Venkata Kari
Notes on Agentic AI
These are the technical insights I gained from building and testing LLMs across multiple projects. I’ve focused on optimizing how these models perform in real-world scenarios, balancing speed, cost, and accuracy to get the best results.
Recent posts
All posts →Field Notes · Agentic QA Strategy
Data Privacy and Agentic AI in Testing: Aligning Your QA Stack With UK ICO Guidance
9 min read
ReadEngineering Notes · Agentic QA
Don't Break Checkout: Agentic QA for Revenue-Critical Retail Funnels
12 min read
ReadField Notes · Agentic QA Strategy
Self-Healing Mobile Test Automation in CI: What Actually Works for iOS, Android, and React Native
10 min read
ReadCase studies
All studies →Case Study · Web E2E
Your CI Isn't Broken. It's Flaky — And Retries Are Hiding the Bill
3.30% — flake rate at retries:0 (2.93% genuine)ReadCase Study · Mobile / React Native
Your Mobile Test Suite Is Probably Healthy. Your Mobile QA Still Isn't
474 / 474 — unit tests passing — types and lint cleanReadCase Study · Reference Pipeline
We Built a SaaS With Agentic QA Wired In From Commit One
30 / 30 — OpenAPI operations with passing contract testsReadGet in touch
Let's talk agentic QA
Thinking about agentic AI in your QA stack, or want to discuss anything in the writing? Drop me a line — I reply to every email.
vkari@vkari.co.uk