One architecture for your entire AI estate — before sprawl becomes debt you can’t unwind.
Your teams are already building AI agents. The problem isn’t whether they work — it’s that each one has its own platform, its own identity model, its own integrations, and no shared control plane. That isn’t an AI strategy. It’s an architecture gap that compounds every quarter. 27Global designs the target-state architecture that makes every agent you build consistent, governed, and cheaper to deliver.
The Salesforce Connectivity Benchmark found enterprises now deploy an average of 12 AI agents, and 94% of IT leaders say sprawl is increasing complexity and technical debt. Half of enterprise agents run in isolated silos, ungoverned. Gartner projects that 50% of agent deployment failures through 2028 will stem from governance gaps.
The economics compound the problem. Integration cost exceeds model cost by three to five times in production, and every team that builds independently rebuilds the same connectors, the same identity plumbing, and the same observability from scratch.
The enterprises that win are not the ones with the most agents. They are the ones whose agents share one governed architecture.
A platform-agnostic logical model plus per-platform physical models, so the standard survives a platform decision changing.
Registry, identity and RBAC, lifecycle management, federation, model and MCP gateways, an observability standard, and policy enforcement.
How agent identity works across agent platforms and your enterprise identity providers, including delegation and run-as-user patterns.
Explicit selection criteria for single cloud, multi-cloud, or hybrid — with the reasoning documented so the decision can be revisited honestly.
Knowledge and RAG strategy, integration bus, agent identity and secrets, guardrails, and audit architecture.
Cost attribution, budgets, model routing, and chargeback designed before spend becomes politically difficult to control.
An ARB charter with real decision rights, plus an exception process that people will actually use instead of route around.
An ADR process and a pattern library, so the reasoning behind a standard outlives the person who set it.
How agents get specified, built, reviewed, and released — including agent rules files, trust levels, and machine-readable specs.
Costed by wave and tied directly to the delivery engagements that implement each one.
Ongoing advisory and ARB participation, for organizations that need the authority present without a full-time hire.
This engagement defines the target state, the standards, and the decision rights. It does not deliver production agents or run ongoing agent operations — deliberately, so the architecture stays independent of any one build.
AI Agent Factory, Enterprise Data Platform, AI-Ready Cloud Transformation, and Agent-Native App Modernization implement the approved target state. AgentOps Governance implements and operates its operational portions.
Inventory the AI estate, map cloud, data, and identity, and surface the architecture debt already accumulating across teams.
Logical and per-platform models, platform strategy, build-versus-buy decisions, and the principles and standards that govern them.
Registry, identity and RBAC, lifecycle, federation, gateways, observability standard, and the shared MCP tool layer.
Knowledge and RAG strategy, integration bus, security model, agent identity and secrets, and the AI FinOps model.
AI Architecture Review Board, ADR process, pattern library, and the agent-first SDLC your teams will work inside.
Sequenced, costed waves tied directly to the delivery engagements that will execute them.
Enterprise AI spending reached $37B in 2025, more than tripling from $11.5B in 2024. Gartner projects 40% of enterprise applications will embed AI agents by the end of 2026, up from under 5% in 2025. The architecture decisions being made right now will be expensive to reverse.
Senior architect-led, but the same team ships production agents. The target state we design is one we know how to implement, because we implement it.
The architecture typically pays for itself by preventing duplicated platform and integration spend across the first two or three build projects.
We architect across AWS Bedrock AgentCore, Azure AI Foundry, Databricks Mosaic AI, and open source. The platform strategy follows your constraints, not our incentives.
An ARB charter, an ADR process, and a pattern library with real decision rights. Standards nobody can enforce are just documentation.
MCP has become the integration standard, with 97M monthly SDK downloads as of March 2026 and more than 9,400 servers in the public registry. Designing the shared tool layer once, correctly, is what keeps every subsequent agent cheap to build.
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The Discovery phase is focused on several activities that will identify all current data-related elements and map the flow of data throughout its lifecycle.
