Architecting Multi-Agent Systems with Ray Serve & MCP

8/11/2025

The Ultimate Guide to Architecting Multi-Agent Systems with Ray Serve & MCP

Alright, let's talk about something that's REALLY starting to change the game in AI: multi-agent systems. If you've been in the AI space for a while, you know that we've gone from single, monolithic models to something way more interesting. We're now building ecosystems of specialized AI agents that can work together to tackle incredibly complex problems. It’s like going from a solo artist to a full-blown orchestra.

But here's the thing: making that orchestra play in harmony is the hard part. How do you get all these different agents to talk to each other, share what they know, & not step on each other's toes? This is where the architecture of your system becomes CRITICAL.

In this guide, we're going to get into the nitty-gritty of how to build robust, scalable multi-agent systems. Specifically, we'll be looking at a pretty powerful combination: Ray Serve for distributed computing & the Model Context Protocol (MCP) for communication. It's a setup that's designed for the real world, where you need things to be fast, reliable, & flexible.

So, What Exactly Are Multi-Agent Systems?

First off, let's get on the same page. A Multi-Agent System (or MAS) is a setup where you have multiple AI agents working together in a shared environment. Think of it like a team of experts. You might have one agent that's a brilliant researcher, another that's a data analysis guru, & a third that's a master of writing code. None of them can do everything on their own, but together, they can achieve some pretty amazing things.

These systems are perfect for problems that are too big or too complex for a single agent to handle. We're talking about things like:

Supply Chain Management: Agents could manage inventory, predict demand, & coordinate deliveries across a global network.
Healthcare: Imagine agents that monitor patient data, assist with diagnoses, & schedule resources in a hospital, all in real-time.
Autonomous Vehicles: Fleets of self-driving cars need to communicate with each other to optimize traffic flow & prevent accidents. This is a classic MAS problem.

The key characteristics of these systems are:

Autonomy: Each agent has its own goals & can make its own decisions.
Decentralization: There's no single point of control. The intelligence is distributed across the system.
Collaboration: Agents need to communicate & coordinate to achieve their collective goals. This is where things get tricky, & where tools like MCP come in.

The Challenge: Getting Agents to Work Together

So, you've got your team of expert agents. Now what? You can't just throw them in a room & expect magic to happen. You'll quickly run into some major roadblocks:

Scalability: What happens when you need to go from 10 agents to 1,000? How do you manage all that computation without everything grinding to a halt?
Communication: How do agents share information? Do they just shout into the void? How do you make sure they have the most up-to-date context?
State Management: Each agent has its own memory & understanding of the world. How do you keep that all in sync, especially when they're working on a shared task?

This is where our power-duo, Ray Serve & MCP, come into play. They provide the scaffolding you need to build a system that can handle these challenges.

Ray Serve: The Engine for Scalable AI

Let's start with the muscle of our operation: Ray Serve. Ray is an open-source framework that makes it ridiculously easy to scale Python applications. It's been a go-to for distributed computing for a while now, & it's PERFECT for multi-agent systems.

Here’s why Ray Serve is such a game-changer for this kind of work:

Effortless Scaling: Ray lets you take a simple Python function or class & turn it into a distributed service with a single decorator. Want to run your agent on multiple cores or even multiple machines? Ray handles all the scheduling & resource management for you.
Actor Model: Ray is built on the actor model. You can think of an actor as a stateful worker. In our case, each AI agent can be its own Ray actor. This gives each agent its own protected memory & allows them to run in parallel. It’s a natural fit for the autonomy of agents in a MAS.
Deployment Graphs: This is where it gets REALLY cool. Ray Serve lets you define your entire multi-agent system as a deployment graph. You can chain agents together, have them run in parallel, or even dynamically choose which agent to call next. This is all done in simple Python, no messy YAML files needed.

Architecting Your MAS with Ray Serve Deployment Graphs

Imagine you're building a system to generate a research report. You might have a few different agents:

Planner Agent: Takes the initial prompt & breaks it down into a series of research questions.
Researcher Agent: Takes a research question, scours the web for information, & returns a summary.
Writer Agent: Takes all the research summaries & compiles them into a coherent report.
Editor Agent: Reviews the report for style, grammar, & accuracy.

