The AI-native SDLC

The problem

Most “AI in engineering” stops at autocomplete. The harder problem is the whole delivery loop: how does a team take a ticket all the way to a merged, reviewed, CI-green pull request without a human babysitting every step — and do it consistently, across an org, in a way you can actually measure and trust?

That means three things at once: a pipeline reliable enough to ship real work, the tooling to wire agents into internal systems, and the enablement to get an entire engineering org using it rather than one early adopter.

What I did

I built the agentic pipeline my team ships through, end-to-end, and drove its adoption across an 18-engineer org.

• The full loop. Ticket → low-level design → epic/spec → plan → review → PR → agent code review → CI → merge → follow-up. Each stage is an agent step with a defined contract, so work flows through the pipeline instead of stalling on a human at every hop.
• Custom MCP servers. I authored Model Context Protocol servers that plug the agents into internal tooling — issue trackers, code-knowledge graphs and delivery systems — so the agents act on real project state, not a guess.
• Project conventions as code. The project CLAUDE.md, project-specific Claude skills and reusable prompts encode our standards, so every agent run inherits the same architecture, naming and review rules.
• Observability. A dashboard tracks every agent run — totals, activity over time and per-project breakdowns — so the workflow is measurable rather than anecdotal.

Impact

• 55 tickets to staging in 24 hours on a recent freelance project via a ~99% agentic workflow — the proof point that the loop ships real work, not demos.
• Adopted across an org that grew from 5 to 18 engineers, with hands-on enablement, training and AI-augmented onboarding for new hires.
• A repeatable, measured delivery process — every run captured on the dashboard, so throughput and adoption are visible rather than guessed at.