Skan AI, the enterprise context graph company, today introduced the Agentic Business Context Foundation (ABCF), a technical framework that captures the context traditional enterprise systems miss, the human reasoning, exceptions, and workarounds, and turns it into actionable context for AI agents. Agents trained on documentation and event logs perform well in straightforward scenarios but falter at the edges. Those edges are where the high-value work lives: exceptions, quarter-end cycles, regional regulatory variation, the informal workarounds that make operations actually function.
Skan AI introduces Agentic Business Context Foundation (ABCF) for enterprise AI agents.
A 1% gap in observational coverage compounds to approximately 40% failure rate by time agents execute.
ABCF captures Signal Paths, Latent Intelligence, and Process Delta from direct observation of work.
Built on Agentic Ontology of Work released earlier this year.
Framework defines operational context layer beneath relational and informational context layers.
Includes seven-dimensional context model, compounding error taxonomy, and feedback mechanics.
"The enterprise AI community has converged on the right architectural direction with context graphs and business context layers. What is consistently underestimated is where the operational context actually comes from," said Manish Garg, Co-founder and CTO of Skan AI. "Documentation describes what work is supposed to do. Event logs record what systems saw. Neither captures the Signal Paths, the Latent Intelligence, or the Process Delta where real enterprise work happens. ABCF addresses that gap directly."
ABCF is built on years of direct observation across Fortune 500 operations and the developed pathways, judgment calls, and exception-handling routines that never appear in procedure manuals. This accumulated operational intelligence is what AI agents need to execute autonomously in complex environments.
ABCF is built on direct behavioral observation of work as it is executed, structured through the Agentic Ontology of Work that Skan released earlier this year, and refined continuously through an execution-feedback loop in which every agent deployment enriches intelligence rather than eroding it. The framework defines the operational context layer that sits beneath, and determines the effectiveness of, the relational and informational context layers that other enterprise AI architectures address.
The full technical details, including the seven-dimensional context model, the compounding error taxonomy, and the feedback mechanics that prevent corpus corruption, are available in the Agentic Business Context Foundation white paper at this link.
About Skan AI
Skan AI is an enterprise context graph company that provides a living, continuously updated operational record of how work actually happens across every system and application. Its suite of technologies span AI Blueprint, AI Intelligence and AI Agents, and provides an integrated platform designed to support every stage of an enterprise's AI and digital transformation journey, moving from initial planning to operational optimization and final automation.
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