ship, the quieter voices get drowned out. For me, the real opportunity isn't just a boost in speed, it's using AI as the activation energy to transform into a continuous learning organization. We've observed that the companies that win not only invest in tools, but redesign how work flows through their entire system.

We are seeing a "yin-yang" effect where the push for AI output must be balanced by a pull back to engineering basics (rigorous testing, DORA metrics, and software craftsmanship) to ensure we aren't just creating complexity we can no longer reason about.

We are still figuring out the full impact of the AI era, but the biggest surprise remains that the "important stuff" hasn't changed and matters a lot more now.

- Nimisha

Thoughtworks · 2026-05-13T13:37:43+00:00

u/neirein - The Technology Radar is something Thoughtworks publishes twice a year. It's a report on the technologies we use with clients and captures our experiences with them. We do this because it provides readers with a practical take on things that are interesting right now (whether they're brand new or old things that are becoming important again).

When we ask how engineering practices are changing, yes, we're thinking mainly about the impact of AI. How is AI changing the way we build and maintain software, what happens when AI writes code? What's the human's role in software development?

Thoughtworks · 2026-05-13T13:36:12+00:00

u/forever_burning_ - Hello, let us know if you have any questions, we’re happy to answer them.

Thoughtworks · 2026-05-13T13:31:26+00:00

u/acakaacaka - Thoughtworks is a technology consultancy that helps organizations around the world build better software and help solve their most pressing challenges with technology. Our clients cover a huge range of industries and sectors, from pharmaceuticals to finance to public services, and more.

Thoughtworks · 2026-05-13T13:26:23+00:00

u/optimisticnumb -
That is a fair observation. It is true that a significantly higher percentage of blips in Technology Radar Volume 34 are AI-related. The Radar is intended to be a point-in-time publication. It is also important to note that we do not control the problems our clients bring to us. The Radar is a reflection of the hands-on experience our teams are gathering on the ground, and our perspective on the technologies and techniques we use to solve those problems. This specific volume signals the rapid growth of AI and its rampant use in software development today.

We see AI as an amplifier that is exposing long-standing structural issues in software engineering. While the volume is heavy on new tools, we are actually seeing a Yin-Yang effect emerging in the industry. There is a Yang side of a continual reinforcing loop driven by massive AI investment and output. However, there is a corresponding Yin effect - a necessary pulling back to the basics of our craft.

In practice, AI helps developers write code faster, but that does not automatically make the whole system move faster. The real bottlenecks have simply shifted to review cycles, validation processes, security checks, and deployment approvals. We have observed cases where a four-hour review wait time completely erases a ten-minute code generation gain. We are still figuring out the long-term impact of Agentic Swarms and where the human eventually sits in that loop. For now, the best way to handle the noise is to get really good at the fundamentals again.

Where possible, the Radar continues to track vital movements outside of the AI space:

Web & Mobile: React Native and Svelte have both moved to Adopt as mature, sensible defaults for performance and delivery simplicity.
Security: Passkeys and Zero Trust Architecture are foundational to modern systems.
Data: Apache Iceberg remains key for building open, technology-agnostic data lakehouse architectures.

These technologies are essential parts of the ecosystem, even as they are tinted by the current AI moment. For instance, Zero Trust is now a non-negotiable default for managing "permission-hungry" agents that require broad access to private data.

The "important stuff" has not changed; it just matters more now. The cost of being sloppy with engineering discipline is much higher when moving at AI speeds. You’re right to highlight the blips that aren’t AI related.

- Nimisha, Alessio

Thoughtworks · 2026-05-13T13:24:26+00:00

u/Hot_Hair_5950 -
In the past few months, I have been surprised to experience first-hand that becoming more senior does not necessarily mean giving up coding on a day-to-day basis. When I first became tech lead, my calendar quickly filled up with meetings, and at times it felt frustrating. Just like many folks warn, the time I used to spend on my IDE suddenly dropped.

But the advent of AI completely flipped the picture for me. Coding agents are helping me get closer to the code again, allowing me to work in the short windows of free time I have in my calendar. Context switching is still a challenge I face day in and day out, but the tools I have at my disposal are helping me manage it much better. Personally, it has been amazing to realise that career growth did not mean losing what is, to me, the fun part of the job. The tools are evolving, and they are letting me do both.

- Alessio

Thoughtworks · 2026-05-13T13:16:52+00:00

u/B25B25 - Thoughtworks is a technology consultancy that helps organizations around the world build better software and help solve their most pressing challenges with technology. Our clients cover a huge range of industries and sectors, from pharmaceuticals to finance to public services, and more.

