Skip to content

Reading Radar

Fresh ideas.
Better ways to work.

Explore Agile, ways of working and practical AI for teams. Find recent articles from six sources, then follow an idea back to its author.

Read our perspective

On our radar

Updated daily
Martin Fowler Agile By Martin Fowler

Bliki: Paracelsus Maxim

The difference between a medicine and a poison is dosage. Often we talk about certain habits, in programming or life, are good or bad. But few things are simple binaries. Some vary with context: reading a book is a good thing sitting in my garden, but not while driving my car. But another variable is dosage: a little pain-killer salves my headache, but too much will kill me. The importance of dosage was noticed by a 16th century Swiss physician called Paracelsus. His quote was originally in German “Alle Dinge sind Gift, und nichts ist ohne Gift; allein die Dosis macht, dass ein Ding kein Gift

Read the original
Martin Fowler Agile By Martin Fowler

An Accidental Blackboard

Giles Edwards-Alexander reports that during an experiment to see how productive a team could be using fully agentic engineering practices, the team accidentally prompted the agents into creating a blackboard coordination system inside the git repository. more…

Read the original
Martin Fowler Agile By Rachel Laycock

Maybe We Shouldn't Be Reviewing All This Code

TL;DROr, perhaps the problem isn't that AI has broken code review, maybe it’s that we've been using code review to solve the wrong problems I was on a panel recently with Brian Houck from DX at Code Remix, hosted by Moderne. It was one of the more interesting panels I’ve done, largely because we disagreed. As my colleague Martin Fowler says, panels are much more interesting when people disagree and both sides have a good argument. Brian and I definitely did. Brian has since written a thoughtful piece called What are code reviews even for? He is clearly passionate about his position, and I am p

Read the original
Martin Fowler Agile By Martin Fowler

Fragments: September 1

Like many readers, I’m wary of AI generated prose. Simon Wilison has written an LLM cliché highlighter - paste in some text, or a URL, and it will flag various patterns common to LLMs. It references a wikipedia page of signs of AI writing. That page points out that: Humans are notoriously bad at distinguishing human and LLM-generated text. While research on humans’ abilities to detect AI-generated text is still limited, a 2025 study has shown that human ability to distinguish LLM text from human is no better than random chance. Another 2025 study on German theses has shown that humans managed

Read the original
Martin Fowler Agile By Martin Fowler

Making Your Data Ready for Agentic AI

Lots of organizations are excited about what AI can do to streamline their processes, save money, and juice margins. But AI's capabilities are founded on the data that AI accesses, and for many organizations that foundation is little more than sand. Pramod Sadalage and Prem Chandrasekaran write about how to build a reliable foundation of data that can be accurate and trusted. more…

Read the original
Martin Fowler Agile By Martin Fowler

Fragments: August 24

I was listening to Ezra Klein’s interview with Helen Toner about the recent OpenAI hack of Hugging Face and the subsequent discovery that there were swarms of agents inside OpenAI doing unsanctioned activities. One of the points Klein made was that at no point did any of these (thousands of?) agents ever try to check in with a human [Klein:] So these message boards — you have however many A.I. agents posting hundreds of thousands of messages. At no point do they say: Hey, researchers, programmers, parents at OpenAI, Anthropic — do you want us coordinating with each other on this message board

Read the original
Martin Fowler Agile By Rachel Laycock

Citizens Build, Agents Execute, Experts Govern

TL;DRWhy building an app over the weekend isn't the same as building enterprise software I’ve noticed an interesting gap opening up over the last six months. It isn’t really a gap in technology. It’s a gap in what different people think software engineering actually is. The conversation usually starts the same way. A non-techie, maybe an executive, tells me about something they’ve built over the weekend. Sometimes it’s a chatbot. Sometimes it’s an internal workflow. Sometimes it’s a surprisingly polished application that solves a real business problem. They’re excited, and they should be. Twel

Read the original
Martin Fowler Agile By Martin Fowler

Fragments: August 18

Part of the reason why I’m at Thoughtworks is because I’d like to see a software development organization founded on technical excellence as an example for the rest of the industry. The trouble is that I have little aptitude or inclination for the hard work of building such an organization. So I rely on working with people who are prepared to actually put the effort in. A key partner in all of this is Rachel Laycock, who is the global CTO of Thoughtworks. Not just is she far better than me at running a technology organization, she’s also a keen observer and connector of ideas. I’ve been urging

Read the original
Martin Fowler Agile By Martin Fowler

Fragments: August 4

There’s been a fair bit of publicity of the Open AI “rogue agent” that hacked into Hugging Face. This prompted Anthropic to check what their models were up to and, to my complete lack of surprise, discovered three incidents where models had gained unauthorized access to data in other organizations. Simon Wilison concluded: It’s abundantly clear now that running evals of cyberattack potential in models is a spectacularly risky business. Every AI lab needs to pay attention to this. Keeping a close eye on what’s happening in those sandboxes is crucial It strikes me that this is akin to a virus es

Read the original
Martin Fowler Agile By Rachel Laycock

The Conductor Developer

TL;DRWhy I think software development is starting to feel a little more like conducting an orchestra. There’s a shift happening in software development that I don’t think we’re talking about clearly enough. For the last couple of years we’ve framed AI as a productivity tool. How much faster can it write code? How many more features can we ship? How much cheaper can we build software? I think that’s the wrong question, but I understand why. The first thing AI became good at was writing code, so naturally that’s where we focused. As AI got better at coding, I expected the bottlenecks to move thr

Read the original

Follow the ideas

Meet the sources.

These articles belong to their original authors. Summaries help you choose what to read; visit the original for the full context.

COME TAKE A LOOK

A little less busy.
A lot more together.

Curious? Join the waitlist and see how we welcome your team.

Tell us a little about your team. We’ll explain the next steps and invite you when we have capacity for your workspace.

Join waitlist Start with your email. No payment needed to join.