Markdown source

From Vibes to Production: Evaluating and Shipping AI Agents That Work 201

Conference Context

Session Description

Building an AI demo is easy. Knowing whether it actually works — and keeping it working in production — is the hard part. Most teams ship agents on vibes: they try a few prompts, the output looks good, and they push to production with no real way to measure quality or catch regressions. This hands-on workshop walks through the full lifecycle of shipping a real AI agent, using a working financial-analyst agent built on the Claude Agent SDK as the running example. You'll instrument it with tracing, do structured error analysis on its actual outputs, and build a layered evaluation suite — from cheap deterministic code checks to LLM-as-a-judge evaluators with custom rubrics. We'll cover the parts most tutorials skip: why agents fail in ways single LLM calls don't, the eval anti-patterns that quietly mislead you, and how to know whether you can even trust your judge (meta-evaluation). Finally, we'll close the loop: turning eval results into datasets and experiments, running evals online against production traffic, wiring them to monitors and alerts, and feeding failure explanations back to a coding agent to actually fix the underlying problems. You'll leave with a runnable notebook and a repeatable, evaluation-driven workflow you can apply to your own agents the next day.

Media Evidence

Ship Real Agents: Hands-On Evals for Agentic Applications — Laurie Voss, Arize (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Evidence Graph

This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

Media Signals

Agent Reading Notes

Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Cached at raw/sources/youtube-transcripts/Xfl50508LZM.txt (22,591 words).

People

Supporting Slides

Slide Evidence

Synthesis

Synthesized Breakdown

Hi everybody. Uh my name's Laurie Voss. I am head of developer experience at Arize AI. Uh in a former life, I co-founded npm Inc.

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Topics Covered

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Novel Concepts / Clever Methods

Evidence Boundary

This synthesis uses the official schedule plus cached video transcripts. Official AI Engineer World's Fair San Francisco 2026 livestreams and cut videos are primary event video sources for transcript/slide evidence; external, historical, or speaker-matched videos remain supporting context unless manually verified as exact official event recordings.