Model Relevance Protocol
MRP Workforce
MRP agents can get complex work done with just a few simple low-level tools, a long-context model and optional docs.
Give an MRP agent a natural language task, and it'll be able to figure out how to get the job done with just a few very simple tools.
HubSpot MRP
Generate a few records to pre-populate my CRM like a live demo environment. I want Contacts, Deals, Companies, Notes, Logged Emails and Calls. Use Dummy numbers.
Google Docs MRP
Generate a Google Doc titled “AI Agent Use-Cases and Building Techniques”. Break down each AI Agent use-case and building technique in-depth.
Webflow MRP
Generate a new blog post about AI Agents and add to the "Blog Post" collection. Return the full content for me to review and approve. Once I've approved, publish it and share the link to the live page.
Notion MRP
Generate a task management system with an inline-database for projects, another for tasks. Use a relation field to link the tasks to the projects. Add 3 x example projects.
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Give an MRP agent a natural language task, and it'll be able to figure out how to get the job done with just a few very simple tools.
Model Relevance Protocol
What does an MRP look like at an agent level? And why might you want to use them as an extension to, or instead of MCPs? Let's dive in.
A multi-agent system (MAS) is a group of intelligent agents that interact and work together towards achieving common goals. Agents in a MAS combine their individual capabilities and knowledge to collaborate on solving problems that are beyond the scope of any individual agent.
Some key characteristics of a multi-agent system are:
- Composed of multiple autonomous agents
- Agents have particular capabilities and expertise
- Agents communicate and coordinate with other agents
- Decentralized control and decentralized data
- Emergent global behavior from local agent interactions
Some of the key benefits of developing a multi-agent system are:
- Modularity - Agents can be added or removed as needed
- Robustness - System continues operation if some agents fail
- Scalability - Easy to expand system by adding agents
- Flexibility - Agents can be customized for different roles
- Faster problem solving - Parallelization of work across agents
- Broader intelligence - Combination of multiple skill sets
Relevance AI provides a no-code platform for easily building intelligent multi-agent systems. Here are the key steps:
- Identify the Goals and Scope - Define the objectives and outcomes you want to achieve an map out different tasks and workflows required.
- Create and Train Agents - Use pre-built agents or create custom agents. Train agents on specific domains and give them specialized skills
- Define Agent Interactions - Model how agents will communicate and work together
- Deploy and Iterate - Deploy your multi-agent system. Monitor performance and add/adjust agents as needed
Relevance AI provides a no-code platform for easily building intelligent multi-agent systems. Here are the key steps:
- Identify the Goals and Scope - Define the objectives and outcomes you want to achieve an map out different tasks and workflows required.
- Create and Train Agents - Use pre-built agents or create custom agents. Train agents on specific domains and give them specialized skills
- Define Agent Interactions - Model how agents will communicate and work together
- Deploy and Iterate - Deploy your multi-agent system. Monitor performance and add/adjust agents as needed
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The Model Context Protocol standardise how tools are shared and accessed. You can think of an MCP as a bundle of tools packaged together in a way that lets AI assistants use them through a shared interface. This makes it possible for agents built on one platform, to use tools developed on another.
What is an MRP?
The Model Relevance Protocol takes things a step further. Instead of packaging multiple tools into one server, an MRP is about having one agent with a couple of low-level tools that it can use to complete complex workflows. Like a generic API or code step tool that the agent can figure out how to use, instead of 30+ tools that are hyper-specific.
MRPs are an extension to MCP
MRPs allow your AI assistants to complete work that isn't possible with just the tools packaged into an MCP bundle alone. Instead of writing out every step or copying detailed API calls, you can just connect your agent to the relevant MRP agent and ask for what you want in plain language. The specialist MRP agent handles the rest.
MCP - Many good tools
Your AI assistant can use any tool that an integration provider has packaged up for you. Each tool will be very reliable, and do its job well. However, it will only be able to complete work that it has tools for. If the API changes, you have to update any tool that is affected, and any tool that require strongly formatted inputs.
MRP - 1+ simple tool
Your AI assistant can use any tool that an integration provider has packaged up for you. Each tool will be very reliable, and do its job well. However, it will only be able to complete work that it has tools for. If the API changes, you have to update any tool that is affected, and any tool that require strongly formatted inputs.
MRP agents can get complex work done with simple tools
With just a single generic API call tool, and some API documentation, this Notion MRP agent was able to create a full task management system with relational databases, and a few example projects and tasks.
Before, it would have taken hours to build out each individual tool needed to create a page, create the databases each with custom properties, create projects and tasks and add them to the database and link them together and more. You'd have to do that all over again if you wanted a different kind of Notion management system too.
Now, all you need to do to get started is clone the agent, connect your Notion account, and give it a task.
MRP workforces, are mindblowing
This makes things more flexible:
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