Intent, agents, and the end of the SDLC

Buzz, Freeq, pi-brain, Boris Tane, Phoenix Architecture

I’ve been obsessed with a single question: how do you actually work with AI tools in a way that scales?

The old software development lifecycle feels like it’s collapsing. Boris Tane’s “The Software Development Lifecycle Is Dead” nails it: AI agents didn’t make the SDLC faster, they killed it. The new loop is tighter: intent → agent → code + tests + deployment → does it work? → ship or iterate. Requirements, design, implementation, testing, review, and deployment are merging into one continuous conversation.

That shifts the bottleneck from execution to intent management. The skill becomes: can you articulate what you want, provide the right context, and steer the agent without micromanaging it?

I’ve been collecting signals around this:

• Buzz: people, agents, and projects in one place.
• Freeq: IRC with Bluesky identity, a different take on how identity and conversation flow.
• pi-brain: my own experiment in building a system that captures intent and context.
• Ruby Native: collapsing mobile delivery into a config file.

The pattern I keep seeing: store intent, feed context, let agents execute.

The Phoenix Architecture takes this even further. Its framing is that production truth should be a first-class input to software creation: requirements include operational constraints, telemetry gets canonicalized into evidence, and when evidence drifts, only the affected subgraph is invalidated and regenerated. A module isn’t good because it once passed tests; it’s good only as long as the evidence supports the claim that it satisfies the requirement. This is the intent-store idea in a different shape.

This is also a big part of why I organized rubyconf.at: I want to be closer to the people thinking seriously about what Ruby and Rails development looks like when agents are doing most of the typing.