Markdown source

Your agents lack context: Here's how to fix "You're absolutely right!"

Conference Context

Session Description

Every AI coding tool can generate code. Very few can generate the right code for your organization, because they're missing context. They don't know why your team chose Redis over DynamoDB, what the team decided in a Slack thread earlier today about the auth migration, or which architectural patterns your principal engineers actually enforce in review. This talk is a practitioner's guide to building a context engine: the reasoning layer that continuously ingests & synthesizes organizational knowledge across disparate sources into unified, queryable understanding. I'll walk through the problems you actually have to solve — reasoning across systems that don't agree with each other, searching globally before you can reason, maintaining identity-scoped permissions so every user and agent only sees what they should, and personalizing results based on who's asking and what they're working on. These are the engineering challenges that make naive RAG fall short, drawn from real lessons building this at scale.

Media Evidence

Stop babysitting your agents... — Brandon Waselnuk, Unblocked (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

Attendance Visibility

No high-confidence attendance icon signal is shown for this talk. The sampled video evidence was either low confidence, source-proxy-only, or did not expose a clear audience view.

Synthesis

Synthesized Breakdown

Your agents lack context: Here's how to fix "You're absolutely right!" ## Conference Context - Date/time: 2026-06-30 · 12:05pm-12:25pm - Track/room: Context Engineering · Track 8 - Speaker(s): Brandon Waselnuk - Session type/status: session · confirmed - Track: Context Engineering - Room: Track 8 - Session type: session - Status: confirmed ## Session Description Every AI coding tool can generate code. Very few can generate the right code for your organization, because they're missing context. They don't know why your team chose Redis over DynamoDB, what the team decided in a Slack thread earlier today about the auth migration, or which architectural patterns your principal engineers actually enforce in review. This talk is a practitioner's guide to building a context engine: the reasoning layer that continuously ingests & synthesizes organizational knowledge across disparate sources into unified, queryable understanding.

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.