Fundamentals
What is a Context Graph?
A context graph is a living record of decision traces stitched across entities and time, so precedent becomes searchable. It's the missing layer that enables AI agents to learn from institutional memory and make autonomous decisions.
What is a Context Graph?
A context graph is a structured representation of knowledge that captures not just what entities are and how they relate, but the full context in which those relationships exist and matter. Put simply, it's a triples-representation of data that is optimized for usage with AI.
Unlike traditional databases that record what happened, context graphs capture why it was allowed to happen. They store the exceptions, overrides, precedents, and cross-system context that currently live in Slack threads, deal desk conversations, escalation calls, and people's heads.
“Rules tell an agent what should happen in general. Decision traces capture what happened in this specific case.”
Over time, these records form connections between entities the business cares about—accounts, renewals, tickets, incidents, policies, approvers, agent runs—connected by decision events and “why” links. The context graph becomes the real source of truth for AI autonomy.
Why Context Graphs Matter
Enterprise value is shifting from “systems of record” (Salesforce, Workday, SAP) to “systems of agents.” In this new paradigm, the crown jewel isn't the CRM or ERP—it's the context graph.
Without Context Graphs
- ✗AI agents follow rules but miss exceptions
- ✗Tribal knowledge stays locked in people's heads
- ✗Every edge case requires human escalation
- ✗No audit trail for AI decisions
With Context Graphs
- ✓AI agents learn from precedent and handle exceptions
- ✓Institutional memory becomes searchable
- ✓Compound intelligence with every decision
- ✓Complete traceability for compliance
“Without context graphs, an AI agent is like an extremely smart intern on day one—it can follow written rules but gets tripped up by every unwritten exception.”
Context Graph vs Knowledge Graph
While the terms are sometimes used interchangeably, context graphs and knowledge graphs serve fundamentally different purposes:
| Aspect | Knowledge Graph | Context Graph |
|---|---|---|
| Primary Purpose | Store facts and relationships | Capture decision reasoning |
| Data Type | Static entities and relationships | Dynamic decision traces |
| Temporal Aspect | Current state | Historical context and evolution |
| Optimized For | Information retrieval | AI agent decision-making |
| Key Question | “What is X?” | “Why was X decided?” |
Context graphs expand upon conventional knowledge graph structures by incorporating additional information such as time validity, geographic location, and source provenance. They're designed to answer not just what exists, but why it exists and under what circumstances.
Key Components of a Context Graph
1Decision Traces
The core data structure. Decision traces capture not just the outcome, but the specific context, reasoning, exceptions, and approvals that led to that outcome. They record the “why” behind every automated and human decision.
2Entity Relationships
Connections between business entities—accounts, people, products, policies, tickets, and more. These relationships provide the structural foundation for understanding how decisions impact different parts of the organization.
3Temporal Context
Time-aware storage that captures when decisions were made and how context evolved. This enables AI agents to understand not just current state but historical patterns and precedent.
4Provenance Metadata
Source tracking for every piece of information. Who made the decision? What system recorded it? What evidence supported it? This creates the audit trail required for enterprise AI governance.
How Context Graphs Work
Context graphs operate through a continuous cycle of capture, connection, and retrieval:
- 1
Capture Decision Events
When a decision is made—whether by a human or AI agent—the context graph captures the full context: who made it, what information was available, what alternatives were considered, and why this outcome was chosen.
- 2
Connect to Entities
Each decision trace is linked to relevant business entities—the customer involved, the products affected, the policies applied, the approvers consulted. These connections form a rich web of institutional knowledge.
- 3
Enable Precedent Search
When an AI agent encounters a new situation, it queries the context graph for similar past decisions. “Show me how we handled tier-1 customer escalations about pricing exceptions in Q4.”
- 4
Compound Intelligence
Every new decision adds another trace to the graph, creating compound intelligence. The system gets smarter with every interaction, building a comprehensive map of how the organization actually works.
Context Graph Use Cases
Customer Support Automation
AI agents access historical resolution patterns, customer-specific exceptions, and escalation precedents to handle support tickets autonomously.
Deal Desk & Pricing
Capture every pricing exception, discount approval, and deal negotiation to enable AI-powered deal structuring with institutional knowledge.
Compliance & Audit
Maintain complete decision lineage for regulatory compliance (EU AI Act, SOX, GDPR) with full traceability of AI agent actions.
Workflow Orchestration
Enable AI agents to understand and navigate complex approval workflows by learning from historical routing patterns and exceptions.
The Trillion-Dollar Opportunity
Foundation Capital has called context graphs “AI's trillion-dollar opportunity.” The thesis is straightforward: as AI agents become more prevalent in enterprise operations, the organizations that capture and leverage decision context will have a massive competitive advantage.
“We've spent decades building systems that record what happened. We're about to spend the next decade building systems that record why it was allowed to happen.”
Data is no longer the new oil; it's decisions—the map of how the organization actually works.
Gartner predicts that 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from essentially none in 2024. This shift requires a new data layer—one that captures the institutional memory AI agents need to operate with true autonomy.
Getting Started with Context Graphs
The Context Graph Marketplace is building the platform for organizations to discover, share, and monetize decision traces. Our marketplace connects:
- ●Enterprises looking to build context graphs from their existing decision data
- ●AI agent developers who need rich contextual data to build more capable agents
- ●Industry experts who can contribute domain-specific decision patterns and best practices
Join the Waitlist
Be among the first to access the Context Graph Marketplace when we launch.
Get Early AccessFrequently Asked Questions
What is a context graph?
A context graph is a living record of decision traces stitched across entities and time, so precedent becomes searchable. Unlike traditional databases that store what happened, context graphs capture why decisions were made, enabling AI agents to learn from institutional memory.
What is the difference between a context graph and a knowledge graph?
While knowledge graphs store static relationships between entities (facts), context graphs capture dynamic decision traces including the reasoning, exceptions, and temporal context behind each decision. Context graphs are optimized for AI agent decision-making, while knowledge graphs are optimized for information retrieval.
Why do AI agents need context graphs?
Without context graphs, an AI agent is like an extremely smart intern on day one—it can follow written rules but gets tripped up by every unwritten exception. Context graphs provide the institutional memory and decision precedent that AI agents need to handle edge cases and make autonomous decisions.
What are decision traces?
Decision traces are records that capture not just what decision was made, but the specific context, reasoning, exceptions, and approvals that led to that outcome. They form the core data structure within a context graph.
How do context graphs support AI compliance?
Context graphs provide complete decision lineage for regulatory compliance. With frameworks like the EU AI Act requiring organizations to demonstrate AI traceability, context graphs create the audit trail needed to show how and why AI agents made specific decisions.