About Fastn UCL

What is it?

Fastn UCL (Unified Command Layer) is a secure, scalable integration layer that connects AI agents to real-world tools and data, so they can go beyond suggestions and take action.

Think of it as a universal remote control for AI agents, letting them interact with real-world tools and services while maintaining security and proper access controls. Fastn UCL brings the protocol to life with a fully managed server that handles authentication, routing, multitenancy, and context management out of the box.

Why do SaaS companies need UCL?

SaaS platforms serve many customers, and each customer often uses a different mix of tools, CRMs, ticketing systems, and messaging apps. To make AI agents truly effective inside a SaaS product, they need to connect to each customer’s unique stack securely and in real time.

Fastn UCL handles this complexity out of the box. It enables you to support custom integrations for every customer while managing authentication, routing, and tenant isolation. So, whether one customer uses Salesforce and Slack and another uses HubSpot and Microsoft Teams, their AI agents can still act intelligently within the right context and without requiring any code changes.

Why do enterprise companies need UCL?

Enterprises need more than just secure integrations. They need smart, permission-aware agents that understand how different teams and departments work, that is also completely owned and controlled by enterprises. For example, marketing might need access to content tools, while IT needs infrastructure access, each with the right boundaries.

Fastn UCL enables this with contextual permissions, team-based access to connectors, and isolated execution per workspace. This allows AI agents to run across different departments with just the right level of access and visibility. Each team’s agent stays secure and focused, even when operating across a shared environment.

Imagine this, You are building a task reminder feature for your SaaS product. Your customers use different tools to receive notifications:

• Customer A prefers Slack.

• Customer B relies on Microsoft Teams.

• Customer C wants reminders via Gmail.

The Challenge with MCP: Using Model Context Protocol (MCP) alone, you can standardize how agents describe actions, but you’re still responsible for building the infrastructure to execute them. Each customer, tool, or environment requires a separate setup:

• Custom logic for routing

• Manual integration for each tool

• Repeated work for every tenant

This quickly becomes brittle, time-consuming, and hard to scale in production.

How Fastn UCL Solves This: Fastn’s Unified Command Layer (UCL) accepts MCP-formatted commands and connects with multiple everyday apps like Slack, Notion, Jira, and more. Each workspace can have multiple users, each with access to different connectors based on the enabled actions (e.g, sending a message or assigning a task).

UCL does the heavy lifting;

• Detects the tenant and user context.

• Routes the command to the correct connector, like Slack, Teams, or Notion.

• Manages authentication, retries, and logging in the background.

You don’t need to write separate logic for each tenant or app. UCL ensures the right action happens in the right context.

Result:

• Reduced development and maintenance effort

• Scalable, multi-tenant support out of the box

• More reliable and flexible integrations

A user interacts with an AI assistant to trigger an intent (e.g., sending a task reminder). The assistant securely connects to Fastn UCL to retrieve the tenant-specific context. Fastn’s UCL server processes the request, selects the right connector based on tenant settings, and executes the action (like sending a message via Slack, Teams, or Gmail). Finally, the result is sent back to the assistant, which delivers the response to the user.

Visual overview of how Fastn UCL components interact:

How Fastn UCL Works

1. Understands AI Commands Accepts MCP-formatted commands from AI agents or user interfaces.

2. Detects Context Identifies the correct tenant, user, and workspace context for each command.

3. Connects to Apps Uses pre-configured connectors to link with the appropriate app or tool.

4. Executes Securely Performs the action with built-in routing, authentication, and tenant-based access.

Why This Approach Works

Fastn UCL enables intelligent routing without the need for hardcoded logic or manual segmentation. You benefit from:

• Simplified integration logic

• Centralized context and configuration

• Consistent data isolation and security

• Faster scalability across varied customer environments

This allows your team to deliver flexible, secure, and tailored experiences to every customer, without reinventing your workflow for each use case.

UCL vs MCP

Fastn UCL transcends MCP's basic protocol by providing a complete, production-ready platform that handles complex enterprise requirements out of the box. While MCP offers the foundational protocol for AI-tool interactions, UCL delivers a fully managed solution with built-in multitenancy, security, and monitoring capabilities.

The key differentiator is that UCL removes the burden of infrastructure management and security implementation from developers, allowing them to focus on building AI features while Fastn handles the complexities of integration, authentication, and tenant isolation.

How UCL changes the game?

Fastn UCL extends the Model Context Protocol (MCP) into a robust, production-ready platform that addresses the practical demands of SaaS integration at scale. It enables AI systems to operate securely, contextually, and reliably across multiple environments.

Embedding: Seamless Integration Within Your Product

Fastn UCL enables deep, native integration with apps directly within your SaaS product. Without requiring end users to manage external configurations, the integration logic remains fully embedded, delivering a unified experience while UCL handles the complexity in the background.

Multitenancy: Native Support for Scaled, Isolated Environments

Fastn UCL is architected for true multitenancy. Each customer (tenant) operates within an isolated environment, with separate credentials and data contexts. This eliminates the need for multiple deployment instances and ensures consistent, secure operation as your customer base scales.

Monitoring: Centralized Observability and Operational Insight

UCL provides comprehensive visibility into all integration activity across tenants. Built-in monitoring and logging tools allow your team to track usage patterns, identify issues proactively, and maintain high system reliability, without the need for additional infrastructure.

Security: Built-In Isolation and Access Control

Security is fundamental to UCL’s architecture. Tenant data and execution contexts are strictly isolated, and access is governed by fine-grained, role-based controls. Every request is authenticated and validated, ensuring that actions taken through AI agents remain secure and compliant with organizational policies.