Testing & Evaluation Articles
35 articles · Page 1 of 3
How to Build a Regression Test Suite for AI Agents
Your CI/CD pipeline catches code regressions. But who catches it when a prompt change breaks your agent's compliance behavior? Here's how to build behavioral regression testing for non-deterministic AI agents.
Synthetic Users: Test Your Agent Against AI Personas
Scripted tests catch only the failures you anticipated. Build AI-powered synthetic users that simulate real customers and break your agent before it ships.
How to Build a Trajectory Eval for Your AI Agent
Outcome evals check the final answer. Trajectory evals check the path: tools called, data touched, steps taken. Here's how to build one for a CX agent.
Cost Per Successful Outcome: The AI Agent Metric Teams Miss
Most teams measure AI agent quality by pass rate. The metric that actually predicts ROI is cost per successful outcome: what each resolution costs paired against whether it actually resolved. Here's how to build it.
Trajectory Eval: Catch Agent Bugs Output Scoring Misses
Final-output scoring misses 20-40% of agent regressions. Trajectory evaluation scores every step an agent takes -- tool calls, reasoning decisions, order of operations -- and catches the bugs that output-only evals can't see.
Your Agent Has Observability. It Doesn't Have Measurement.
89% of AI teams added observability. 52% added evals. But only 31% can say whether their agent is getting better or worse. Here's the difference between watching your agent and actually measuring it.
AI Agent KPIs: What to Measure Before You Ship
Only 31% of teams have a measurement framework for their AI agents. Here's how to define task completion rate, escalation rate, cost per outcome, and CSAT delta before your first production interaction.
GPT-5, Claude 4.5, Gemini Score the Same Calls. Their Kappa Is 0.52
Run the same calls through GPT-5, Claude 4.5, and Gemini and Cohen's kappa lands at 0.52. Here is how to measure judge agreement on your own corpus.
How to Eval Agents When There's No Right Answer
Most eval methods assume you know the correct response. CX agents rarely have one. Here's how to score agent quality with criteria-based rubrics and LLM-as-judge, no labeled ground truth required.
Stop Using SWE-Bench to Pick Your CX Model
SWE-Bench scores 85% or 23% depending on the harness, and neither measures customer experience. Why tau-bench, tau2-bench, and pass^k matter for CX agents.
Every Conversation Is an Experiment You Didn't Run
Your agent already ran the A/B test you're scoping. Here's how to read the results in your logs with propensity matching, synthetic control, and diff-in-diff.
Is AI Better Than Your Humans? Score Both on One Rubric
Most teams can't say whether AI beats humans because they score them differently. One rubric, run on both, sliced by segment, gives you an honest answer.
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