Evaluation Platform for Trustworthy AI Agents

Context

I owned the product strategy for a generalized benchmark and evaluation platform for internal AI agents, starting with integrity investigation agents.

The platform helped teams create reusable benchmarks, evaluate agents, compare versions, detect regressions, analyze failures, and establish quality gates before and after launch. The long-term vision was to become the CI/CD system for AI quality: a shared way to prove whether agents could be trusted in production.

The problem

Agent evaluation was fragmented and manual. Teams were creating benchmarks through spreadsheets, custom scripts, one-off graders, manually collected examples, and agent-specific execution workflows. That made evaluation expensive, inconsistent, hard to reproduce, and difficult to compare across teams.

For high-stakes integrity work, this was dangerous. An agent could sound fluent while hallucinating, skipping required evidence, using the wrong source system, or producing an answer that looked plausible but was not safe to rely on.

The insight

Evals are not a dashboard after the product is built. For agentic systems, evals are part of the product architecture.

A useful benchmark had to be more than a set of prompts. It needed versioned datasets, explicit rubrics, trusted golden examples, repeatable execution, failure analysis, benchmark health, and ownership. It also had to evaluate more than final-answer accuracy. For agents, the path matters: which tool was used, which evidence was retrieved, which source was trusted, and whether the agent followed the right steps to reach the answer.

What I did

I defined the product vision for a generalized agent evaluation platform: a centralized place where teams could create benchmarks, manage datapoints, write rubrics, version datasets, run evaluations, compare agent versions, and analyze failures.

I personally created the first golden dataset and rubric for the integrity investigation use case. I translated real investigator questions into structured benchmark cases with expected answers, grading criteria, and failure modes. That became the reference template other agent teams could follow.

I shaped the core platform objects: agents, benchmarks, benchmark versions, datapoints, rubrics, runs, graders, results, and trust dimensions.

I also defined the evaluation workflow: select an agent, attach a benchmark, run it through a shared execution engine, grade outputs with deterministic or LLM-based rubrics, inspect failed examples, compare results across versions, and turn production failures back into benchmark cases over time.

Finally, I pushed the product from raw eval outputs toward a product-facing quality system: dashboards, score breakdowns, regression alerts, failure clustering, category performance, benchmark health, ownership, review status, and approval gates that PMs, engineers, and safety teams could actually use.

Impact

• Made high-stakes agent launches defensible by replacing ad hoc testing with versioned benchmarks, explicit rubrics, reproducible runs, and actionable failure analysis.
• Created the first golden dataset and rubric for integrity investigation agents, then generalized the method into a reusable platform pattern for other agent teams.
• Helped teams ship faster with more confidence by making regressions visible before launch instead of discovering quality issues through user trust loss.
• Turned evaluation from a fragmented engineering task into a shared product system for agent quality, governance, and launch confidence.

What I learned

Evaluation is not just a quality gate. It is the infrastructure that determines whether an AI system can be trusted.

A benchmark is only useful if the ground truth is current, the rubric is explicit, the inputs are stable, and failures are explainable. A single score can hide the most important issues; teams need to know whether a failure came from hallucination, missing evidence, wrong tool use, bad grounding, rubric ambiguity, or dataset drift.

Agent evaluation is harder than model evaluation because the path matters. A final answer may be correct, but the agent may have used the wrong tool, skipped a required source, taken an unsafe action, or relied on evidence it should not have trusted. For agentic systems, evaluation has to measure the trajectory, not just the response.