---
title: "Agentic vs. Vector Search: An Eval-Driven Approach to Coding Agent Performance"
category: "talks"
date: "2026-06-29"
time: "11:40am-12:00pm"
track: "Expo Stage 2 NW"
room: "Expo Stage 2 NW"
speakers: ["Jess Wang"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: ""
scheduleRoom: "Expo Stage 2 NW"
scheduleLabels: ["Expo Stage 2 NW", "session", "confirmed"]
---
# Agentic vs. Vector Search: An Eval-Driven Approach to Coding Agent Performance

## Conference Context
- Date/time: 2026-06-29 · 11:40am-12:00pm
- Track/room: track TBD · Expo Stage 2 NW
- Speaker(s): Jess Wang
- Session type/status: session · confirmed

- Track: track TBD
- Room: Expo Stage 2 NW
- Session type: session
- Status: confirmed

## Session Description
Evals let you replace gut feelings with quantifiable decisions. This talk breaks the basic concepts of evals, including the four core components: datasets, tasks, scoring, and experiments. Then, to solidify the concept, we’ll walk through a real eval comparing agentic search versus vector search for coding agents. We'll also cover practical challenges like tracing Claude Code subprocess calls and why a single eval run is never enough. You'll leave with a concrete framework for building evals that actually inform your ship decisions.

## Media Evidence
No related AI Engineer channel video found yet.

## 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
No linked video, transcript, or slide source has been attached yet.

### 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
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[jess-wang]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# Agentic vs. Vector Search: An Eval-Driven Approach to Coding Agent Performance ## Conference Context - Date/time: 2026-06-29 · 11:40am-12:00pm - Track/room: track TBD · Expo Stage 2 NW - Speaker(s): Jess Wang - Session type/status: session · confirmed - Track: track TBD - Room: Expo Stage 2 NW - Session type: session - Status: confirmed ## Session Description Evals let you replace gut feelings with quantifiable decisions. This talk breaks the basic concepts of evals, including the four core components: datasets, tasks, scoring, and experiments. Then, to solidify the concept, we’ll walk through a real eval comparing agentic search versus vector search for coding agents.

### Speaker And Company Context
- [[jess-wang|Jess Wang]] — role not listed at company not listed.

### Topics Covered
- [[agentic-search]]
- [[coding-agents]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### 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.