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Defining and Monitoring Quality Metrics in an Agentic RAG QE System
It is easy to fall in love with shiny AI testing dashboards. They glow with promise: self-healing tests, intelligent agents, adaptive pipelines. But the fundamental question we need an answer is: Is all this intelligence actually making quality better? That’s the question I had the first time our RAG-based test agents started generating their own […]
Deploying the Testing Pipeline: Orchestration, Scale, CI/CD, Cloud Execution
There’s a point in every AI-driven testing journey when the theory stops being exciting and reality starts talking back. Your RAG agents reason beautifully in notebooks, your dashboards sparkle with early wins — and then you plug it all into your CI/CD. That’s when you realize: orchestration isn’t just about running tests; it’s about making […]
Building the Automation Framework and Toolchain for Agentic RAG in QE
If you’ve spent years maintaining automated tests, you already know the grind: flaky scripts, brittle locators, test failures after every UI tweak, and constant firefighting. Now imagine a framework that not only notices when something changes, but also understands why — pulls the latest documentation, reasons through code commits, regenerates the broken tests, and validates […]
Designing an Agentic RAG Workflow for Quality Engineering: Architecture, Agents & Retrieval Strategy
I’ll never forget the moment: our team had just merged a major micro-service feature into production at 2 a.m. The next morning the first defect came in — stemming from a change we thought was low-risk. We had a robust regression suite, yet somehow, the edge case slipped through. It hit me: our testing process […]
Testing Agentic RAG: Retrieval Accuracy, Source-Grounded Answers, and Multi-Step Workflow Assurance
Agentic RAG fails in the spaces between steps: missing or stale retrieval, untraceable claims, and agents that over-act. This guide turns those failure modes into testable contracts with gates for retrieval (recall/diversity/freshness), groundedness (span-aligned evidence, clean citations), and agent workflows (steps, cost, latency, repair). Wrap it in CI/CD, add observability, and you’ll ship answers you can defend. Omniit.ai helps you do it at scale.
Testing Agentic AI: Validating Reasoning, Tool Use, and Autonomy with Action-Safe Test Automation
The first time I tested an agentic AI system, I went in with the same mindset I’d always had as a tester. I was ready to look at input–output mappings, validate correctness, and hunt for boundary conditions. But instead of simply producing an answer, the system paused, invoked a tool, reasoned through multiple steps, and […]