AI in the software delivery lifecycle is moving faster than any process you can write down. A model that works today may be wrong in a month or two.

Dave Sharrock and Peter Maddison unpack what AI is really doing to the SDLC, from ideation and design through build, release, and operations. They start with Anthropic’s recent paper on an AI-native SDLC. Peter has spoken with its authors and sees useful ideas in it: capturing intent, using agents to build several versions and pick the best, and handing operations over to agents. He also sees gaps around where context lives, how it is versioned, and what happens when it goes stale. Dave compares two paths he sees in organizations. Some run AI-native delivery alongside their existing systems. Others swap AI into parts of the process they already have. In both cases, context, governance, and architecture matter more than they did before.

This week’s takeaways:

• Treat published AI-native SDLC models as sketches, not blueprints, because they leave real gaps, especially for large and regulated organizations.
• Agents still need to know the purpose and constraints of what they are building, or they will happily layer in abstraction until the system is far more complex than it needs to be.
• Govern two things: whether the system produces the right output, and whether your agentic orchestration still behaves correctly every time the underlying models change.

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AI can now produce output faster than most organizations can absorb it. Mik Kersten explains why the move from output to outcome has become a leadership job.

Mik, author of Project to Product and the new book Output to Outcome, joins Peter and Dave to talk about what happens when coding stops being the bottleneck. For years, business teams fought to get their feature into the next plan because delivery capacity was scarce. With agents taking that constraint away, the bottleneck moves. It might be customer adoption, security reviews, budget, or how long it takes to get real feedback. Mik walks through the outcome loop, which connects strategy and budget to outputs and then to customer and business outcomes, and explains how to apply the theory of constraints to it. He also covers the outcome tree, a way of cascading company-level targets down to the value streams doing the work. Peter and Dave push on the hard parts: business leaders who can’t name their top outcome, B2B teams that wait months for usable feedback, and operating models that fall apart once the leader who championed them moves on.

This week’s takeaways:

• When AI removes coding as the constraint, the bottleneck shows up somewhere else, so measure the whole loop from budget to customer outcome and go find it.
• If a team can’t connect its work to a company-level outcome, the strategy was never decomposed, and moving faster only makes that gap more obvious.
• Outcome ownership needs to sit with the teams closest to the work, with the business at the table, and the operating model has to outlast any single leader.

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Spec-driven development with AI is getting a lot of attention. Peter and Dave ask when that structure actually helps you, and when it slows you down.

They start with the AI SDLC white paper Anthropic recently published. It lays out a structured, spec-driven approach built for consistency. Peter sees it working best on large legacy systems, the kind with millions of lines of code written over 15 years by thousands of people. For greenfield work, a looser approach of prototyping and exploring often fits better. Dave pushes on the piece most teams drop: the feedback loop. Everyone agrees to review and validate, and then the next shiny feature shows up. They also cover what happens when an AI agent follows an over-detailed spec too literally, why systems thinking beats chasing every edge case, and how explore versus exploit plays out differently for a startup and a company with 10 million customers.

This week’s takeaways:

• A structured, spec-driven SDLC pays off on large, complex systems where stability matters, but it can feel like far too much when you are just prototyping something new.
• Define enough up front to guide the work, then rely on regular check-ins and feedback loops to course-correct, because no spec can predict every case and an AI agent will happily take the shortcut you never meant to allow.
• Explore and exploit are not either/or. Organizations can now run both under one roof, as long as they know where they sit on the path from startup to established player and use judgment about how much structure to add.

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