Markdown source

Your Agent Is Lying to You About Whether It Worked

Conference Context

Session Description

Every span is green, every tool call returned cleanly, and the agent still regenerated the same plan 27 times before giving up invisible to any outcome metric, obvious in the trajectory. We pull up a real trace where the outcome looks healthy and the path is a disaster, then show Signal, our agent, surfacing it automatically: sweeping the project, ranking it above the noise, and linking straight to the offending trace with debugging evidence attached. The live version of the trajectory-over-outcomes argument, with a one-click path from "something's wrong" to "here's exactly where."

Media Evidence

LLM Observability, Evaluation, Experimentation Platform — Dat Ngo, 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. Not fetched yet.

People

Supporting Slides

Slide Evidence

Synthesis

Synthesized Breakdown

Your Agent Is Lying to You About Whether It Worked ## Conference Context - Date/time: 2026-06-29 · 12:05pm-12:25pm - Track/room: track TBD · Expo Stage 1 NE - Speaker(s): Dat Ngo - Session type/status: session · confirmed - Track: track TBD - Room: Expo Stage 1 NE - Session type: session - Status: confirmed ## Session Description Every span is green, every tool call returned cleanly, and the agent still regenerated the same plan 27 times before giving up invisible to any outcome metric, obvious in the trajectory. We pull up a real trace where the outcome looks healthy and the path is a disaster, then show Signal, our agent, surfacing it automatically: sweeping the project, ranking it above the noise, and linking straight to the offending trace with debugging evidence attached. The live version of the trajectory-over-outcomes argument, with a one-click path from "something's wrong" to "here's exactly where." ## Media Evidence LLM Observability, Evaluation, Experimentation Platform — Dat Ngo, Arize (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions). - Source video: youtube-JsCCrBF7F1g - Slide deck: Dense Slides: LLM Observability, Evaluation, Experimentation Platform — Dat Ngo, Arize — 1 visible slide image(s); 1 HTML recreation(s).

Speaker And Company Context

Topics Covered

Derived Links And Source Material

Novel Concepts / Clever Methods

Evidence Boundary

This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.