---
title: "Slides: Connecting the Dots with Context Graphs — Stephen Chin, Neo4j"
category: "slides"
video_id: "eW_vxrjvERk"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Connecting the Dots with Context Graphs — Stephen Chin, Neo4j

## Source Video
[Connecting the Dots with Context Graphs — Stephen Chin, Neo4j](https://www.youtube.com/watch?v=eW_vxrjvERk)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/eW_vxrjvERk/slide-001.jpg]]

OCR text:

> PLATINUM SPONSORS
> Braintrust WorkOS OpenAI

![[assets/slides/eW_vxrjvERk/slide-002.jpg]]

OCR text:

> AlEngineer
> Connecting thed
> EUROPE
> withcontextgrap
> Stephen Chin(@steveonjava)
> VPof Developer RelationsNeo4]
> AlEngineer

![[assets/slides/eW_vxrjvERk/slide-003.jpg]]

OCR text:

> AIE
> AI Engineer
> EUROPE

![[assets/slides/eW_vxrjvERk/slide-004.jpg]]

OCR text:

> Scattered,lostreasoning Today
> AIE CRM Why? Alternatives Considered?
> Slack Slack ZendeskSupport Ticket
> JraProject Board
> pd
> PagerDuty Zoom
> Context Lose inPeople's Heads& Silos Tribal Knowledge,Cross-System Synthess ApprovalChains Outside Systems
> AIEngineer
> EUROPE

![[assets/slides/eW_vxrjvERk/slide-005.jpg]]

OCR text:

> Hype cycle for Agentic Al
> AIE EXPECTATIONS Hunan-Agent Collaborbonwospace AIAgentDe Domai Context Graphs Emtarprine Al Assistants- eticAIGo AgeticA/Security Agentic Analytics Multingeet Systes No-Code Agent Buders AlCodingAgonts Machine O Model Conlext Protocol certcAl stomers
> EmbodedAl
> AlAgnt Communicaticn Protoool Agent Managam tPatform
> AlAgentsin SofthwareEngine Agent Marketpiace
> FinOgs for Agentic Al AgenticBrowser Coetinul Leaig Agentc Commerce
> Guardlan Agent Agent Experonce(Ax Computer Use for Agents Agent Deveiopent Lre Cycle Ageet Orchestration Platesu will be reached: 2ym 2-5y O5-10ym 10yrx Obsclete before plateau AsetAgri2026
> Innovation Peak of Inflated Expectations Disillusionment Troughof TIME Enlightenment Productivity Plateauof
> AIEngineer
> EUROPE

![[assets/slides/eW_vxrjvERk/slide-006.jpg]]

OCR text:

> AIE
> AI Engineer
> EUROPE

![[assets/slides/eW_vxrjvERk/slide-007.jpg]]

OCR text:

> Knowledge graph components of
> Narre “Dan” Cee
> eS AIL) ene Y LS)
> at N odes Arce cuane SC
> * a Earns
> * * represent entitics in the graph ie) we!
> * * ecleniind
> dl . A & be ean &
> Relationships
> G a
> a stop echt tet lene Reg an ~~
> interactions between nodes aa 7
> Jan 10, 2011
> i
> o=0
> Properties
> ; 7 ory
> ice] Ike ei pL area GAL DIU Ce MON MR IOIG (3101) Hae oh
> relationships including vectors, can be indexed Bheccen pt oo “An executive car manutactured and...”
> eae eae (ORC OR SOO |
> eR RMERMaS TC UT SI SaRee ai seee eat Serr SE CT 22
> i
> A
> Al Engineer
> EUROPE

![[assets/slides/eW_vxrjvERk/slide-008.jpg]]

OCR text:

> Enhance relevance with domain context a
> What was in the Care Plans associated with Andrea Jenkins'’s emphysema?
> Pane a
> ba bd
> “ae
> bd ad
> * vs bd
> LLM Direct
> TTT over Neodj ay call
> eet ae wy i wot
> connected
> ic a OP ee C toreal
> ee oo
> | Al Engineer |
> EUROPE

![[assets/slides/eW_vxrjvERk/slide-009.jpg]]

OCR text:

> . A a)
> Enhance relevance with domain context ;
> What was in the Care Plans associated with Andrea Jenkins'’s emphysema?
> ae Baseline RAG
> . ‘ OEP hese VCR stats ey mel od Me Ae)
> * re and Chroma vector database
> Grounded but
> Incomplete
> | Al Engineer |
> EUROPE

![[assets/slides/eW_vxrjvERk/slide-010.jpg]]

OCR text:

> Enhance relevance with domain context a.
> What was in the Care Plans associated with Andrea Jenkins'’s emphysema?
> Pane a
> Pd a
> ae
> * 4
> bd * 5d
> GraphRAG
> Neo4j, LangChain,
> pres ee ree
> fotevantolintten i sea & 7 - ' - .
> a heen, errno
> | Al Engineer |
> aU elas

![[assets/slides/eW_vxrjvERk/slide-011.jpg]]

OCR text:

> I KNOW KUNG FU
> Engineering the future of AI

![[assets/slides/eW_vxrjvERk/slide-012.jpg]]

OCR text:

> AA
> Long-term memory 7
> Persistent knowledge graph of entities, relationships, and learned preferences
> rane a
> * bd
> aco
> va ia a Person eae ne Te (ot eat Core Rene banana Saleh Canes Cad el ae ona ed
> a *
> : (Oleecein 1 aeh tole Meee cerca en (eee eT Ser CTS ore ee COOL LS
> eee ee one nth cea te eC EeaReel ee Ta
> Tre ble ate eet Ed Cr cd
> Aco te pebae pe erent Race Oia eee ee eS
> Google DeepMind

![[assets/slides/eW_vxrjvERk/slide-013.jpg]]

