Engineering insight for AI systems.
Practical research for teams testing, evaluating, securing and releasing AI agents where a good answer is only one part of the system.
Featured research
Start with the systems problem.
Agentic AI Latency: How Engineering Leaders Should Measure and Diagnose Agent Response Time
A practical engineering guide to measuring latency in multi-turn agentic AI, separating TTFT from response latency, tracing LLM, RAG, tool and orchestration delays, and finding the critical path.
Agentic AI latency is not one number. Engineering leaders need a consistent response boundary, separate responsiveness from completion, and trace the execution path to understand where latency originates.
agentic AI latency
Research library
Browse the assurance disciplines.
Testing strategy, evaluation, security and execution evidence—organized around the decisions engineering teams actually need to make.
When Retrieval Looks Right but Answers Are Wrong: A RAG Reliability Blueprint
An original engineering blueprint for tracing RAG failures from source ingestion to generated claims, with architecture diagrams, reproducible evaluation and release controls.
AI Agent Testing: Why Response Quality Is Not Enough for Production Assurance
An AI agent can produce a good answer and still fail the customer's journey. Learn how to test outcomes, orchestration, tools, deterministic contracts, security, and evidence as one assurance system.
AI Test Design for Business Workflows
A practical method for translating business workflows into risk-based, traceable tests for software and AI-enabled systems.
AI Test Engineering for Tax and Immigration Workflows
A practical quality engineering playbook for validating document extraction, classification, AI-generated guidance and agentic workflows in regulated service delivery.
DeepEval Rubric Scores: How to Make LLM Evaluation More Consistent
A practical guide to DeepEval rubric scoring, including score bands, evaluation steps, thresholds, and how to use rubric-based semantic evaluation in an AI assurance workflow.
The Problem With Green Checkmarks on Broken Conversations
Why semantic scores can look healthy while an agent journey has actually failed, and how an execution-integrity gate prevents false passes.
What LLM-as-Judge Actually Means in Practice
LLM-as-judge is a semantic evaluation layer, not a universal truth oracle. Here is how to use rubrics, context and evidence without creating false confidence.
Why Conversational AI Needs a Different Testing Model
Why agent tests should validate goals and acceptable trajectories instead of replaying one fixed conversation transcript.
Evidence first
Claims should connect to observable execution evidence, not only generated language.
Outcome focused
Test the business goal and critical invariants, not one ideal conversation transcript.
System aware
Treat orchestration, tools, retrieval, security and model behaviour as one system.
See it evaluate your own agent
Bring one real scenario. We'll run it against your live conversational AI agent and walk through every judged turn with you.