# gupta-garg-context-graphs-trillion-dollar-opportunity-2025-12-22

## Veille

Foundation Capital Context Graphs - new generation of systems of record for AI agents

## Titre Article

AI's trillion-dollar opportunity: Context graphs

## Date

2025-12-22

## URL

https://x.com/JayaGup10/status/2003525933534179480

## Keywords

context graph, system of record, AI agents, decision traces, orchestration, enterprise, data, governance, startups, workflows

## Authors

Jaya Gupta, Ashu Garg

## Ton

Profile: VC investor article (Foundation Capital), strategic analytical register, investment thesis perspective.
Style: Assertive and structured tone, contrast-based argumentation (incumbents vs startups). Uses concrete examples (Salesforce, Workday, SAP) to ground the abstraction. Enterprise software technical vocabulary mixed with VC jargon. Quotes and references to other analyses (Jamin Ball). Target audience: founders, investors, enterprise tech decision-makers.

## Pense-betes

- Core thesis: the next trillion-dollar platforms will not come from adding AI to existing data but from capturing "decision traces"
- Key distinction: Rules (what should happen) vs Decision Traces (what happened in this specific case)
- Context Graph = structured record of decision traces linked across entities and over time
- What current systems lack: exception logic, precedents, cross-system synthesis, informal approval chains
- Structural advantage of "systems of agents" startups: they are in the execution path, not reading after the fact
- Limits of incumbents: Salesforce/Workday store the current state, not the state at the moment of decision; Snowflake/Databricks receive data after decisions via ETL
- 3 paths for startups: replace existing SoRs, replace modules, create new SoRs
- Examples cited: Regie (AI SDR), Maximor (finance), PlayerZero (production engineering), Arize (agent observability)
- Signals for founders: high headcount on workflow, decisions with exceptions, "glue" organizations between systems

## RésuméDe400mots

Jaya Gupta and Ashu Garg of Foundation Capital develop a thesis on the emergence of a new generation of systems of record centered on decision traces rather than traditional business objects.

The previous generation of enterprise software created a trillion-dollar ecosystem by becoming systems of record: Salesforce for customers, Workday for employees, SAP for operations. The current question is whether these systems will survive the shift to AI agents.

The authors agree with Jamin Ball's analysis that agents do not replace systems of record but raise their requirements. However, they identify a critical missing layer: decision traces. These capture the exceptions, waivers, precedents, and cross-system context that currently live in Slack, deal desk conversations, escalation calls, and employee memory.

The fundamental distinction opposes rules (what should generally happen) to decision traces (what happened in this specific case, under which policy, with which exception, on which precedent). Agents need access not only to the rules but to the history of their application.

"Systems of agents" startups have a structural advantage: they sit in the execution path. They see the full context at the moment of decision and can persist these traces as durable artifacts. The accumulation of these traces forms a "context graph": a living record of decisions linked across entities and over time, where precedent becomes queryable.

Incumbents cannot build this context graph. Operational systems like Salesforce store the current state, not the state at the moment of decision. Data warehouses like Snowflake receive data via ETL after decisions are made, losing the decision context.

The authors identify three paths for startups: replacing existing systems of record (such as Regie for sales engagement platforms), replacing specific modules (such as Maximor for finance), or creating new systems of record for categories of truth never captured before (such as PlayerZero for production engineering).

Signals for identifying these opportunities include high headcount on manual workflows, decisions rich in exceptions, and the existence of "glue" organizations (RevOps, DevOps, SecOps) that exist precisely because no system captures the cross-functional workflow.

## GrapheDeConnaissance

- Jaya Gupta —publie→ thèse context graphs (CONCEPT, 0.95)
- Ashu Garg —publie→ thèse context graphs (CONCEPT, 0.95)
- Foundation Capital —emploie→ Jaya Gupta (PERSONNE, 0.9)
- Foundation Capital —emploie→ Ashu Garg (PERSONNE, 0.9)
- context graph —est_basé_sur→ traces de décision (CONCEPT, 0.95)
- context graph —remplace→ systèmes de record traditionnels (CONCEPT, 0.85)
- startups systems of agents —surpasse→ incumbents (Salesforce, Workday) (ORGANISATION, 0.88)
- Salesforce —utilise→ état actuel pas état décisionnel (CONCEPT, 0.9)
- Snowflake —utilise→ données reçues après les décisions via ETL (CONCEPT, 0.88)
- Regie —remplace→ plateformes sales engagement (TECHNOLOGIE, 0.82)
- exceptions, précédents, contexte cross-système —fait_partie_de→ traces de décision (CONCEPT, 0.9)
- agents IA —améliore→ exigences des systèmes de record (CONCEPT, 0.85)

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Canonical: https://www.thekb.eu/en/fiches/gupta-garg-context-graphs-trillion-dollar-opportunity-2025-12-22/