Defining AI agents on existing enterprise infrastructure
AI agents on existing enterprise infrastructure are software assistants that use contextual data, workflows, and controls from current systems rather than replacing those systems, so they can automate tasks, reason over complex situations, and act within established business rules and compliance constraints. Vendors are converging on this integration-first view as they see how fragile “greenfield” agent deployments can be. Skan AI’s Agentic Business Context Foundation (ABCF) and Hyland’s Enterprise Context Engine both treat the existing stack as the source of truth, not an obstacle. Instead of ripping out legacy platforms, they add a context graph framework and an operational intelligence layer that capture how work really happens. This approach reframes AI agents as a strategic extension of enterprise infrastructure, reducing disruption while improving automation quality.
Context layers: from ABCF to Enterprise Context Engine
Two emerging designs show how context-aware frameworks make AI agents use legacy systems more effectively. Skan AI’s ABCF defines an operational intelligence layer grounded in an “Agentic Ontology of Work,” built from direct observation of how employees handle exceptions, regional regulatory quirks, and informal workarounds. According to Skan AI, “a 1% gap in observational coverage compounds to roughly a 40% failure rate by the time agents execute,” so this hidden context is not optional. Hyland’s Enterprise Context Engine plays a similar role for content-centric enterprises, federating documents from existing repositories, structuring unstructured data, and organizing it into a governed knowledge graph shaped by industry ontologies. Both products show how a context graph framework can sit above existing stack deployment, giving AI agents the situational awareness they need without new core platforms.
The risks of starting from scratch with AI agents
Replacing core systems to become “agent-ready” may sound clean, but it often multiplies operational complexity. Hyland CEO Jitesh Ghai calls this approach “blowing things up,” arguing that enterprises do not need to revisit all business processes or move all data into a new cloud platform to gain AI value. Fresh stacks lack the historical nuance encoded in legacy systems, user behavior, and exception paths that frameworks like ABCF capture. They also introduce new governance gaps: security models, audit trails, and compliance controls must be rebuilt from the ground up. At enterprise scale, where data is messy and workloads are regulated, this reinvention can stall AI initiatives instead of accelerating them. Integration-first AI agents keep processes stable while adding intelligence, reducing change fatigue and avoiding risky big-bang transformations.
Turning legacy system integration into an AI advantage
For many enterprises, legacy system integration is now a strategic asset for AI agents rather than a constraint. Hyland’s Content Innovation Cloud, Enterprise Agent Mesh, and upcoming governance “Control Tower” show how vendors are building agent meshes that reach into existing content stores and line-of-business systems, automate what Ghai calls “human ETL,” and expose decisions for oversight. Skan AI, meanwhile, focuses on capturing the “Signal Paths” and “Process Delta” that never appear in manuals or logs, then feeding that intelligence back into each agent deployment. Together, these approaches let AI agents inherit enterprise workflows, data classifications, and compliance rules instead of rebuilding them. Teams gain automation that understands quarter-end spikes, regional regulations, and informal workarounds, while retaining the observability, control, and reliability they expect from their current stacks.
