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.
Chapters:
0:00 Welcome And A Weather Oddity
0:51 Why AI Puts SDLC Back Center
2:48 Intent, Agents, And Many Variants
5:18 Where The SDLC Model Breaks
10:41 Context, Inference, And Complexity Risk
15:38 Practical Takeaways And Open Questions
20:18 Closing And Until Next Time
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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