<feed xmlns="http://www.w3.org/2005/Atom"> <id>https://smcgowan.me/feed/ai-sdlc.xml</id><title>You can't install an AI-SDLC</title><subtitle>An article series by Stephen Mc Gowan on growing an AI-enabled software development lifecycle.</subtitle> <updated>2026-09-24T10:02:16+10:00</updated> <author> <name>Stephen Mc Gowan</name> <uri>https://smcgowan.me/</uri> </author><link rel="self" type="application/atom+xml" href="https://smcgowan.me/feed/ai-sdlc.xml"/><link rel="alternate" type="text/html" hreflang="en" href="https://smcgowan.me/writing/"/> <generator uri="https://jekyllrb.com/" version="4.4.1">Jekyll</generator> <rights> © 2026 Stephen Mc Gowan </rights> <icon>/assets/img/favicons/favicon.ico</icon> <logo>/assets/img/favicons/favicon-96x96.png</logo> <entry><title>Interlude: Jev and the economics of no-training</title><link href="https://smcgowan.me/posts/economics-of-no-training/" rel="alternate" type="text/html" title="Interlude: Jev and the economics of no-training" /><published>2026-09-23T00:00:00+10:00</published> <updated>2026-09-23T00:00:00+10:00</updated> <id>https://smcgowan.me/posts/economics-of-no-training/</id> <content type="text/html" src="https://smcgowan.me/posts/economics-of-no-training/" /> <author> <name>Stephen Mc Gowan</name> </author> <category term="ai-sdlc" /> <summary>I re-ran an old AWS Comprehend project against TypeSafe's Jev. With no training data it beat the trained classifier at roughly a two-hundredth of the cost. It also showed where the risk goes when you stop training models and start writing questions.</summary> </entry> <entry><title>Architecting for agent legibility</title><link href="https://smcgowan.me/posts/architecting-for-agent-legibility/" rel="alternate" type="text/html" title="Architecting for agent legibility" /><published>2026-09-17T00:00:00+10:00</published> <updated>2026-09-17T00:00:00+10:00</updated> <id>https://smcgowan.me/posts/architecting-for-agent-legibility/</id> <content type="text/html" src="https://smcgowan.me/posts/architecting-for-agent-legibility/" /> <author> <name>Stephen Mc Gowan</name> </author> <category term="ai-sdlc" /> <summary>An AI agent is a permanently new team member: it re-pays your codebase's discovery cost every single session. What that means for repo structure, docs as code, restraint about abstraction, and conventions machines can check.</summary> </entry> <entry><title>Build the quality floor before the autonomy</title><link href="https://smcgowan.me/posts/quality-floor-before-autonomy/" rel="alternate" type="text/html" title="Build the quality floor before the autonomy" /><published>2026-09-10T00:00:00+10:00</published> <updated>2026-09-10T00:00:00+10:00</updated> <id>https://smcgowan.me/posts/quality-floor-before-autonomy/</id> <content type="text/html" src="https://smcgowan.me/posts/quality-floor-before-autonomy/" /> <author> <name>Stephen Mc Gowan</name> </author> <category term="ai-sdlc" /> <summary>Nineteen phantom products, the pull request that pivoted a proof of concept into a product, and what a ratcheting coverage floor actually buys when agents write most of the code: regression detection at machine speed, so the human can follow the bottleneck.</summary> </entry> <entry><title>Constrain, observe, review: agent safety as infrastructure</title><link href="https://smcgowan.me/posts/agent-safety-as-infrastructure/" rel="alternate" type="text/html" title="Constrain, observe, review: agent safety as infrastructure" /><published>2026-09-04T00:00:00+10:00</published> <updated>2026-09-04T00:00:00+10:00</updated> <id>https://smcgowan.me/posts/agent-safety-as-infrastructure/</id> <content type="text/html" src="https://smcgowan.me/posts/agent-safety-as-infrastructure/" /> <author> <name>Stephen Mc Gowan</name> </author> <category term="ai-sdlc" /> <summary>A self-hosted agent sandbox with exactly three egress paths, the breakout attempt it contained, and the reference architecture enterprise agent platforms should be heading for: every path an agent can take runs through a control point you own.</summary> </entry> <entry><title>Show me what you think we built</title><link href="https://smcgowan.me/posts/show-me-what-you-think-we-built/" rel="alternate" type="text/html" title="Show me what you think we built" /><published>2026-08-26T00:00:00+10:00</published> <updated>2026-08-26T00:00:00+10:00</updated> <id>https://smcgowan.me/posts/show-me-what-you-think-we-built/</id> <content type="text/html" src="https://smcgowan.me/posts/show-me-what-you-think-we-built/" /> <author> <name>Stephen Mc Gowan</name> </author> <category term="ai-sdlc" /> <summary>Generated customer updates, questionnaires with assumptions attached, and a solution design that audits its own citations: reading what the AI thinks you built is how you test architecture and finish the last 10 per cent.</summary> </entry> <entry><title>Engineered disagreement</title><link href="https://smcgowan.me/posts/engineered-disagreement/" rel="alternate" type="text/html" title="Engineered disagreement" /><published>2026-08-20T00:00:00+10:00</published> <updated>2026-08-20T00:00:00+10:00</updated> <id>https://smcgowan.me/posts/engineered-disagreement/</id> <content type="text/html" src="https://smcgowan.me/posts/engineered-disagreement/" /> <author> <name>Stephen Mc Gowan</name> </author> <category term="ai-sdlc" /> <summary>Four AI personas that changed nothing, one that changed everything, and what it actually takes to stop two agents on the same model from agreeing with each other.</summary> </entry> <entry><title>You can't install an AI-SDLC</title><link href="https://smcgowan.me/posts/you-cant-install-an-ai-sdlc/" rel="alternate" type="text/html" title="You can&amp;apos;t install an AI-SDLC" /><published>2026-08-11T00:00:00+10:00</published> <updated>2026-08-11T00:00:00+10:00</updated> <id>https://smcgowan.me/posts/you-cant-install-an-ai-sdlc/</id> <content type="text/html" src="https://smcgowan.me/posts/you-cant-install-an-ai-sdlc/" /> <author> <name>Stephen Mc Gowan</name> </author> <category term="ai-sdlc" /> <summary>Why an AI-enabled software development lifecycle (SDLC) has to be grown, not installed: eighteen weeks of evidence from one human directing AI agents on a production system.</summary> </entry> </feed>
