---
title: "From Systems of Record to Systems of Context"
category: "talks"
date: "2026-07-01"
time: "12:05pm-12:25pm"
track: "Graphs"
room: "Track 5"
speakers: ["Omri Bruchim"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Graphs"
scheduleRoom: "Track 5"
scheduleLabels: ["Graphs", "Track 5", "sponsor", "confirmed"]
---
# From Systems of Record to Systems of Context

## Conference Context
- Date/time: 2026-07-01 · 12:05pm-12:25pm
- Track/room: Graphs · Track 5
- Speaker(s): Omri Bruchim
- Session type/status: sponsor · confirmed

- Track: Graphs
- Room: Track 5
- Session type: sponsor
- Status: confirmed

## Session Description
Enterprise AI agents are moving fast, but most of them still hit the same wall in production: they have access to tools, documents, APIs, and databases, but they do not understand the real context of how work gets done. At monday.com, we are building agents that operate across real customer workflows, internal product surfaces, knowledge, permissions, memory, and actions. The hard part is not just calling the right tool or retrieving the right document. The hard part is building a reliable context layer that helps agents understand users, work objects, organizational knowledge, prior decisions, business rules, and the relationships between them. This talk will explore the emerging idea of the context graph: a living, queryable layer that connects entities, history, permissions, decisions, and meaning across an organization. Foundation Capital describes context graphs as the next major enterprise AI opportunity because agents need more than rules. They need decision traces: how rules were applied, where exceptions were made, who approved what, and what precedent actually governs reality. I will share how we think about this opportunity at monday.com, how we are implementing parts of it in practice, and what we have learned from building AI agents inside a real AI work platform. The talk will include concrete examples, including how context is collected, represented, retrieved, governed, and evaluated. The audience will leave with a practical framework for moving beyond one-off RAG pipelines and prompt stuffing toward a reusable context layer that compounds over time, improves agent quality, and becomes a strategic moat for companies building AI-native products.

## 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
- [[omri-bruchim]]

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

## Synthesis
### Synthesized Breakdown
# From Systems of Record to Systems of Context ## Conference Context - Date/time: 2026-07-01 · 12:05pm-12:25pm - Track/room: Graphs · Track 5 - Speaker(s): Omri Bruchim - Session type/status: sponsor · confirmed - Track: Graphs - Room: Track 5 - Session type: sponsor - Status: confirmed ## Session Description Enterprise AI agents are moving fast, but most of them still hit the same wall in production: they have access to tools, documents, APIs, and databases, but they do not understand the real context of how work gets done. At monday.com, we are building agents that operate across real customer workflows, internal product surfaces, knowledge, permissions, memory, and actions. The hard part is not just calling the right tool or retrieving the right document. The hard part is building a reliable context layer that helps agents understand users, work objects, organizational knowledge, prior decisions, business rules, and the relationships between them.

### Speaker And Company Context
- [[omri-bruchim|Omri Bruchim]] — Engineering Group Manager at [[monday|Monday]].

### Topics Covered
- [[agent-security]]

### 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.