Model Context Protocol (MCP)
MCP is the emerging standard for connecting AI agents to external tools, data sources, and APIs — making agents composable, portable, and auditable across deployment environments.
Model Context Protocol is an open standard developed by Anthropic that defines how AI agents connect to external tools and data sources. Instead of building custom integrations for every AI application, MCP provides a standardized interface: an MCP server exposes resources, tools, and prompts through a common protocol, and any MCP-compatible AI client can use them. This makes agentic capabilities composable — an MCP server built for one application can be reused across multiple AI deployments without rewriting the integration.
The practical significance of MCP for enterprise deployments is substantial. An organization's internal knowledge base, CRM, ERP, and compliance systems can each be exposed as MCP servers with defined capabilities and access controls. AI agents built on top of these MCP servers automatically inherit the access control model of each underlying system. The agent can only do what the MCP server permits — and what the MCP server permits is defined by the access control logic of the underlying business system. This is architecturally superior to giving an AI agent direct database access or API credentials.
MCP adoption is accelerating rapidly in 2026, with major development tools and enterprise platforms adding MCP server support. The standard is gaining adoption across the AI tooling ecosystem — Claude, Cursor, Zed, and a growing list of platforms support MCP clients. Organizations that invest in MCP server development now are building a reusable AI integration infrastructure — not point-to-point integrations that must be rebuilt for every new AI application. This is the right abstraction for enterprise AI at scale.
We build MCP servers as a standard component of enterprise agentic deployments — exposing internal tools, databases, and APIs through the MCP protocol with access controls enforced at the server layer. Our MCP implementations are auditable: every resource access and tool call is logged. We design MCP server architectures that can be reused across multiple AI applications, making your investment in AI integration infrastructure compound over time.
Compliance-Native Architecture Guide
Design principles and a structured checklist for building software that is compliant by default — not compliant by retrofit. Covers data architecture, access controls, audit trails, and vendor due diligence.