I help enterprise .NET teams adopt agentic AI.
Solutions Architect & Product Owner · 22+ years in .NET across healthcare, insurance and managed services · C# Corner MVP · Greater Noida, India
Most .NET teams don't stall on agentic AI because the models are weak. They stall because there is no lifecycle around the model — no verification gates, no definition of done that a generated change has to survive, no enablement path for the people expected to use it. That gap is what I build for.
AI-First Development Playbook — team edition A seven-phase agentic SDLC: phase gates, a Verifier subagent backed by Playwright and dotnet integration tests, and a four-command library. Ships with an honest adoption-lessons section, because the first rollout mostly didn't take.
TechieFlow — solo edition The same philosophy at one-person scale — the framework I actually run my own work on. Now instrumented, so the claims about it can be checked against telemetry instead of taken on trust.
TechieRag — library
Retrieval-augmented generation for .NET, built so a stranger goes from dotnet add package to a working query without leaving the ecosystem. v0.1 in progress.
TrBlazeUI — library Blazor component library: Components, Primitives, and icon packs for Lucide, Heroicons and Feather.
- Founder, NCR Cloud & Core Techies — 500+ member developer community in Delhi NCR
- Writing: techierathore.com · [C# Corner](<>)
Open to conversations about agentic AI adoption in .NET estates — architecture, guardrails, and the enablement half that usually gets skipped.
