Context Graph Glossary
Every Key Term Defined
The authoritative reference for context graph terminology. 25 terms covering context graphs, AI agent infrastructure, decision intelligence, and related concepts.
Context Graph
A decision-aware knowledge structure that determines what is valid, applicable, and authorized in a given situation — enabling AI agents to operate deterministically, auditably, and at enterprise scale.
A context graph extends knowledge graphs with five additional layers: temporal validity, applicability logic, exception handling, decision traceability, and provenance. Where a knowledge graph answers 'what is true?', a context graph answers 'what is valid right now, for this situation?' This makes it the decision infrastructure layer beneath autonomous AI systems.
Knowledge Graph
A structured representation of real-world entities and the relationships between them, storing facts as triples (subject → predicate → object).
Knowledge graphs model the world by mapping entities, relationships, and semantic meaning. Google's Knowledge Graph, Wikidata, and enterprise knowledge graphs are prominent examples. They excel at information retrieval, discovery, and semantic enrichment but do not govern decisions — they describe relationships without enforcing temporal validity, applicability, or policy constraints.
State Drift
A divergence between an AI agent's internal assumptions about the world and the actual state of the systems it interacts with.
State drift occurs when an agent's model of reality falls out of sync with execution state across tools and systems. This causes subtle reliability degradation as the system scales — not through dramatic failures, but through compounding minor errors. State drift is the primary reason production AI agents degrade over time, and it cannot be solved by observability alone.
Decision Trace
A recorded reasoning chain that captures what data was consulted, what alternatives were considered, what outcome resulted, and why that decision was justified.
Decision traces are what separate a context graph from a knowledge graph. They enable every decision to be replayed, audited, and queried. In production AI systems, decision traces provide the audit trail required for compliance, debugging, and trust. They capture not just the decision itself but the full chain of evidence and reasoning that led to it.
Temporal Validity
The constraint that rules, contracts, policies, and data have effective dates — expired logic cannot execute, and future-dated rules do not apply prematurely.
In enterprise systems, almost everything has a time dimension: contracts expire, policies get superseded, approvals have windows. Temporal validity makes time a structural constraint rather than optional metadata. A context graph with temporal validity can answer: 'Was this rule valid at the time the decision was made?' and 'Which version of this policy was in effect on date X?'
Applicability Logic
The mechanism that determines which rules, policies, or constraints apply to a given situation, based on context, conditions, and scope.
Not every rule applies to every situation. Applicability logic is the layer that evaluates context and determines which subset of rules, policies, and constraints are relevant. This is critical for enterprise AI agents that must navigate complex regulatory environments where different rules apply to different jurisdictions, customer segments, product types, or time periods.
Provenance
The origin, authority, confidence score, and data lineage of every piece of information in a context graph.
Provenance tracks where data came from, how trustworthy the source is, whether it has been corroborated, and how it was transformed. In a context graph, provenance is a first-class citizen embedded in every node and edge — not optional metadata stored elsewhere. This enables trust scoring, conflict resolution, and source attribution for AI-generated decisions.
Context Engineering
The practice of designing, structuring, and managing the contextual information that AI systems use to make decisions.
Context engineering goes beyond prompt engineering. While prompt engineering optimizes individual inputs to language models, context engineering designs the entire information architecture that surrounds AI decision-making — including what context is available, how it is structured, when it expires, and how conflicting contexts are resolved. It is the discipline of building reliable context graphs.
Context Collapse
The loss of critical contextual information as data flows through AI pipelines, transformations, and summarizations.
Context collapse happens when information is processed through multiple stages — retrieval, chunking, summarization, embedding — and the original reasoning, conditions, and qualifications are stripped away. By the time context reaches a decision point, the nuances that made it actionable are gone. Context graphs prevent this by preserving the full contextual chain.
Tool Misalignment
When an AI agent's actions through external tools do not align with the actual constraints, permissions, or intended outcomes of the task.
Tool misalignment occurs when an agent selects or parameterizes tools incorrectly — not because the LLM lacks capability, but because the constraints governing tool usage are not structurally encoded. The agent may have the right information but apply it to the wrong tool, with the wrong parameters, at the wrong time. This is an infrastructure problem, not an intelligence problem.
Decision Infrastructure
The structural layer between language models and execution that validates, constrains, and governs AI agent decisions before they are acted upon.
Decision infrastructure is the missing architectural layer in most production AI systems. It provides deterministic validation of state transitions, enforcement of business rules and policies, exception handling, and audit-grade traceability. A context graph serves as the foundation of decision infrastructure — it is the system that ensures AI agents make authorized decisions, not just plausible ones.
Deterministic Validation
The process of verifying that an AI agent's proposed action satisfies all applicable rules, constraints, and policies before execution — with a guaranteed pass/fail outcome.
Unlike probabilistic outputs from language models, deterministic validation produces binary results: an action is either valid or it is not. There is no 'probably allowed.' This is achieved by evaluating proposed actions against the context graph's rules, temporal constraints, applicability conditions, and exception logic. It is what makes enterprise AI agents trustworthy.
