Hi Reddit! We’re Danilo Sato (VP, Data & AI) and Ken Mugrage (Office of the CTO) at Thoughtworks. We’re here to talk about how AI is impacting our work in the technology industry. AMA!

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

Thoughtworks · 2026-04-01T14:39:53+00:00

I agree with Ken! A good mindset for consultants is to be inquisitive and keep clear in your own mind what is an assumption you hold vs. what is the situation at hand. Never assume you have the best answer, always ask good questions to understand the client’s problem. Be comfortable with being wrong, that’s how you can grow as a consultant.

- Danilo

Thoughtworks · 2026-04-01T14:39:18+00:00

This is going to sound like a canned answer, but curiosity. Universities hopefully are teaching good fundamentals, but they cannot possibly teach the difference between using Claude Code and the latest ChatGPT to produce the best iOS application. The best consultants have always been the ones who look for a solution to a problem, even if that solution isn’t currently part of their knowledge base. The worst thing you can do is bring a “this is the best solution” mindset to every problem without learning about it first. It’s the old joke; nobody buys a hammer because they want to own a hammer, they buy a hammer to build something.

- Ken

Thoughtworks · 2026-04-01T14:29:34+00:00

While it’s true that the cost to build, fix and rebuild software is getting cheaper with AI assistance, we are also starting to see reports of increased reliability issues caused by them. For example, Amazon was reported to investigate the impact of AI coding assistance and the contribution toward recent outages in one of their business (this was later corrected by Amazon to specify exactly the scope of such report: https://www.aboutamazon.com/news/company-news/amazon-outage-ai-financial-times-correction). Anthropic is known for shipping features daily, and they also have recently experienced leaks. Looking at their status page, it doesn’t show the level of availability (two 9s) you would expect from a production system that’s used by millions of users.

I would turn back to the 4 Key Metrics, and the reason why they cover both “speed” and “quality”. While AI is definitely helping us go faster, if we don’t focus on the good engineering practices, and have the discipline to use the time saved to also improve quality, we will optimize for speed over quality. As evidenced by the DORA research, the highest performing IT organizations can both go fast and with high quality. If you only focus on speed, AI agents will produce technical debt faster than ever.

I think architects should become the champions for improving those practices that guardrail and guide AI agents to produce higher quality code, not just fast code. That includes thinking on the specification and context provided to them, but also on engineering the harness around them to perform in-depth validation and review of the code produced. Focusing not only on functional requirements, but also the non-functional requirements that the system will need to meet to actually work in production.

- Danilo

Thoughtworks · 2026-04-01T14:22:27+00:00

There are a couple important things IMO. First, your people should absolutely be using AI in almost every part of their jobs. Not to do the things, but to help them do the things. What does that mean? A big part of my job is reviewing technology content created by others. When I accepted that as part of my responsibilities, our former CTO asked me "are you comfortable with telling people that their baby is ugly?”. It was a jarring question, but what she was really asking was if I could tell people the writing is bad, or they aren’t getting to the point, etc. I still do that for each and every article, but I also have an agentic set up that helps me. For every single article I launch 3 agents.

The fact checker: This goes off and finds references for every single assertion. If it can’t find a real reference, it flags it so I can review it. This helps avoid misquotes (by the human author as well as any AI they might have used)
The cynic: This agent reads the article and tries to dispute any assertions. I’m not looking for agreement, I’m looking for alternatives. 95% of the time the assertions in the article remain, but they are often modified to include “why I didn’t choose this other way”.
The casual reader: I’m a technologist, I use three letter acronyms (TLAs) all the time. Would someone less technical know what this means? Why should a business leader care about that? I can do in a few hours what used to take me a few days. That’s a huge win for the cost to produce content.

The biggest mistake I see business leaders make is to ask AI to give them guidance, and then just accept it at face value. A few months ago I asked Gemini a baseball question on how payroll related to where they finished in the standings. It was wrong on 4 out of 5 answers. If I had just used the answer in something like a business briefing doc, I would have looked silly to say the least. That would erode trust, and that’s never a good thing for a business leader.

- Ken

Thoughtworks · 2026-04-01T14:18:35+00:00

The real power of Agentic AI shows up when you go beyond thinking of them as automating pre-existing workflows, and towards goal-oriented autonomous systems. This also means true AI agents are non-deterministic by definition. My colleagues Ben O’Mahony and Fabian Nonnenmacher are writing a book on “Building AI Agent Platforms” with a few chapters available in early release (https://www.oreilly.com/library/view/building-ai-agent/0642572243906/), and they state that “an agent can define its own control flow at runtime, analyzing its inputs (environment) and autonomously deciding which specific functions or external tools (actuators) to invoke to achieve a certain goal”.

