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AI agent observability: Here’s what you need to know

AI agent vs RAG: how the two differ and where they overlap

AI agent vs MCP: how they differ and overlap

Jon Gitlin

Senior Content Marketing Manager

@Merge

As you look to leverage AI, you’ll need to understand the nuanced relationship between AI agents and the Model Context Protocol (MCP).

We’ll help by breaking down each and reviewing how they compare.

What is an AI agent?

An AI agent is a software-based system that performs tasks on behalf of users or employees autonomously. It operates based on programmed logic and can improve over time through additional data, user feedback, and learned behavior.

Its primary goal is to make employees or users more productive, whether that’s by saving them time or helping them uncover timely and actionable insights.

For example, Siit, which offers a modern IT service management platform, provides an AI agent that can perform IT tasks for employees at large.

The AI agent can, for instance, use SCIM directory and HRIS integrations to provision incoming employees with access to applications on behalf of hiring managers.

How Siit's AI agent can auto-provision incoming employees with apps

This example is just the tip of the iceberg. AI agents can manage and resolve repetitive customer issues, answer employees’ questions, create comprehensive sales proposals for go-to-market leaders, and more.

https://www.merge.dev/blog/ai-agent-integrations?blog-related=image

What is MCP?

MCP is a standardized protocol from Anthropic that lets AI agents access and interact with 3rd-party applications via tools.

A visual of how the Model Context Protocol works

Through the protocol, an MCP server exposes tools—or prefined functions from a 3rd-party application—that correspond to API endpoints. For example, a “List projects” tool from a project management platform can be associated with that platform's <code class="blog_inline-code">GET /projects</code> endpoint.

An MCP client, which is often an AI agent, decides which tools to invoke from an MCP server based on the users’ inputs and the tools that are made available.

Since the protocol has gained significant traction, countless MCP servers have been made available.

For example, Merge offers an MCP server that lets AI agents access tools from hundreds of 3rd-party systems. And individual SaaS providers also offer their own MCP servers. GitHub, for instance, offers one that lets AI agents and assistants get information from and add information to specific repos and issues.

Based on this context on MCPs and AI agents, we’ll break down their direct relationship next.

https://www.merge.dev/blog/rag-vs-mcp?blog-related=image

MCP vs AI agents

MCP is simply a protocol that allows AI agents to access and interact with data from 3rd-party applications more easily. AI agents, on the other hand, are software-based systems that can perform countless tasks across datasets, applications, and teams.

MCP enhances AI agents’ capabilities by allowing them to invoke tools in real-time based on users’ prompts. But AI agents don’t directly impact MCP servers. The servers can only be developed and modified over time by the MCP providers.

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Jon Gitlin

Senior Content Marketing Manager

@Merge

Jon Gitlin is the Managing Editor of Merge's blog. He has several years of experience in the integration and automation space; before Merge, he worked at Workato, an integration platform as a service (iPaaS) solution, where he also managed the company's blog. In his free time he loves to watch soccer matches, go on long runs in parks, and explore local restaurants.