Enterprises are slowly evolving from agent prototypes to full scale agent deployments. Most enterprise agents today rely solely on static Retrieval-Augmented Generation (RAG) data or historical training sets. They answer questions well in isolation, but they lack real-time awareness, business context, and access to the systems where actual work happens, like SAP, Salesforce, ServiceNow, or custom APIs. This siloed approach severely limits their business impact.
Understanding the concept of fully integrated AI agents
Picture an enterprise agent that doesn’t just know things, but does things. This agent could:
- Check real-time stock from SAP before responding to a customer inquiry
- Cross-reference current opportunity pipelines and customer lifetime value in Salesforce before drafting a renewal quote
- Trigger backend processes using MuleSoft APIs, updating status across systems automatically
- Close out a conversation when an issue is resolved — and logs a post-interaction summary into the CRM for compliance and tracking.
MuleSoft is the integration fabric that brings this vision to life.
This level of integration and orchestration is not just aspirational, but essential – without which AI agents will become yet another siloed tool with high expectations, yet low impact. We’ll explore how MuleSoft can transform AI Agents from siloed tools to integrated digital task force in your enterprise.
AI agents to enterprise application communications: The MAC Chain Connector
Picture an employee using his organization’s chat-agent to check for real-time inventory of laptop models and to subsequently place an order. In this case, the chat agent needs access to real-time data from inventory databases and order management applications in order to assist the employee. MuleSoft’s comprehensive integration platform along with MAC Chain Connector helps the agents to address this. The Tools | Use AI Service Operation in the Mule AI Chain Connector facilitates communication between the agent and the MuleSoft APIs. MuleSoft APIs can help to securely connect to diverse applications in an enterprise. Agents can call the relevant APIs whenever the prompt cannot be directly answered by the LLM.
A prerequisite for using Tools is to prepare a tools.config.json which includes all the necessary API information to execute the relevant APIs successfully.
The Einstein AI connector provides a similar functionality. In addition, it also provides access to Einstein Trust Layer that elevates the security of Generative AI through seamless data and privacy controls
Model Context Protocol: AI agents and enterprise applications
AI agents, powered by LLMs, access enterprise applications via APIs. They leverage the LLM’s reasoning to decide which APIs to invoke and orchestrate those calls based on context.
AI integration challenge
An enterprise will have hundreds of applications exposing data over diverse protocols. Each AI agent must integrate separately with these applications and understand their capabilities. With multiple agents, this integration challenge multiplies significantly.
How MCP solves this AI integration challenge
- Natural language context: MCP requires APIs to include rich, descriptive metadata that LLMs can understand. MCP-compatible APIs need to provide human-readable descriptions of what each function does, when to use it, and what the expected outcomes are.
- Unified access control: MCP servers act as intermediaries that handle authentication and authorization. Instead of agents needing to establish individual relationships with each API provider, agents authenticate once with the MCP server, which then manages access to all the underlying tools and services on behalf of the agent.
- Standardized discovery: MCP provides a consistent way for agents to discover what tools are available and how to use them, eliminating the need to parse different documentation formats or guess at API capabilities
Key components of MCP architecture
- Host: The application environment where user interactions originate (e.g. chatbots, dedicated AI assistants, etc.)
- Client: The component that orchestrates the interaction between the LLM and the available tools. MuleSoft provides support to act as an MCP client
- Server: The component that manages available tools and handles their execution. MuleSoft provides support to act as an MCP server
- Tools/services: The actual capabilities that perform specific functions (database queries, APIs, etc.)
MuleSoft Topic Center: Agentforce to enterprise application communication
Seventy-five percent of AI’s value comes from the front office, where most customer interactions happen. To deliver the best results, customer-facing AI agents created with Agentforce need a connected and secure network of applications where they can take action autonomously. However, with the average enterprise using over 900 applications (72% of which aren’t connected), Agentforce’s ability to access relevant data and complete tasks is limited.
Picture a customer using an AI agent to check the status of delayed order. The customer wants to know the delay is being addressed and that the delivery is still on its way. For Agentforce to provide help, it must access all the systems tied to the delivery process – potentially involving third-party vendors, inventory databases, and shipping platforms. If those systems aren’t connected or the AI agent can’t act on the information it finds, the customer experience will fall short.
Anypoint Platform is a comprehensive integration platform designed to connect applications, data, and devices across diverse environments. For Agentforce, the power of Anypoint Platform lies in its API-led connectivity. This approach enables Agentforce agents to seamlessly integrate with external and often hard-to-reach systems such as SAP, Oracle, and warehouse management systems (WMS).
MuleSoft Topic Center makes MuleSoft APIs available as agent actions and instructions that inform Agentforce how and when to use these APIs to execute actions across the enterprise. With Anypoint Code Builder you can define Topic within your existing or newly defined API spec to make it easier for Agentforce to parse and provide accurate and high-quality responsiveness.
MuleSoft Agentforce Connector: Enterprise applications to Agentforce communication
Agentforce is a powerful investment, but its true business value is realized only when it is integrated into the broader enterprise application ecosystem. Your enterprise applications (CRMs, ticketing systems, supply chains, and analytics platforms) should take advantage of the Agentforce agents to make decisions, complete tasks, and drive intelligent automation. The MuleSoft Agentforce Connector makes this vision a possibility. Here are its key features:
- Effortless onboarding: Instantly authenticate and connect to your Salesforce org. The connector eliminates complexity so you can quickly start leveraging the agents deployed in your environment without building new infrastructure
- Agent discovery made easy: Automatically discover all available and active agents in your Salesforce environment. This enables quick selection and usage, making it easier to operationalize Agentforce across different business processes
- Initiate and orchestrate agent sessions: Begin live sessions with agents from external applications – whether to resolve support cases, run backend operations, or drive decision automation. The connector ensures agents aren’t siloed tools but collaborators across applications
- Multi-prompt interaction for real workflows: Enable back-and-forth interactions between agents and enterprise systems. Unlike one-off queries, this supports sustained conversations where agents refine, verify, and improve outcomes in real time
- Context-rich, data-grounded prompts: Feed structured data from ERP, CRM, or any external system directly into agent prompts. This grounds the agent’s responses in real business context and allows them to act on behalf of users, not just respond
- Proactive session management: Control session lifecycles to optimize performance and cost. Close inactive or completed sessions to avoid unnecessary compute usage, reduce risk, and maintain secure integrations
AI agents for business workflows
MuleSoft operationalizes AI agents inside your business by embedding them into the workflows, data streams, and business processes that your teams already use. Here is a summary why MuleSoft is mission-critical for operationalizing AI agents.
| Challenge | MuleSoft solution |
| Siloed, static agent knowledge | Real-time, dynamic data access via APIs |
| Security and governance concerns | API-level access control and observability |
| Lack of context in prompts | Prompt grounding with live enterprise data |
| Disconnected from workflows | Event-driven triggers and action orchestration |
| High cost of scale | Reusable APIs and centralized integration governance |
This is not a future-vision. It’s the foundation for building AI-native businesses today. True business value comes from real-time collaboration with systems, services, and agents. MuleSoft supercharges agentic communication through its power of API-led connectivity and introducing support for emerging agentic protocols.