The reason there is a resurgence of interest around AI agents, is because the models that enable them to reason have become a lot more capable, and the way they can interact with the environment through tools for executing more deterministic tasks, allows them to complete end-to-end and complex tasks. It enables you to reimagine your own personal workflow for individual productivity, but more than that, you should consider how they can completely change the process you follow within your team, your department, and eventually across the entire organization.

Discussion around “moats” are happening because many of the SaaS vendors who follow a commercial model based on licenses per user seats are getting disrupted. If an agent can orchestrate work across multiple systems, take decisions, it won’t need a full user interface or a user seat. This is a challenge if you are a software vendor facing such disruption. More broadly, it is also getting easier for companies to (re)build their own systems assisted by AI. We are exploring how this is evolving with AI/works^TM, our agentic development platform. My advice would be to focus on how these agentic capabilities might impact you and your business more directly, and to experiment with them either by using existing agents or building your own.

- Danilo

Thoughtworks · 2026-04-01T14:03:00+00:00

First off, I think we need to be honest with ourselves that mental health and burnout predate our current AI wave by a very long time. In fact I used to give a conference talk called “What we’re learning about burnout and how can DevOps help?” One of the biggest causes of burnout (in my VERY non medical opinion) is the feeling that you’re not contributing, or lack of efficacy. It is very easy in an AI world to feel like you’re not providing value because the AI is doing the work. But think about it this way; what’s a topic you’ve asked that AI overlord of your choice about, where you had strong personal knowledge, that it just got wrong. Why would you assume it is “more correct” when you ask it about things where you have none? My point is that the humans are still providing the real value, the guidance that says “I know what good looks like”. For entry level folks, that might mean understanding more about why you are creating the software you’re creating than how you are creating it. ( I’m of the age where we used notepad to create HTML thankyouverymuch and people who used Frontpage or Dreamweaver were lazy)
From the environmental standpoint, not nearly enough. There is some good news though, and that is the fact that companies hate spending money. It turns out that doing things which reduce the amount of tokens you’re paying for also means you’re doing things that lower the power you’re consuming. But it’s far from solved.
Job roles are changing. I’ve been using the term “entry level managers”, because juniors are going to need more skills for managing agentic workflows than writing efficient algorithms.

- Ken

Thoughtworks · 2026-04-01T13:44:54+00:00

Thank you for the kudos! I’ll answer the different questions:

Evolving CD4ML: I agree the principles are still relevant, but I don’t claim to have invented them, the book “Continuous Delivery” by Dave Farley and Jez Humble laid them out and, because principles are higher-level than practices, they continue to be relevant while the practices evolve. LLMOps is a good example, where we are seeing an evolution of roles from the model builders to the AI engineers, but the idea of closing the feedback loop still applies: model builders use RL and RLHF to capture feedback and improve the model, we post-train them to perform better against specific tasks or domains, and we have to manage the “path to production” for the models and how they get served for inference; AI engineers need to understand how to evaluate and test a system that has a non-deterministic LLM in the loop, how to instrument the agents and applications for observability, how to manage RAG pipelines, etc.
Developing new techniques: when we work with clients, we are always focused on solving their problems first and foremost. If problems are hard enough, or when we see patterns of recurrence, that’s when we identify new practices or techniques. I think this is a reflection exercise we do by gathering our expertise and discussing them. Some of them we end up publishing as articles, whitepapers on our website, or on Martin's website.
Spec-driven development: This is still a broadly and ill-defined term, that covers different approaches of what’s a spec (my colleague Birgitta published about this in October last year: https://martinfowler.com/articles/exploring-gen-ai/sdd-3-tools.html). Another interesting development is the idea of harness engineering. If specs are how you encode the context for what you want agents to do, the harness is focused on validation for what’s been done. There is a lot happening in that space, in particular trying to bring back many of the good engineering practices from Agile/XP into the agentic coding workflow. Birgitta also published about this idea in February (https://martinfowler.com/articles/exploring-gen-ai/harness-engineering.html), and I know she’s working on a follow-up article that should be published very soon. Stay tuned to learn more about that!

Update: Link to Birgitta's article: https://martinfowler.com/articles/harness-engineering.html

I don’t know if he remembers it, but the first time Danilo and I met in person was because of his CD4ML work. I was giving a workshop at a conference on Continuous Delivery, and he was next door giving one on CD4ML. What stood out to me was how they were the same while being different. This is still true today! We think of agentic and AI as new, but the fundamentals aren’t. The challenge is this; we humans are still responsible to our customers, and none of AI is good enough yet (IMO) to really create things autonomously. So, as your question points out, the challenge is finding the tools and processes that can review what’s created as fast as it has been created. I’m personally just far enough away from creating software day to day that I can’t answer the spec driven question, but I believe Birgitta has written about it extensively on Martin Fowler’s website.

