Tapesh Nagarwal/ qa-sdet-aie
senior specialist · quality engineersayreville, nj  ·  et

Shipping agentic systems that work in production.

I turned a quality obsession into a governed AI engineering org.

Senior quality engineer, seven years. In an AI era the scarce skill isn't writing tests — it's evaluation: defining what “good” means, building the harness that measures it, and turning that into a release decision you can trust. I ship that as real software — a release-confidence engine on npm and a graded QA-practice platform — not slideware.

Voice is “we”. This site is “I”.

00

How I build the systems that answer “should we ship?”

QA taught me the real question is never “does it pass?” — it's “how confident are we, and where's the risk?” So I build the evaluation systems that answer it: the rubric, the harness, the score, and the honest verdict. Each idea below is something I've shipped.

release confidencequlib

Quality is a decision, not a checkmark

A green suite means “the cases I wrote pass” — not “ship it.” qulib fuses live-app evidence, automation maturity, and API coverage into one scored verdict — ship · caution · hold · block — and refuses to fake confidence it hasn't earned.

  • buildsthe rubric & the score
  • verdict4-state · 0–100
  • honestythin coverage ≠ ready
eval as productnotquality

If you can't grade it, you can't improve it

On NotQuality.com free-text bug reports are graded against a documented registry — coverage, severity, repro quality — the same way you'd score an LLM's output. Designing that answer key is the eval-engineering skill.

  • scoresseverity-weighted
  • truth76-bug answer key
  • shapegraded, not pass/fail
measure firstdeterministic

Let AI explore; make the checks deterministic

AI is great at finding unknown gaps — but the moment a judgment matters, it needs a reproducible check behind it. I draw that line on purpose: deterministic gates scale, the model reasons over real signal, and every verdict is one you can re-run and verify yourself.

  • gatesreproducible · in CI
  • modelreasons, doesn't guess
  • proofre-runnable verdicts
01

Now

Three things in flight right now — full detail on each lives in selected work below.

02

Playground

Not a mockup. Outer-loop principal agents — Route, Check, Fetch, Scan, and Build — govern policy and learning while inner-loop execution agents run inside project workflows. Press run and those five principals execute a real release-readiness sweep against the live notquality.com deployment: an actual HTTP fetch, a transport + security-header audit, and a11y/seo signals parsed from the page — rolled up into a release-confidence score you can verify yourself.

targetnotquality.com·live
pass warn fail skip
press run live sweep to probe notquality.com for real
03

Selected work

NotQuality.com

/ qa training platform
visit ↗
notquality.com live
notquality.com homepage — a QA training platform with playground labs and scored bug-hunt challenges

Strata

/ public agent-learning journal
read on github ↗

@qulib/mcp

/ open-source mcp server
view on npm ↗
npmjs.com / @qulib / mcp published

@qulib/mcp

"is this ready to ship?" — for deployed web apps

v0.10.0MITmcp · stdio
$ npm install @qulib/mcp
qulib_score_confidenceflagship — fuses every signal into one verdict: ship / caution / hold / block, with a 0–100 confidence score.
analyze_appcrawls the deployed surface, returns a structured readiness report.
explore_authwalks login flows under bounded credentials, with redirect tracing.
detect_authpassive detection of auth shape — sso, jwt, session cookie, etc.
qulib_score_automationscores a repo's test-automation maturity across weighted dimensions.
qulib_score_apidiscovers API endpoints and scores their test coverage.
qulib_scaffold_testsgenerates a ready-to-run Cypress scaffold from a deployed URL.
04

Experience

  1. Senior Quality Engineer / Senior Specialist

    2019 – Present

    Scholastic, Inc. · New York, NY

    • Established AI workspace guardrails and agentic quality workflows adopted across engineering teams; validated deployment readiness via automated feature-flag and code-movement signals.
    • Architected a Cypress/TypeScript ETL automation framework for end-to-end validation of event-data pipelines — ingestion, transformation, aggregation, fixtures, and release-readiness checks.
    • Built and scaled the QA automation strategy and mentored engineers — a REST-Assured/Cucumber/Docker framework validating 500+ endpoints, lifting regression reliability ~50% via Jenkins CI/CD.
  2. Quality Engineer — Warehouse Management Systems

    Jul 2022 – Jul 2023

    Blue Apron · New York, NY

    • Test automation for warehouse management systems supporting fulfillment operations.
05

Stack

ai & agents

  • Claude
  • MCP Protocol
  • Multi-Agent Architecture
  • Agentic Orchestration
  • RAG · ChromaDB · pgvector
  • Agent instruction design
  • LLM Evaluation
  • AgentOps
  • Cursor

languages

  • Python
  • TypeScript
  • Java
  • JavaScript
  • SQL

backend

  • Node.js
  • Spring Boot
  • REST
  • GraphQL
  • gRPC
  • ETL

frontend

  • React
  • TypeScript

cloud & infra

  • AWS
  • Kubernetes (EKS)
  • Docker
  • GCP
  • Vercel
  • Jenkins
  • Neon Postgres

reliability

  • Selenium
  • Cypress
  • DataDog
  • JMeter
  • REST-Assured
06

Contact

Want to work together, collaborate, or just talk systems? Send a note. For client engagements, head to tapquality.ai. I reply to most things within a day.

Looking to hire? Skip the back-and-forth.

start an engagement ↗