// Delivery operations

Self-Driving Agentic SDLC Factory

Novel SDLC Automation turns a GitHub issue queue into a self-driving coding factory: it picks up each issue, implements it, opens a PR, self-reviews, fixes review comments, conflicts, and failing CI, then squash-merges — and moves on to the next.

Scaling

The opportunity

Modern coding agents are powerful but still need a conductor — someone to decide what starts next, avoid overloading the repo, notice blocked tasks, run a review pass, route fixes back, and merge finished work. That orchestration is exactly the mechanical overhead that pulls a developer out of the creative, high-leverage part of the job.

The hypothesis

If the conductor itself is made programmable and autonomous — with an AI agent as the worker behind every step — a prioritized backlog becomes a living assembly line: issues → implementation → PR → self-review → fixes → squash merge. The developer expresses intent and reviews outcomes; the loop handles the handoffs, rebases, and CI fixes in the background.

Current state → future state

Current stateCoding agents produce work but each handoff — start, review, conflict, CI, merge — needs a human babysitter.
Problem / painMechanical orchestration breaks creative flow and caps how much an agent fleet can actually ship.
Who is affectedSolo developers and small teams running agentic development.
Impact focusThroughput · flow · forward momentum.
What success looks likeUnblocked issues are picked up, implemented, reviewed, fixed, and merged automatically; only finished work accumulates.
What would improveThe developer stays on intent and architecture while the loop pulls weight in the background.
Constraints & risksA manual label keeps any issue or PR under direct human control; merges are squash-clean with closing references preserved.

How impact is measured

  • Autonomous merge rate — issues taken from open to merged without human intervention.
  • Human touch-points per change — how often a person must step in per shipped unit.
  • CI-fix turnaround — time from a failing check to a pushed, passing fix.

Why it’s a Lab case

It is a governed, measurable bet on developer leverage — and the conductor pattern generalizes: any repository with a well-formed backlog can adopt the same self-driving loop, making it a reusable delivery-operations capability rather than a one-off script.

// COULD THIS PATTERN APPLY SOMEWHERE ELSE?

The exact system may be different. The pattern is what matters: a real workflow, a measurable pain, and a bounded first proof point.

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