An enterprise agent needs integration infrastructure when it must work with business systems beyond its own chat. The Model Context Protocol (MCP) gives applications a standard way to discover and call tools. A production deployment also has to decide whose account an action uses, how credentials are protected, where servers run, and what users can review.
What MCP provides
MCP is an open standard for connecting AI applications to external data and tools. A server exposes capabilities, and a client discovers and invokes them. The official architecture guide explains the roles of hosts, clients, and servers.
What a company still has to build
A protocol does not choose permissions for a customer or deploy a service into an enterprise environment. The integration needs authorization flows, credential handling, monitoring, and interfaces that show users what the agent can do. The right design depends on the systems being connected and the actions the agent may take.
Sweetspot's implementation
Sweetspot asked Martian to connect its platform agent to customer software. We built MCP servers for Microsoft and Google Workspace workflows, deployed them in Sweetspot's cloud, bridged an enterprise authorization gap with a proxy, and protected customer secrets using AWS Key Management Service. Product design and UX were part of the same engagement. The Sweetspot case study describes the system and its constraints.
Martian's software development service covers the applications, integrations, and data systems around agent workflows. The work may use MCP where its tool interface fits the task; the protocol is one part of the production system.