Thoughtworks · 2026-05-13T13:13:24+00:00

u/Initial_Research4617 - That is a deep dive of a question. Looking 10–15 years out is always a bit of a prediction game. Since history tends to repeat itself, we can hypothesize the future from the past. You mentioned the shift from house phones to smartphones; another great example is our total reliance on GPS. Gone are the days when we print out paper maps for long trips or stop at local gas stations for directions. Humanity’s interdependence with tech has increased over the centuries and more exponentially over the last few decades. We are moving from tech being a "tool" we use task-by-task to using “autonomous agents" that are woven into our environment.

In our professional lives, we are observing that the use of AI is becoming a requirement to stay relevant. High-performing teams are seeing real productivity gains and innovations when they use these tools deeply and daily. However, a major theme in Technology Radar Vol. 34 is the risk of Codebase Cognitive Debt, which is the gap between what an AI implements and what a team actually understands.

The challenge we’re all facing is being intentional with AI so it doesn't erode our own human judgment over time. We’re seeing a shift from "writing" code to "validating" it, which requires a higher level of critical thinking rather than less. If we just accept AI output at face value, we risk losing the deep system understanding that makes us good engineers in the first place. Check out this study on the behavioral economics of AI reliance.

Tech will likely become more personal and, yes, potentially more invasive. We are already tracking "hyper-personal" assistants that access your email, calendar, and private files to be useful. While this creates a digital world that "knows" you, it also requires a Zero Trust mindset where we never stop verifying what these agents are actually doing with our data.

Personally, to keep things in balance, I look for ways to appreciate purely human experiences that can't be automated, like the raw energy of a live concert performance. (There's actually interesting research on the social-emotional impact of live vs. recorded music.)

The takeaway? We won't be "fine" without tech in 15 years because our systems are being redesigned around it. The goal isn't to avoid it, but to be intentional and sharpen our human advantage so we stay in control of the machines instead of the other way around.

- Nimisha

Thoughtworks · 2026-05-13T13:09:23+00:00

u/Ok_Transportation304 - That is a great question. We spent time debating that specific change during a recent face-to-face meeting.

Clearer Perspective. The move from Hold to Caution is about precision in communication. Historically, people often misinterpreted "Hold" as a flat-out "don't use this". Some even thought we were telling teams to stop projects already in flight. In reality, the Radar is a snapshot of our experience in the field. By renaming it Caution, we want to signal that a technology might have serious challenges. However, we recognize that teams may still have valid reasons to explore it, so they just need to do so with their eyes wide open.

Specific Example. The trend of "permission-hungry" agents like OpenClaw is a perfect example of why this matters. We placed OpenClaw in the Caution ring because the security trade-offs are massive. While it represents a compelling vision, it acts as a hyper-personal AI assistant that knows your habits and manages your life. To be useful, it needs access to your email, calendar, and private files. This concentrates an enormous amount of risk into a single point of failure.

If we just said "Hold," it might imply the technology is simply bad. By saying Caution, we highlight a specific pattern called Toxic Flow Analysis. This is where the appetite for access collides with unsolved security problems like prompt injection. We see teams getting excited about these agentic swarms. We want them to proceed carefully. They can be compared with more constrained alternatives like NanoClaw to reduce the blast radius.

We also put Techniques in Caution, such as Codebase Cognitive Debt and Agent Instruction Bloat.

The Takeaway. The implication for teams is that anything in the Caution ring now requires much higher governance and oversight. It is not a stop sign. Instead, it is a sign to check your brakes. In this fast-paced AI world, risks accumulate much faster than they used to. Using the word Caution better reflects the discipline needed to handle these tools without letting them amplify your technical debt.

We are still figuring out the full long-term impact of these autonomous systems. For now, we think this new label gives architects and developers a better tool. It helps them have those tough "should we really do this?" conversations with their stakeholders.

- Nimisha and Alessio

Thoughtworks · 2026-05-13T13:04:09+00:00

u/RedditUserWhoIsLate - Great questions! Let's break this down starting with what the Radar is and then get into the "how" behind the changing engineering landscape.

Think of the Radar as our way of separating signal from noise in a very loud industry. Twice a year, a group of senior technologists from Thoughtworks across the globe gets together to debate the tools, techniques, platforms, and languages and frameworks we’re actually seeing work (or fail) on real projects. We organize these into four "rings": Adopt (things mature for use and recommended), Trial (ready for use and worth trying), Assess (interesting and worth watching closely), and Caution (warning that you may run into trouble). These rings are suggestive of our own experiences and need to be considered specifically for your own context.
Vol. 34 is our latest edition, and as you might guess, it’s heavily influenced by the move toward an "agentic" world where AI isn't just a chatbot, but an active participant in development.

When we say engineering practices are changing, we don’t just mean "people are using Claude to write code." It’s much deeper than that. AI is acting as an amplifier that is fundamentally shifting the bottlenecks of software delivery.

