Principal Product Manager, Agentic Evals
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- Define what good means and design an evaluation system around real user outcomes
- Operate a statistically defensible, governed eval platform that drives product decisions
Benchmark: Expedia Group Principal Product Manager, Agentic Evals (current Sep 2026) Market refresh: company-wide AI evaluation strategy; agent/task/trajectory evaluation; metrics for accuracy, safety, latency, helpfulness and long-term outcomes; scalable datasets and tooling; LLM-judge calibration; experimentation and statistical reliability; scorecards/release gates; governance, risk, adoption and roadmap recommendations.
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Define what good means and design an evaluation system around real user outcomes
Evaluation foundations, failure taxonomy + product metrics
Coverage: AI Evals / Error Analysis / Product Quality / User Outcomes
Agent evaluation architecture + capability vs regression tests
Coverage: Agent Evals / Task Success / Regression / Test Pyramids
Trajectory/tool-use evaluation + representative datasets
Coverage: Agent Trajectories / Tools / Golden Sets / Experiments / Continuous Eval
LLM-as-judge design, calibration + evaluator reliability
Coverage: LLM Judges / Human Labels / Calibration / Meta-Evaluation / Error Analysis
Operate a statistically defensible, governed eval platform that drives product decisions
Benchmark reliability, repeated trials + release-gate confidence
Coverage: Statistical Reliability / Benchmark Noise / Repeatability / Release Gates
Evaluation governance, safety + continuous optimization
Coverage: Evaluation Governance / Safety / Feedback Loops / Optimization / Org Adoption
Role-Readiness Proof / Capstone
Define and launch an org-level evaluation system for an agentic product portfolio. Create the eval taxonomy and success criteria; build representative golden datasets from realistic tasks and production-like traces; instrument runs, tools and trajectories; implement deterministic, LLM-judge and human evals; calibrate judges; measure accuracy, safety, helpfulness, latency, cost and user outcomes; quantify repeatability; create scorecards and release gates; investigate failures; compare two agent/model versions; and publish governance plus roadmap recommendations.
Proof Rubric
- Prove that the eval system supports product decisions, not leaderboard theater: valid user-centered metrics
- task, outcome and trajectory coverage
- representative data
- calibrated judges with human reference labels
- error analysis
- statistical reliability
- safety/transparency
- reproducibility
- latency/cost guardrails
- production feedback loops
- traceability
- clear release/rollback thresholds
- and roadmap decisions that follow from evidence