# Rahul Trikha — AI Engineer Sydney 2026

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Canonical page: https://webdirections.org/ai-engineer/speakers/rahul-trikha/
Program status: The speaker lineup and talk descriptions are public. Session days, times, rooms and the full timetable have not yet been published.

## Give Every Agent a Flight Recorder


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.

## Rahul Trikha

Principal AI Engineer, Zendesk

**Rahul Trikha** is a **Principal AI Engineer at [Zendesk](https://www.zendesk.com/)**, where he builds the platforms and evaluation systems that help AI agents operate reliably in production. His work spans agent observability, trace-based evaluation, durable execution, governed tools, and safe execution at enterprise scale. He is interested in the practical engineering seams that turn promising agent demos into systems teams can trust.

## Conference

- [Conference overview](https://webdirections.org/ai-engineer/index.md)
- [Agent guide](https://webdirections.org/ai-engineer/for-agents/)
- [llms.txt](https://webdirections.org/ai-engineer/llms.txt)