Conference In 6 weeks
December
Monday
to Tue 8 December 2026 · Sydney time
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Speakers: Rahul Trikha (Zendesk)
Track: AI Engineering
Agent teams should not need to file a ticket with a central evaluation team just to learn whether a new prompt, model, or tool made their agent better. At Zendesk, we developed and deployed a trace-first evaluation platform that gives every agent a flight recorder: a versioned, safe trail from execution to release decision.
The stakes are real. Our agents generate millions of executions each month. To illustrate the scaling challenge, even a modest sample with several graders can create hundreds of thousands of evaluation jobs—before retries, comparisons, or human review. The hard problem is no longer writing a score; it is preserving trust under load.
In 18 minutes, we’ll show the platform in action. We’ll take a deliberately broken tool trajectory, inspect the recorded spans that expose the failure, and watch the CI gate reject the change without exposing raw customer payloads. Then we’ll unpack the design that lets teams self-serve without turning quality into chaos: immutable trace datasets and snapshots, versioned evaluators, metadata-first evidence, scoped access, governed content review, quotas, back-pressure, and comparable baselines.
The result is a quality loop teams can own, with guardrails the platform can enforce. The takeaway: empower teams to learn from every agent execution—but make “cannot verify” a visible outcome, never a green check.
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