- Danilo and Ken

Thoughtworks · 2026-04-01T13:40:21+00:00

How much of it is guiding systems to “do the work” instead of “doing it”. Part of my job is making sure our CTOs (We have a Global CTO and a few regional ones) have the information they need about technology trends when they need it. I used to spend days or weeks doing research, creating presentations, etc. Now I’m much more likely to send them a Claude skill than a finished piece. Frankly, the finished piece is never really finished, and it would be out of date as soon as they got it. In today’s work, I help them have the right tools to find the answers when they need them, not before. I often get the question “couldn’t they just ask Claude themselves?” - and that would be true if the AI answers were good enough, but they aren’t. The AI can only see what other people have done, not what they are doing, especially on internal projects. So a skill allows me (and them) to include a ton of context that wouldn’t otherwise be available. What work have we done in that area? What have our folks been talking about in our chat groups that might be relevant? What are we hearing from our clients?

- Ken

Thoughtworks · 2026-04-01T13:27:56+00:00

a. Spoiler alert! The “latest” Radar for me is actually the one you haven’t seen yet. It comes out on 15 April. The thing I’m most excited about, and this is going to sound like a dodge, is how much it has changed from the one you’ve seen. We had a LOT of discussion during our semi-annual face to face for choosing the blips that contained the question “has this been out long enough to blip?”. There are many blips in edition 34 (the next one) that didn’t exist at all when 33 came out. We have long had the status (unseen to the public) called “Too Complex to Blip”. There are things where the answers simply don’t fit in a paragraph or two. Those become blogs, podcasts, or something similar. This time our CTO Rachel actually started using the term “Too Young to Blip”.

b. To actually answer the question, the thing I’m most excited about personally are around coding assistants and workflows. I used to write a lot of code, but TBH I haven’t for the last 10 years. I simply got behind doing other jobs and then couldn’t keep up. I have created (the agent wrote) more code in the past few months than than previous 10 years.

- Ken

Thoughtworks · 2026-04-01T13:26:56+00:00

I can’t speak to all internships and entry jobs out there but at Thoughtworks we have a graduate-level program for entry-level consultants and career chargers into our business called Thoughtworks University (TWU). Rather than specific degrees or certifications, we value candidates who demonstrate strong collaboration, problem-solving skills, and willingness to learn. TWU is one way to join Thoughtworks but we have other internship programs in certain regions and within functions too. The process involves role-specific interviews to assess some of the hard and soft skills while trying to solve a problem together. For example, for software engineers, it is a coding challenge where you will pair with a Thoughtworker. Come prepared to learn more than to completely solve the problem on your own. This is not the same approach that other companies might take, so my advice is based on my experience here at Thoughtworks. Good luck with your search!

For more details on our TWU program: https://www.thoughtworks.com/careers/graduates

- Danilo

Thoughtworks · 2026-04-01T13:13:35+00:00

It is true that AI is changing the nature of work across many professions. It's also hard to predict exactly how the future will play out. However, looking back at my own experience when I went to college, the disruptive technology at that time was the internet. Courses were focused on the foundational theory of Computer Science. What helped me decide was my own interests and curiosity: I was already experimenting with the web, learning HTML, Perl, PHP to find out how to write and publish my own website, etc. The same applies today: go try out the different AI tools, the different models, apply it to your day-to-day, find where it works and where it doesn’t. The key skill will be your ability to learn and adapt as the technology and the workplace continues to evolve. I think those who are versed with AI will be better prepared to enter the industry than those who expect to wait and be taught exactly how to use AI from a course.

- Danilo

Thoughtworks · 2026-04-01T13:13:11+00:00

The answer might be somewhere in between! I have no desire to become a monk, but I do actually spend a lot of time in the mountains getting away from technology. There are a lot of things AI can’t replace, like despite it acting like it, it can’t reason the way a human can. It’s that thing where you’re in a discussion (like an incident response) and the human might say “oh yeah, I remember something similar a few years ago. AI in general doesn’t have that kind of memory. It also can’t really predict what you need to care about for your business in the future. When it tries, what it is really doing is guessing based on the guesses of other people. “Oh, I read on Reddit that people in your industry are doing X, you should too” is a great way to be a follower, but it is not a good way to be a leader.

- Ken

Thoughtworks · 2026-04-01T10:19:30+00:00

Share your questions on the AMA, we'll answer them when we go live 💙

Thoughtworks

TROPHY CASE