Continuing the conversation on the future of software engineering
Engelberg, Switzerland | June 28-30, 2026In February 2026, Martin Fowler and Thoughtworks hosted an event in Utah to explore the impact of AI on the future of software engineering. Taking in everything from developer identity to emerging techniques and tools, it provided a snapshot of an industry on brink of significant change.
A few months later and the pace of change shows no signs of letting up. That's why, at the end of June, a second retreat was convened in Engelberg, Switzerland. Bringing together practitioners, researchers and business leaders, the event was an opportunity to build on the insights of Utah and to map out the frontiers of responsible and impactful in more detail.
Key insights and themes from the event
Code generation is no longer the bottleneck — verification is. Across testing, legacy modernization, code review and team-design sessions, the same conclusion kept coming up: agents can produce code (and specs, tests and infrastructure) far faster than any team can trust it. The discipline that wins is the one that builds cheap, fast, human-legible verification — characterization tests, constraint tests, mutation testing, production back-testing — not the one that generates the most code.
‘Harness engineering’ is emerging as a distinct, ownable discipline. The scaffolding around an agent — context management, deterministic guardrails, skills, self-improving feedback loops — is repeatedly described as more important than the model or the prompt. It may be the place where competitive differentiation will live once models commoditize. Notably, the need for ownership and accountability came up repeatedly, but there was no consensus on who should take that on.
Organizations are colliding with a real apprenticeship crisis. Multiple sessions independently raised the same fear: if senior engineers pair exclusively with agents, junior engineers lose the hands-on path to judgment, taste and the production instinct the industry has always relied upon to cultivate the next generation of seniors. Consistent with the discussions in Utah, most people agreed it was important that people developing software in this manner need to know what good looks like, which explains why senior engineers thrive. However, this increases the risk associated with the apprenticeship issue.
The executive/engineer expectation gap is a bigger risk than any technical limitation. Boards and CEOs are making large, fast bets based on vendor demos and their own experience with report-writing AI while engineers see a widening list of unresolved verification, security and governance problems underneath the productivity gains.
Legacy modernization is the clearest, most defensible near-term value pool. Several sessions described rigorous, working, technically detailed approaches to AI-assisted COBOL/mainframe modernization with real verification discipline.
“The punishment for your bad habits now comes much sooner than before... it's only a couple of hours now until it comes back and kicks you.”
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