Design & Daily Operation · 2026, ongoing
Most "AI in delivery" efforts don't fail because the model is weak. They fail because nobody redesigns the workflow around it, so the tool sits next to the old process instead of replacing it. Deployed, rarely adopted, rarely trusted with real work.
I closed that gap for my own operation first. For three years I ran delivery as the sole technical PM at a US DevOps consultancy (11 engineers, roughly ten greenfield AWS builds), after taking over a one-person PM function and making a backfill unnecessary. Then I took an open-source multi-agent scaffold, extended it into my own production operations system, and now run everything else through it.
Every request goes through a single coordinator that clarifies the ask, picks the right specialist, and briefs them. Each specialist works under a written scope defining what it does, what it refuses, and what it produces. Memory persists across sessions, so work resumes from a log instead of starting cold.
It's the layer around it, plus two pieces that do quieter work: a structured way to pressure-test a decision from multiple angles before committing to it, and a closing step every session that reviews what worked and refines the system's own playbooks, so a correction only has to be given once. That's the layer most AI-in-delivery pilots skip, which is why they stall after the demo.
If your team already tried AI in delivery and it didn't stick, that's usually a workflow and adoption problem, not a model problem. I'd love to hear what you're working with.
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