Exception Handling (Context Graph)
The treatment of exceptions, overrides, and edge cases as first-class citizens in a context graph — not as afterthoughts or bugs.
In enterprise reality, exceptions are the norm, not the edge case. A manager override, a regulatory exemption, a contractual carve-out — these are all exceptions that must be modeled explicitly. Context graphs treat exceptions as structured entities with their own provenance, temporal validity, and decision traces, rather than hard-coded special cases.
Entity Resolution
The process of identifying whether incoming data refers to an entity that already exists in the graph, merging duplicates, and flagging conflicts.
When new data enters a context graph, entity resolution determines whether it represents a new entity or an update to an existing one. Unlike simple deduplication, entity resolution in a context graph considers provenance, temporal context, and confidence scores. Conflicts between sources are flagged with metadata rather than silently overwritten.
Decision Intelligence
The discipline of capturing, analyzing, and leveraging organizational decision history to improve future decision-making.
Decision intelligence uses context graphs as institutional memory — preventing context loss when people leave and enabling evidence-based decisions. It goes beyond business intelligence (which analyzes what happened) by analyzing why decisions were made, what alternatives existed, and what outcomes resulted. Context graphs provide the structural foundation for decision intelligence systems.
AI Overview Optimization (AIO)
The practice of structuring content so that AI systems — including ChatGPT, Google AI Overviews, Perplexity, and Claude — can accurately understand, cite, and surface it in AI-generated answers.
AIO goes beyond traditional SEO by optimizing for how AI models understand and cite content. This includes providing structured data (JSON-LD), maintaining clear entity definitions, creating LLM-readable content formats (llms.txt), and establishing topical authority through comprehensive, well-structured content. Sites that structure content around context graph principles are more likely to be cited in AI-generated answers.
Retrieval-Augmented Generation (RAG)
A technique that retrieves relevant text chunks from a knowledge base and injects them into an LLM's prompt to ground its responses in specific data.
RAG improves LLM outputs by providing relevant context at inference time. However, RAG has fundamental limitations for production AI: semantic similarity does not equal applicability, chunks lose their surrounding context, there is no temporal awareness (expired information is retrieved alongside current), and there is no decision provenance. Context graphs address these gaps by providing structured, governed context rather than raw text retrieval.
Vector Database
A database optimized for storing and querying high-dimensional vector embeddings, enabling semantic similarity search across large datasets.
Vector databases power semantic search by converting text, images, and other data into numerical vectors and finding similar items based on distance metrics. They are essential for RAG pipelines and recommendation systems. However, they have no concept of temporal validity, decision traces, or applicability logic — they find what is similar, not what is valid or authorized.
Audit Trail
A chronological record of all decisions, actions, and state changes in a system, enabling post-hoc verification and compliance review.
In regulated industries, audit trails are mandatory. Context graphs produce audit trails naturally through decision traces and provenance tracking. Every decision is recorded with its inputs, reasoning, outcome, and the authority under which it was made. This makes context graphs essential infrastructure for AI systems operating in finance, healthcare, insurance, and other compliance-heavy environments.
Context Window
The maximum amount of text (measured in tokens) that a language model can process in a single inference call.
Context windows limit how much information an LLM can consider at once. Even with expanding context windows (100K+ tokens), the fundamental problem remains: stuffing more text into a prompt does not provide structured applicability, temporal validity, or decision governance. Context graphs solve a different problem than context windows — they ensure the right context is available, not just more context.
Guardrails
Output-level constraints that filter, validate, or modify an LLM's responses before they reach the user or downstream systems.
Guardrails constrain outputs — they check what the model said after it said it. Decision infrastructure (via context graphs) constrains inputs and governs the decision process itself. Guardrails are necessary but insufficient for production AI: they catch surface-level issues but cannot prevent state drift, ensure temporal validity, or provide decision traceability. Reliable AI systems need both.
Memory Degradation
The progressive loss of accuracy and relevance in an AI agent's stored memory as volume increases and context evolves over time.
As AI agents accumulate memory through sessions, conversations, and interactions, the quality of that memory degrades. Older memories may be outdated, contradictory to newer information, or irrelevant to current context. Without structured memory management — temporal validity, provenance tracking, and relevance scoring — agent memory becomes a liability rather than an asset at scale.
Structured Data (Schema.org / JSON-LD)
Machine-readable markup added to web pages that helps search engines and AI systems understand the content's meaning, relationships, and context.
Structured data using schema.org vocabularies (typically in JSON-LD format) tells search engines and AI models what entities exist on a page, how they relate, and what type of content is presented. For AIO, structured data is critical — it helps AI systems accurately categorize, understand, and cite content. Context graphs and structured data share a common principle: making implicit relationships explicit.
Cite This Glossary
Joubert, P. (2026). “Context Graph Glossary.” The Context Graph. Retrieved from https://thecontextgraph.co/glossary
Related Resources
What is a Context Graph?
Complete definition, components, and enterprise use cases.
Context Graph vs Knowledge Graph
The architectural difference between mapping reality and governing decisions.
Context Graphs for AI Agents: Resource Guide
Curated resources, tools, and critical perspectives.
Building production AI agents? Missing a term?