Introduction

In earlier articles, we looked at what MCP is and how it connects agents to systems. But connection is only the first step. The real impact comes when agents start using MCP to run workflows: pulling data, reasoning over it, and acting across multiple systems.

This post looks at four common patterns:

• Enterprise use cases like banking, spend management, and operations
• Retrieval-Augmented Generation (RAG) for grounded answers
• Event-driven reactions to real-time signals
• Multi-step orchestration across systems

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Agents in the Enterprise: Real-World Use Cases

Workflows are everywhere in enterprises. MCP lets agents step directly into them.

• Banking: An agent queries the ledger, flags unusual transactions, and drafts a compliance report.
• IT operations: An agent monitors logs, spots anomalies, and can trigger a runbook or open an incident ticket.

Why MCP helps: Instead of building separate connectors, agents use the same protocol for each system.

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MCP for RAG: Querying and Reasoning Over Data

RAG lets agents fetch live information before answering.

Flow with MCP:

1. The agent queries an MCP server (e.g., Postgres).
2. The server retrieves the data.
3. The agent uses the LLM to generate a grounded response.

Example: “Summarize last quarter’s financial performance.” The agent retrieves statements via MCP and produces a report based on real numbers.

Why MCP helps: It standardizes access, so agents don’t need custom code for each source.

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Event-Driven MCP: Agents Reacting to Real-Time Signals

Many workflows are triggered by events. MCP servers can push these directly to agents.

• A payment system emits a high-value transaction. The agent checks compliance.
• A monitoring tool reports errors. The agent fetches logs and suggests a cause.

Why MCP helps: Agents no longer need to poll systems. Events arrive as they happen, making responses faster and cleaner.

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Workflow Orchestration with MCP: Multi-Step Processes

Enterprise processes often span multiple systems. MCP lets agents chain steps without special logic for each one.

Example: Loan approval

1. Query credit history from a database.
2. Fetch documents from a file system.
3. Run underwriting checks.
4. Prepare a decision summary.

Why MCP helps: Because every system looks the same through MCP, the workflow is easier to design and maintain.

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Visualizing MCP Workflows

RAG Workflow

User → Agent (MCP Client) → MCP Server → Database/Docs → LLM Answer

Event-Driven Workflow

System Event → MCP Server → Agent (MCP Client) → LLM → Action

Multi-Step Orchestration

Agent (MCP Client)
   ↓ Credit History (DB)
   ↓ Documents (File System)
   ↓ Underwriting (Risk System)
   ↓ Decision Summary

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Wrapping Up

MCP takes agents beyond simple Q&A. It enables them to:

• Step into enterprise workflows
• Ground answers with real data
• React to events in real time
• Orchestrate multi-step processes

Takeaway: MCP moves agents from answering to acting—and from acting to coordinating.