The Structured Assembly of Decision-Relevant Information

The Context Graph

The Context Graph—more precisely, the Governed Context Graph— is the structured representation of all information relevant to a specific decision, assembled in real-time and governed by organizational policies. It answers the question: "What does an AI agent need to know to make this particular decision correctly?"

The term "Context Graph" reflects its structure: information organized as interconnected nodes and relationships rather than flat data records. A customer isn't just a row with attributes—it's an entity connected to their purchase history, support interactions, contract terms, assigned representatives, and behavioral patterns. These connections matter for decision-making. An isolated data point tells you what; connected context tells you why and what it means.

The qualifier "Governed" distinguishes this from raw data graphs. A Governed Context Graph isn't just assembled—it's curated according to policy. What information is included depends on the decision type, the agent's authority level, data access policies, and relevance criteria. An agent making a routine customer service decision receives different context than one evaluating a fraud alert, even for the same customer. Governance determines scope.

Dynamic Assembly at Decision Time

The Context Graph is constructed dynamically at decision time, not maintained as a staticartifact. When an agent needs to make a decision, the Context Plane queries underlyingsystems, assembles relevant entities and relationships, applies governance filters, anddelivers a decision-specific context package. This just-in-time assembly ensures context isfresh (addressing Context Rot) and relevant (addressing Context Pollution)

This just-in-time assembly ensures context is fresh (addressing Context Rot) and relevant (addressing Context Pollution).

How It Differs from Related Concepts

The Context Graph differs from related concepts in important ways:

• Knowledge Graph — The persistent store of organizational information, the repository from which context is drawn

• Context Graph — The decision-specific extract from that repository, filtered and structured for a particular decision

• Decision Graph — Maps relationships between decisions over time; the Context Graph captures information state at decision time

They work together: the Knowledge Graph provides raw material, the Context Graph structures it for decisions, and the Decision Graph records what decisions resulted.

Structural Elements

Several structural elements compose a Governed Context Graph:

• Entity Layer — Primary objects relevant to the decision: customer, transaction, product, employee

• Relationship Layer — How entities connect: ownership, association, dependency

• Temporal Layer — Historical patterns: past behavior, similar situations, trends

• Policy Layer — Organizational rules: constraints, thresholds, required authorities

• Meta Layer — Context about context: freshness, confidence, gaps

Addressing the Four Failure Modes

The Context Graph directly addresses the Four Failure Modes that plague enterprise AI:

• Context Rot — The graph includes freshness metadata; agents know when information was captured and can request refresh

• Context Pollution — Governance filters exclude irrelevant information; agents receive signal, not noise

• Context Confusion — The graph includes interpretive context, not just data but the ontological framework for understanding meaning

• Decision Amnesia — The graph can include relevant precedents: how similar situations were handled and outcomes

The Core Insight

AI decision quality is bounded by context quality.

The smartest model in the world cannot make good decisions from bad context. Improving AI in enterprise settings means improving context—making it more comprehensive, more relevant, more governed, and more interpretable. The Context Graph is the structure that makes this improvement systematic rather than ad hoc.

Organizations that master Context Graph assembly gain sustainable advantage in AI deployment. Their agents make better decisions because they have better context. Their governance is stronger because context access is policy-controlled. Their audit trails are cleaner because context is captured at decision time.

The Context Graph isn't just a technical construct—it's the foundation for trustworthy enterprise AI.

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