Martin Fowler & Kent Beck: Frameworks for reinventing software, again and again
Twenty-five years after co-authoring the Agile Manifesto, Martin Fowler and Kent Beck face a question they never expected: will AI upend the craft they spent decades refining, or amplify it? At a gathering of AI startups—«an unlikely venue for old furniture,» as one host joked—the two legends grapple with a paradox. For a generation, they've had answers: write tests, refactor, modularize. Today, «nobody knows the answers to anything.» As enterprises rush to replace programmers with agents and juniors discover AI as a superpower, Fowler and Beck must confront whether the principles that survived object-orientation, the internet, and microprocessors can survive the genie. Will developer experience and agent experience form a perfect circle, or are we witnessing the end of the solo programmer's golden age?
Puntos clave
The skill that matters now is not having answers, but learning how to figure out answers—running small experiments to validate claims as tools and models shift week to week.
Test-driven development and modular design, once divisive, are now critical: agents work best with the same clean code and tight feedback loops that humans need.
The middle tier of programmers—those who coded for money, not craft—face the same fate as during the dot-com crash, but this time the displaced population is much larger.
Pair programming (two humans, multiple agents) may prove more effective than solo developers managing agent teams, preserving the social, conversational core of software work.
Enterprises rushing to let LLMs control email and critical systems are courting catastrophic security incidents; blind adoption without guardrails will produce failures this year.
En resumen
AI is not the end of programming craft—it's a forcing function that makes 25 years of agile and test-driven discipline suddenly indispensable. The developers who thrive will be those who shift from perfecting code to perfecting understanding, pairing humans with agents, and learning to validate claims faster than the answers change.
The Golden Age of the Junior Programmer
AI amplifies learning speed, making this the best era for early-career developers.
“AI is an amplifier. And if you're young and learning quickly, AI is going to amplify that or can amplify that. So I personally think this is the golden age of the junior programmer. I get people coming to me all the time, oh my son started his second year in CS and he wants to go into something more commercial like art history. And I'd say this is like if you're a carpenter and they just introduced the circular saw and you think ah well carpentry is over. anybody can build a house now. Well, no, you have more powerful tools.”
What Stuck from 25 Years of Agile
TDD and refactoring endure, though TDD remains polarizing even among converts.
When asked what ideas resonated most over a quarter-century, Fowler cited refactoring as a steady anchor, while Beck offered a blunter picture: «I get thank you so much for test-driven development. I also get this: test-driven development ruined my life. My dog left me, my house burned down, and it's all your fault.» The divisiveness of TDD has always been part of its design—practices meant to provoke and push boundaries rarely win universal love.
Yet an AI researcher recently told Beck that 20 years of TDD advocacy now pays dividends: with powerful, unpredictable agents in the loop, the discipline of verification becomes non-negotiable. Fowler agrees: «When we've got a big powerful genie, you really have to learn how to verify that it's doing the right thing for you, which we've been practicing for 25 years.» The irony is that the craft many dismissed as over-engineering has become the guardrail for an industry handing more control to machines.
How Fowler and Beck Stay in Touch with Technology
The Venn Diagram of Developer and Agent Experience Is a Circle
Practices that make code readable for humans also make it usable for agents.
The Venn Diagram of Developer and Agent Experience Is a Circle
Fowler highlights emerging consensus: well-modularized code, strong tests, and clear naming help both humans and LLMs. One colleague, Unmesh Joshi, found that developing a precise domain language improved his ability to communicate with agents—essentially domain-driven design as a prompt engineering strategy. This convergence suggests that decades of craft discipline are not obsolete, but newly essential.
Historical Echoes: Object-Orientation, the Internet, and the Microprocessor
AI's magnitude is unprecedented, but earlier shifts taught skepticism, curiosity, and probing.