OCR text:

> Wal
> Reasoning memory ;
> Decision traces, tool usage audits, and provenance — the layer that makes Al explainable
> a * &
> . * Tool cell treices Decision provenance
> ra Pa Eseory toolopyccat un, | V1 Why cad the geet ohcose Us path?
> ara Paramerers Pew Ges Cavsal chain tubs recerdecs
> eecrpiete aud t tra |
> Learning from Complicnce &
> experience 3 4 debugging
> Agentebeces ditscwed SOs MCS CELE Sie St
> potetiine sep lar before Wore. youd cae Peace pectly
> Telos sl ceeofu palteres wat nappened ard why
> | Al Engineer |
> SUL ela

![[assets/slides/eW_vxrjvERk/slide-014.jpg]]

OCR text:

> Building memory
> Tool call traces
> 1
> Learning from experience
> 3

![[assets/slides/eW_vxrjvERk/slide-015.jpg]]

OCR text:

> a)
> Why graphs for agent memory? 7
> Pond aa Q
> © il Connec tons betwcer entities. decisions, and Foilaw chains: Customer + Account +
> bad * events are the data potan atterthougnt Trapsaction + Deoson +» Polcy + Employer
> Sg ad
> areas
> o @
> Graph embedd nes (PasthRPi tind s.milar Ful provenance chain trace exactly
> situations by 1eteors topoiosy, mot rust text how and why every decis on was made
> (@) co
> & i
> roams
> Information lesrned in ane conversanan Necdy ACID compliance, enteror se scale.
> PWR CTerirerol Calla ma) tne Lee cutan Cera Ten Orel ars) proven technolosy
> Engineering the fut A

![[assets/slides/eW_vxrjvERk/slide-016.jpg]]

OCR text:

> . a
> Neo4j-agent memory :
> Complete Agent Memory API
> Po : ; ‘
> * * Short-Term Long-Term Reasoning
> Bd 4 Cometsater cistern & Knowledge grach of entities Context graphs for
> we va Picoen Meraal Coral of relator ships Pore Sa nnn Dm Laas Cord aT roe
> rd
> add_message() add_entity() start_trace()
> PT rice) Cele) TF ied ult Sst TO) irate mest sO)
> oI T ES Cle! TSO eee eto) record_tool_call()
> OATS e TT TO} get_locations_near() rors OS ee ale Tee)
> Cote T rt e] PLN SS Se eT TO) Pte ree Teh rs
> '
> Neo4j Context Graph
> Vector Search + Graph Traversal
> | Al Engineer |
> SUL ela 3

![[assets/slides/eW_vxrjvERk/slide-017.jpg]]

OCR text:

> “ a eR keuperrony verce app e > mg 1 7 x
> ~ DP Conversations ZG) Lenny’s Memory O Abou Po Gua
> ° ©
> Cd Agent Configurapen
> Don
> ° & Ayere Copatete et
> - —— , Ask about Lenny’s Podcast
> Pel 2” Bar ep teas coe:
> ° Use memory graph search to bet Pte wart wR we : 5
> * * A explore what quests ay about aan wiee ee dwn 2 oe unm Be
> re r 9 erameteeonnny
> . Shon me Incotions Tennaned © “fF Faplore a Top< “Top Companes ” Se Te
> ty * . the Bean Cesena 2 " sr Who ts Bran Pa .
> . oo oo . : . “8 figieee tan aiey
> Use memary geaph search 10 woe
> fexgilure ate! guests uy about
> a Parris +: Speaker Quotes oh Map Wiew Seema miige La aes
> how do Dat conednste 4 4 . ao “ :
> . . * we wae i P Restate locas a
> OES Ee Xo lead Call Cart ?
> py Eheotpabuagent. 2,
> | HISD we
> | Al Engineer |

![[assets/slides/eW_vxrjvERk/slide-018.jpg]]

OCR text:

> Contextgraphbasedretrieval
> AIE Search ContextGraphMemory RetrievalTools AgentLoop Contextual Memory
> Retrieval Query Information Relevant
> neo4j
> Knowledge Graph + Vectors GraphData Science
> Engineering the future of Al

![[assets/slides/eW_vxrjvERk/slide-019.jpg]]

OCR text:

> £2 A 5 al
> Context graph: financial services data model ;
> Financial Services Context Graph
> Ss ENTITIES (What Exists)
> id * SCH aot me Cetera eA a PTicea UST le-esnPen aera)
> au Cd ie ee
> PY oy
> ket :
> EVENTS (What Happened)
> Decis:ons, Transactions, Approvals, Rejections =
> CONTEXT (The Why) oo nel me
> Policies Applied. Risk Factors. Employee Reasoning
> De
> oer
> Engineering the future of Al

![[assets/slides/eW_vxrjvERk/slide-020.jpg]]

OCR text:

> Context graph demo architecture
> Ai-powered decision tracing for financial institutions
> DATA SOURCES
> Support Ticket System
> CRM (e.g. Salesforce)
> Internal Business Data
> Engineering the future of AI

![[assets/slides/eW_vxrjvERk/slide-021.jpg]]

OCR text:

> AIEngineer
> EUROPE
> LUIACC
> 2026 AlEngineer

![[assets/slides/eW_vxrjvERk/slide-022.jpg]]

OCR text:

> AIE
> Engineering the future of AI

![[assets/slides/eW_vxrjvERk/slide-023.jpg]]

OCR text:

> a)
> Neodgj
> Graph Academy
> re rf
> 7 rd 7 EAS aaa Sree eee ceca Cee SeaTac
> RS Re eee SOOM ere ecm eM eer OCR AM] me ON ee Rea Bese
> WE heer tes Larceny obey
> Oe
> See
> https://dev.neo4j.com/ga-rag
> Google DeepMind

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.