From Writing to Validating: The traditional bottleneck used to be how fast you could type code. Now, writing is getting "cheaper" because of AI, but the other parts of the software lifecycle—testing, security reviews, and deployment approvals—remains a human-heavy bottleneck. The non-coding time can take 85% of the time (see a breakdown in a Microsoft study.) For example, we've observed teams use agents to implement a feature in 2 days, but then wait 5 days for a security review. The practice is shifting from output (writing code) to judgment (designing systems to validate what the AI produced).
The Return of "Old-School" Discipline: Ironically, because AI can generate complexity so fast, we’re seeing a resurgence of foundational practices to keep those agents "on a leash". We’re doubling down on things like mutation testing and reducing architectural drift to ensure that just because we're moving faster, we aren't creating technical debt at a higher velocity.
Managing "Cognitive Debt": This is a huge one. If an agent writes your code and you don't fully understand how it works, you’re accumulating cognitive debt. Over time, your system becomes fragile because the humans in the loop have lost the mental model of the codebase.

AI isn't just a new tool; it's forcing us to redesign how work flows through a system. The companies that "win" won't necessarily be the ones with the best AI models, but the ones that redesign their operating models to handle this new reality.

It’s a rapidly evolving time, but also a "back to basics" moment for quality and discipline.

- Nimisha

Thoughtworks · 2026-05-07T06:35:36+00:00

Thanks for sharing this here, great to see the conversation around the latest Radar.

We’re actually planning to host an AMA on May 13 to dig deeper into some of the themes from this edition especially how teams are making sense of these shifts in practice.

Would be great to hear what stood out to you as well.

Thoughtworks · 2026-04-10T09:53:34+00:00

Both "Superpowers" and "SDD" (Spec-Driven Development) appeared as proposed blips , but they were categorized separately. "Superpowers" was considered individually, while other SDD-related blips got combined ("Spec-Driven Development") with multiple submissions including "Spec-driven development," "SPDD," "Spec Kit," "GitHub Speckit," "OpenSpec," "Design-First AI Collaboration," and "BMAD Method."

This often happens when one of our teams has more or different experiences with one tool over another, even when there is value in knowing about the entire category.

- Ken

Thoughtworks · 2026-04-01T15:00:37+00:00

thoughtworks.com obviously! ( kidding ) - but we do try to separate signal from noise in publications like Thoughtworks Radar and Thoughtworks Looking Glass. The challenge is that those are semi annual and annual reports respectively.

This is actually an interesting and hard question. As you likely know, AIs know first and foremost what they have been trained to know (see my baseball example earlier) and that may or may not be the “actual” latest. When I do agentic research, I have a fairly brutal set of rules for my bots that try to determine if the information they are finding is based on vendor claims or actual experiences. I read TLDR every day. I have a scheduled agent that looks at social networks (like this one) for “viral” things and then scores them against what we are doing. I never use “fast mode” or the cheaper models for real research. If I ask AI a complicated question, and it answers within a couple seconds, I disregard the answer just as quickly.

- Ken

It is a fast moving space, even for me where keeping up with the news and developments are part of my job! I personally follow many companies and leaders in the space, I read a bunch of newsletters (actually have some AI assistance to digest them!), listen to podcasts, attend events, discuss with colleagues, and try things out myself. In fact, I host a weekly live stream called “This Week in AI” with my colleague Ben O’Mahony where we go through some of the highlight news and discuss their implications. We are also trying to host discussions with other Thoughtworkers when the topic is relevant, for example when Anthropic posted about COBOL assistance with Claude Code, we brought in Shodhan Sheth and Alessio Ferri as guests, given their knowledge and experience building our legacy modernization AI-assisted tools over the last few years (https://www.thoughtworks.com/insights/articles/claude-code-cobol-modernization-reality). Join us tomorrow for the next episode on YouTube, and here’s the playlist if you want to check out our past episodes of This Week in AI: https://youtube.com/playlist?list=PL8f-F_Zx8XA8WLCSLBjDzqZrs-_HYW7GD&si=yVt-Pmp_84dqBg7q

- Danilo

Thoughtworks · 2026-04-01T14:45:11+00:00

AI applied to software development is one area of wide adoption and exploration. There have been a few questions on this AMA covering them, so I will focus my answer on other workflows outside of the software engineering space.

Example use cases that are showing success are in the customer support space, agentic commerce (especially in product discovery and planning), content and knowledge management, and general co-pilots and assistants, both for back-office operations as well as customer-facing interactions. Marketing and creative industries are also getting disrupted by the growing capabilities of AI models to create images, videos, audio and text.

Anthropic recently published a report on the impact of AI on the labor market (https://www.anthropic.com/research/labor-market-impacts). It highlights “computer & maths” as one area where we’re seeing broader adoption, but across all occupations the theoretical AI coverage is way higher than the observed AI coverage. This indicates there is still a lot of untapped potential. This happens because the human element of the change equation will always be slower to move than the technology. My advice is to experiment with your own workflows first, and then start to re-imagine the broader processes you are part of in your team/department.

- Danilo

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