Fowler and Beck have weathered transformative waves before—object-oriented languages in the '80s and '90s, the rise of the internet, and the microprocessor revolution that Beck witnessed as a Silicon Valley kid. When the Intel 4004 hit, suddenly a computer wasn't a mortgage-sized box; it was a chip anyone could experiment with. That explosion of possibility mirrors today's AI moment. Beck recalls, «If you can figure out how to write software, if you can figure out how to design hardware around this thing, you can suddenly do things we can't even imagine.»
But every shift brought hype, snake oil, and casualties. Object-orientation scared many; the internet had skeptics (including some who doubted its importance). Fowler learned to be «skeptical about my skepticism,» requiring curiosity to probe whether claims hold up. His advice: run the smallest experiment that satisfies your own bar for evidence, and recognize that your first interaction may not signal truth. When Fowler tried GitHub Copilot in Emacs a year and a half ago, he dismissed it as garbage—until colleagues like Simon Willison demonstrated sustained, skillful use. That taught him not to flip the «bozo switch» prematurely.
The Agile Parallel: Promises vs. Reality
Agile pledged better, faster, cheaper—yet misaligned incentives and snake oil followed.
AI Will Repeat the Agile Pattern—But Faster and Bigger
Expect the same mix of hype, real value, and a chasm between skillful and poor use.
Magnitude and Speed AI dwarfs past technology shifts in impact and adoption velocity. «This is a whole size difference from anything that we've faced before,» Fowler notes. No one can put on blinkers; the importance is undeniable.
Skillful vs. Careless Adoption A huge gap will emerge between teams that learn to use AI well and those that don't. Fowler: «The trick is figuring out how to use it well and putting the effort in to learn to use it well.»
The Snake Oil Surge Just as an agile consulting industry bloomed, an AI hype machine is already in motion. Distinguishing real capability from marketing will require constant probing and healthy skepticism.
Security and Control Risks Enterprises are experimenting with LLMs controlling email, code repositories, and critical systems without adequate guardrails. Fowler warns of «really bad security incidents» in the coming year due to blind trust.
The Fate of the Middle: Who Gets Left Behind?
Programmers who coded for money, not craft, face displacement—but the cohort is much larger now.
The Fate of the Middle: Who Gets Left Behind?
Beck draws a parallel to the dot-com crash: a middle tier of programmers who entered for financial reasons, not passion, left for real estate when the bubble burst. Today's AI retrenchment, combined with zero-interest-rate hangover, is flushing out a similar middle—«but that middle is much bigger now than it was 25 years ago.» The junior developers learning fast and senior craftspeople working effectively will thrive; those in between may not.
The End of Solo Craft, the Return of Social Programming
One-person-plus-agents may be less effective than pair programming with multiple genies.
Beck worries about the «resoloing of programming.» Extreme Programming created safe social environments for fundamentally antisocial people, fostering hours of daily conversation. Now, the fantasy is one programmer managing six agents—«really I'm managing a team.» Beck pushes back: «No, you're not. You're using six tools at once.» Agents don't bring differing beliefs, energy levels, or the friction that produces insight.
Fowler and Beck both see promise in pairing two humans with multiple agents. Fowler notes, «If it's two of us, we can control the genies perhaps a little bit better. And we also have that same interaction.» Beck has found pairing with genies productive precisely because models are slow—«every time the models come out and they're faster, I'm like, 'Oh, there's less time to talk.'» While a prompt runs for three minutes, pairs discuss naming philosophy, conditional structure, and strategy. If the response returns in 15 seconds, that conversation vanishes. The social, conversational core of programming may be what separates effective teams from isolated operators with fancy autocomplete.
Letting Go of the Craft Obsession
The dopamine hit of perfecting one function no longer has leverage.
“I take a kind of OCD enjoyment in the craft and I need to let go of that because that satisfaction of getting this one function just right just doesn't make a difference anymore. Getting an overall understanding of what's going on—I can still develop an overall understanding of what I'm doing. And I need to shift my focus to enjoying understanding the domain and its connection to my program in a way that I used to be focused on the program as the domain and I could make that better and better. It just doesn't have leverage anymore.”
Advice for Engineers Who Care About Craft
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