Playbook for Establishing an Enterprise Context Layer
Build the Context Layer That Grounds Your Enterprise AI Agents
As enterprises move AI from pilot to production, the limiting factor is no longer model capability. It is the context the agent needs to act reliably on real business work. Gartner® identifies context graphs as the foundational layer that grounds AI agents in the declarative, procedural, and causal knowledge required to reason about enterprise tasks and decisions, with policies enforced through runtime constraints. Without that context layer, agents operate on stateless logic over stateful data, and their outputs become difficult to trust, audit, or scale.
This research gives data and analytics leaders a practical playbook for establishing an enterprise context layer. It is essential reading for any organization moving agentic AI from experiment to enterprise scale.
of leaders plan to increase AI and analytics spending in 2026 compared with 2025
report current AI costs exceed value realized
of enterprises use knowledge graphs today
of enterprises use ontologies today
Source: Gartner, Context Graphs Ground AI Agents With Enterprise Knowledge, 15 September 2026.
A context graph captures how an organization operates and runs in reality, including its processes, decision flows, causal influences, and policies. GARTNER® · Context Graphs Ground AI Agents With Enterprise Knowledge, September 2026
How Skan AI addresses the enterprise context layer
Agentic AI needs more than a defined task to act reliably. It needs a real-time, accurate picture of how work is actually executing across an organization’s systems and teams. Skan AI treats the context layer as having three components. Operational state is the one Gartner research directly addresses, and the one Skan AI is purpose-built to deliver.
Skan AI delivers operational state through the Context Graph of Work. It captures live process activity across applications, decision traces, and workflow handoffs, giving AI agents the operational ground truth they need to act on current business reality rather than assumed process models.
Financial services and insurance operations represent the highest-stakes deployments for operational state. Skan AI maps real process execution across claims workflows, underwriting decisions, KYC reviews, and prior authorization queues so AI agents have accurate context before they act.
What We Believe You'll Learn in This Report
Why do 42% of enterprises say AI agent costs exceed value realized?
Gartner research on the gap between AI spending and realized value, and why context is the missing layer behind it.
What are the three types of knowledge graphs that ground AI agents?
How declarative, procedural, and causal graphs each contribute to agent reasoning, and why combining them is becoming the preferred approach for enterprise agentic systems.
What is the difference between context at rest and context in motion?
The distinction between back-end semantic enrichment and the runtime practices that let agents access and apply context dynamically, and why both are required for reliable autonomous decision making.
How do you codify business logic for an agentic system?
The automated rule mining pattern that extracts candidate rules from SOPs and legacy code, formalized as machine-enforceable constraints using graph-based standards.
How do you harden agents before production?
The shadow mode validation approach that pairs reasoning traces with SHACL-based gatekeepers and human-in-the-loop review, hardening agents against hallucinated actions before release.
How We Believe Gartner Recommendations Map to Skan AI
| Gartner Recommendation | How Skan Delivers It |
|---|---|
| Build domain-specific knowledge graphs that capture procedural and causal knowledge | The Context Graph of Work builds the graph continuously from observed enterprise work across every application |
| Mine business logic from SOPs and legacy code | Skan AI Intelligence extracts process patterns and business logic from observed desktop activity, without parsing documents or source code |
| Deploy the graph as a foundational context layer for agentic systems | Agent Operating Procedures (AOPs) encode the Context Graph of Work into runtime structures that govern how Skan Agents execute and escalate |
Skan AI is recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Process Intelligence Platforms. It is deployed at seven of the top ten U.S. banks and three of the top five U.S. insurers.
Gartner, Context Graphs Ground AI Agents With Enterprise Knowledge, Afraz Jaffri, Andrés García-Rodeja, 14 Sept 2026.
Gartner and Magic Quadrant are a trademark of Gartner, Inc., and/or its affiliates.
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.
Frequently Asked Questions
The Context Graph of Work is Skan AI’s living, observed model of how work actually happens across every application, team, and system in an enterprise. It is a property graph where nodes represent people, applications, tasks, decisions, policies, and outcomes, and edges represent the relationships, sequences, and governance constraints between them. It is Skan’s implementation of what Gartner calls a context graph: the foundational context layer that grounds AI agents in operational reality.
Traditional knowledge graphs focus on static entities and relationships. A Context Graph of Work adds procedural and causal information, meaning it captures not just what things are, but how work moves through them and why decisions get made the way they do. It is also built continuously from observed desktop activity, not from documentation or system event logs, so it reflects the exceptions, workarounds, and handoffs that documented processes miss.
Context at rest is back-end semantic enrichment through ontologies and knowledge graphs. Context in motion is the runtime practices that let AI agents access and apply that context dynamically during task execution. The Context Graph of Work supports both, serving as the structured source of truth and the runtime reference agents query during decisions.
Without a context layer, agents operate on stateless logic over stateful data. They cannot maintain state across decisions, explain their reasoning, or reliably apply enterprise policies. The Context Graph of Work gives them observed procedural and causal context, which is what makes autonomous enterprise agents trustworthy in production.
Skan observes how work actually happens across every enterprise application, including legacy systems and mainframes, with no back-end integrations required. Those observations are structured into the Context Graph of Work and distilled into Agent Operating Procedures (AOPs) that govern how Skan Agents and third-party agentic systems execute and escalate.
