Enterprise Agentic AI Platform
A five-zone enterprise agentic-AI platform that lets non-technical business teams self-serve AI-generated insights from governed enterprise data across six business domains.
A Fortune-class retail client wanted to adopt an enterprise agent platform at scale, but the vendor's capabilities were complex and pre-GA, and business teams couldn't access AI insights without deep infrastructure expertise — meaning every query still required a technical intermediary.
Architected a five-zone platform with deliberate governance boundaries and a structured agent taxonomy, translating complex vendor capabilities into practical adoption roadmaps across omnichannel, finance, marketing, pricing, and two further domains. Designed a hybrid agent connectivity layer — API gateway, MCP server patterns, and direct data warehouse access — enabling business teams to self-serve without infrastructure expertise. Served as primary technical liaison to the cloud vendor's engineering team, running working sessions to validate component fit, producing gap analyses, and defining interim governance patterns that unblocked delivery ahead of general availability.
Adoption roadmap accepted across six business domains. Gap analyses and interim governance patterns unblocked delivery planning before the platform reached GA.
The hardest part of this engagement wasn’t the architecture — it was the translation layer. The vendor’s enterprise agent platform was sophisticated but pre-GA, documented for engineers, and opaque to the business stakeholders who needed to commit to it. The five-zone model and agent taxonomy existed to give non-technical executives something concrete to reason about: clear boundaries, clear ownership, and a roadmap they could actually approve.
The hybrid connectivity design — API gateway routing for controlled access, MCP server patterns for tool-level integration, BigQuery-direct for high-volume data products — reflects a deliberate tradeoff between flexibility and governance. Business teams needed self-service; the enterprise needed auditability. The architecture had to deliver both without forcing teams to choose.
The gap analysis work was equally consequential. Identifying where the vendor platform wasn’t production-ready, documenting interim patterns to fill those gaps, and routing field feedback back to the vendor’s engineering team converted deployment friction into a structured backlog rather than a blocker.
This project sits at the direct intersection of what the tools directory curates: MCP server patterns, agentic architecture, and enterprise AI governance.