The Core Problem: Agents Without Real Enterprise Context
Enterprise AI agents are software-based assistants that use large language models and automation to perform business tasks across systems, but in production environments they only work when they have rich, accurate operational context drawn from both structured and unstructured enterprise data. Today, many vendors are pushing AI agent deployment strategies that “blow things up”: they ask enterprises to move all data to a single cloud, rebuild business processes, and redesign workflows to fit a new platform. That approach ignores how hard-won enterprise context is embedded in existing content repositories, applications, and human routines. Without that context, agents look impressive in demos yet fail in day‑to‑day work. The gap between what agents see in clean test data and what they meet in live operations explains why so many projects stall after pilots.
Why Over-Engineering New Stacks Backfires
The current hype cycle pushes organizations to replatform for AI, but tearing out working systems to make room for enterprise AI agents adds risk without adding intelligence. Hyland CEO Jitesh Ghai calls this approach “blowing things up” and says it is improper because it ignores the reality of existing content, data, and processes. So many initiatives are failing because vendors under‑estimate the complexity of underlying data and over‑estimate what generic models can infer from documentation and event logs alone. Skan AI notes that agents trained only on those signals do fine in simple cases but falter at the edges, where exceptions, quarter‑end cycles, and regional variations live. A tiny 1% gap in observational coverage can compound to roughly a 40% failure rate once agents execute. Over‑engineering new stacks does not close that gap; it often widens it.
Enterprise Context Engine, Agent Mesh, and the Context Graph
A more practical model is to wrap current systems with an intelligence and integration layer instead of replacing them. Hyland’s Enterprise Context Engine exemplifies this: a governed context graph built from federated content and data, enriched with industry-specific ontologies for healthcare, insurance, financial services, education, and government. On top of that, the Enterprise Agent Mesh coordinates agents that operate across repositories and applications while honoring governance and audit needs. This is backed by a “Control Tower” for observability into decision paths and performance. Context graphs connect documents, entities, and transactions so agents can answer questions and take actions in business terms, not raw file paths. Because the context layer reaches into existing systems through a content federation approach, enterprises gain agent capabilities without migrating everything or rewriting core processes, accelerating time-to-value.
Agentic Business Context Foundation: Capturing the Missing 1%
Even with a strong context graph, enterprises need a way to encode the tacit practices that never appear in manuals. Skan AI’s Agentic Business Context Foundation (ABCF) defines this layer as the operational intelligence that enterprise AI agents depend on. Built from years of direct observation across Fortune 500 operations, ABCF captures the judgment calls, exception paths, and informal workarounds that keep processes running in reality. It is structured using Skan’s Agentic Ontology of Work and refined through an execution‑feedback loop where each agent deployment improves the model. Documentation describes what work should be, and event logs record what systems saw, but neither captures what Skan calls the Signal Paths, Latent Intelligence, or Process Delta where real work happens. ABCF closes that gap so agents can handle the high‑value edge cases instead of failing when processes deviate.
Integration-First Strategy: Meeting Enterprises Where They Are
The emerging consensus from players like Hyland and Skan AI is that the winning strategy for AI agent deployment starts with existing infrastructure integration, not wholesale replacement. Hyland’s Content Innovation Cloud federates content from current repositories, while its Enterprise Context Engine and Agent Mesh make that content usable and governable for agents. Skan’s ABCF adds a business context layer that captures lived operational behavior and feeds it back into agent design. Together, these patterns show how integration-first architectures reduce complexity, shorten rollout cycles, and improve reliability. Instead of reengineering every workflow, enterprises can plug agents into the systems and context they already trust, then gradually expand automation as confidence grows. In this model, context is not an afterthought; it is the product, and the enterprise stack becomes the foundation rather than collateral damage.