AI · Approach

AISprints

A Human-AI collaboration approach for disciplined LLM-assisted delivery - technical PRDs, guardrails, and micro-sprints.

AI leadership overview Agentic SDLC approach Open the kit

Part of my AI leadership work. Related reading: Agentic SDLC.

Names in this space

AISprints is the lived practice on this page. The vendor-neutral category is Agentic SDLC. AWS's named method in the same shift is AI-DLC. They are related operating models, not interchangeable brands. I did not invent these names. See the Agentic SDLC page for the map.

The problem

Most teams either ban AI or let it erode engineering discipline. Speed without review becomes theater. Ban without leverage leaves capacity on the table.

The method

  1. Technical PRDs - clarify intent, constraints, and success criteria before generation floods the branch.
  2. Guardrails - quality gates, review expectations, and boundaries for what AI may change.
  3. Micro-sprints - short loops that keep human judgment in the critical path.
  4. Measure Human+AI - track outcomes on real features, not demo wow.

Pilot outcomes

On pilot feature releases: about ~50% defect reduction and about ~30% engineer time saved where GenAI-assisted flows applied. These are pilot results, not a universal guarantee.

See pilot results for the verified numbers in context. Public kit: github.com/jitendraapi/agentic-sdlc-kit - also under Build.

Who it is for

Engineering leaders adopting AI in the SDLC who still care about quality, ownership, and measurable delivery - especially in regulated or high-scale product orgs.