Unlocking agentic AI potential with MCP tools in SAS Retrieval Agent Manager

This article was co-written with Saurabh Mishra and Ben Nibbelink.

Retrieval-Augmented Generation (RAG) has moved quickly from experimental AI architecture to a foundational enterprise capability. However, as organizations adopt RAG at scale, a new need has emerged: AI must be able to do more than retrieve information. It must be able to trigger workflows, interact with systems, produce structured outputs, and participate in real business processes.

The Model Context Protocol (MCP) is the technology that enables this shift. MCP allows large language models to interact with tools, services, and APIs in a secure, governed, and predictable way. With MCP server capabilities built into SAS Retrieval Agent Manager (RAM), we unlock a new class of retrieval agents capable of reasoning, retrieving, and taking meaningful action.

This blog explains how MCP enhances the SAS Retrieval Agent Manager and why this combination is a game changer in delivering safe, scalable, and operational AI for enterprises.

Why MCP matters for enterprise RAG systems

The Model Context Protocol defines a standard interface for how LLMs discover, understand, and call external tools. This addresses one of the biggest challenges in enterprise AI: how to make AI systems interact with live operational environments without sacrificing governance, safety, or clarity.

MCP gives AI agents:

A list of tools they can safely use

Clear schemas describing each tool’s expected inputs and outputs

Mechanisms for executing actions through controlled tool servers

Predictable failures, logs, and structured error handling

A standard interface for integrating with enterprise systems

Instead of relying on long prompts or unpredictable natural language instructions, MCP gives agents explicit, machine-readable instructions about what they can do and how they should do it.

Inside SAS Retrieval Agent Manager, this foundation elevates agents from passive assistants to reliable operational agents.

What MCP Enables Inside SAS Retrieval Agent Manager

SAS Retrieval Agent Manager delivers strong agentic retrieval capabilities today, and MCP integration elevates it from providing insights to empowering agents that can act on them, bridging retrieval with real operational processes across the enterprise.

Turning retrieval agents into action agents

SAS Retrieval Agent Manager capabilities currently include:

Unstructured data processing & intelligent retrieval: RAM handles data ingestion, vectorization, and optimal RAG pipeline selection, enabling high-quality querying and reliable agent interactions over unstructured content.

No-code automation: RAM connects data sources, embeddings and agents through a drag-and-drop user interface to automate complex end-to-end workflows in a seamless and a consistent way.

By integrating MCP, RAM moves from insight to action, allowing agents to:

Trigger enterprise APIs and service endpoints

Initiate workflows and business processes

Make database calls and automatically discover schema

Update or query internal applications

Perform multi-step tasks requiring tools and decisions

Strengthening governance, auditability, and trust

Enterprise AI must be safe. MCP ensures every action an agent takes is governed and traceable. Key safeguards include:

Full observability for each tool invocation

Transparent inputs and output schemas for every action

Granular, role-based permissions that explicitly control agent tool access

This governance-first design aligns perfectly with RAM’s focus on trustworthy AI and evidence-based reasoning.

Scalable, modular architecture through MCP tool servers

MCP tool servers allow organizations to wrap internal logic, systems, or processes in reusable, discoverable interfaces. These servers can expose capabilities such as:

Data lookups

Business process triggers

Internal system updates

Metadata and knowledge services

Custom decision rules or validation steps

Because tool servers are modular and standardized, they can be reused across many agents and teams inside RAM, reducing integration costs and accelerating development.

Performance and efficiency Gains

MCP allows retrieval agents to avoid overloading prompts with unnecessary context. Instead of embedding explanations of how to call an API, agents simply invoke a tool with the required structured parameters.

This leads to:

Lower model token usage

Faster task execution

Predictable output formats

More deterministic behavior

Reduced risk of hallucinations

MCP improves both speed and reliability without increasing model size.

Enterprise-ready deployment options

SAS Retrieval Agent Manager supports flexible deployment in cloud, hybrid, or fully air-gapped environments. MCP tools follow the same model.

Organizations can deploy MCP tool servers:

As containers from private registries

As code-based lightweight services

Behind secure firewalls

With isolated secrets and environment variables

Without reliance on external networks

This allows enterprises to operationalize RAG and agentic workflows even under strict regulatory or security constraints.

RAM powered by MCP: How it works

SAS Retrieval Agent Manager currently supports two primary categories of MCP tools enabling seamless integration with enterprise systems

Code-based MCP servers

These are ideal for small utilities, fast iteration, or department-specific automations. They provide:

Simple configuration with environment variables

Clearly defined input/output schemas

Fast deployment and testing

Easy adaptation for evolving requirements

Low overhead for developers

Containerized MCP servers

Typically used for integrations with core internal systems and are perfect for high-scale or mission-critical integrations. Benefits include:

Strong isolation and security controls

Reuse across multiple teams and workflows

Ability to package complex dependencies or language requirements

Consistent runtime environments

Support for enterprise container registries

The full workflow of how MCP Tools execute inside SAS Retrieval Agent Manager

Tool servers are deployed inside of RAM.

RAM ingests each tool’s JSON schema.

Agents analyze tasks and select the appropriate tools.

Tool calls are executed with structured inputs.

Responses, logs, and errors are captured.

The agent synthesizes the final grounded output.

This creates a unified flow where reasoning, retrieval, and action reinforce each other.

Use case for MCP in SAS Retrieval Agent Manager

Why MCP + SAS Retrieval Agent Manager is transformational

By integrating MCP capabilities, RAM evolves into a powerful agentic AI solution that is:

Actionable: Agents can complete tasks, interact with systems, and generate operational outcomes.

Governed: Every action is visible, logged, and governed by schema-defined protocols.

Modular: Tool servers encapsulate logic into reusable, shareable integrations.

Performant: Structured tool calls reduce latency and improve predictability.

Deployment-Ready: The architecture works seamlessly across cloud, hybrid, or offline setups.

This combination enables organizations to shift from informational AI to operational AI turning insights into execution.

Conclusion

Integrating MCP into SAS Retrieval Agent Manager enables the next generation of enterprise AI, with agents that can retrieve, reason, and act. By combining structured tool execution, reliable governance, and flexible deployment, organizations gain a scalable and trustworthy foundation for operational AI. RAM’s MCP enabled retrieval agents go beyond simple workflows to automate real-world business processes, interact seamlessly with enterprise systems, and deliver consistent, explainable results that drive tangible business outcomes.