With Ray Serve, you can set this up as a deployment graph. Each agent would be its own

serve.deployment

Here’s a simplified look at how that might work:

The
1Planner Agent
is the entry point. It receives the user's request.
It then calls the
1Researcher Agent
multiple times in parallel, one for each research question it generated. Ray Serve handles fanning out these requests.
Once all the
1Researcher Agent
tasks are done, the results are passed to the
1Writer Agent
.
Finally, the output from the
1Writer Agent
goes to the
1Editor Agent
for a final polish.

Each of these deployments can be scaled independently. If your research tasks are really intensive, you can spin up more replicas of the

Researcher Agent

without touching the other agents. That's the power of this architecture. You get fine-grained control over your resources.

This is also where a tool like Arsturn can be incredibly valuable. Imagine your multi-agent system is designed to handle complex customer support queries. You could have a front-facing chatbot, built with Arsturn, that acts as the initial point of contact. This chatbot can handle the simple questions instantly. But for more complex issues, it could trigger a multi-agent workflow in the background, powered by Ray Serve. For instance, it could pass the query to a "triage agent" that then routes it to a "technical support agent" or a "billing agent" within your Ray Serve architecture. Arsturn helps businesses create these custom AI chatbots that provide instant customer support, answer questions, & engage with website visitors 24/7, making it a perfect front-end for a powerful back-end system.

MCP: The Universal Translator for Your Agents

So, Ray Serve gives us the power to run & scale our agents. But how do they talk to each other in a smart, efficient way? This is where the Model Context Protocol (MCP) comes in.

MCP is an open standard designed to solve the context-sharing problem in multi-agent systems. Think of it as a universal language for AI agents. It defines a standardized way for them to:

Share contextual information: What's the latest update on the task? What did the user just say?
Access memory: Agents can have a shared memory or their own, & MCP provides a structured way to access it.
Use tools: If one agent has a special tool (like an API for a specific database), it can make that tool available to other agents through MCP.
Coordinate on a plan: MCP can help keep all the agents aligned on the overall goal.

How MCP Works in Practice

At its core, MCP is a standardized architecture, often with a host-server-client communication system. It standardizes how agents access, update, & track context. This could be anything from conversation history to the current state of a task.

Let's go back to our research report example. Without MCP, the

Writer Agent

would just get a jumble of text from the

Researcher Agents

. It wouldn't know which information is most important, where it came from, or if there are any contradictions.

With MCP, the context would be much richer. Each piece of information from the

Researcher Agent

could be sent in a

ContextBlock

that includes:

The content itself.
A timestamp.
The source of the information.
A relevance score.
Metadata tags (e.g., "statistic," "expert quote," "background info").

Now, the

Writer Agent

can be MUCH smarter. It can prioritize information with higher relevance scores, make sure to cite its sources correctly, & even flag contradictions between different sources.

MCP also helps with what's called "context versioning." In a dynamic system, information is constantly changing. MCP ensures that when an agent requests information, it gets the absolute latest version, preventing it from acting on stale data. This is HUGE for avoiding errors & making your system more reliable.

Putting It All Together: The Ray Serve + MCP Architecture

So, what does the final architecture look like when we combine Ray Serve & MCP? Here’s a high-level view:

Ray Cluster: At the base, you have your Ray cluster. This could be running on your laptop for development, or on a massive cloud deployment for production.
Ray Serve Deployments: On top of the cluster, you have your Ray Serve deployments. Each agent in your system is its own deployment, defined as a Python class. This allows you to scale each agent independently.
Deployment Graph: You use Ray Serve's deployment graph API to define the workflow between your agents. This is where you set up the logic for how agents call each other, whether it's in a chain, in parallel, or based on certain conditions.
MCP for Communication: Now, instead of agents just passing raw data to each other, they communicate using MCP. When one agent calls another using its
1DeploymentHandle
, the payload is an MCP-formatted message. This ensures that all communication is rich with context.
MCP Server/Host: You might have a central MCP host or server within your Ray cluster. This could be another Ray actor that's responsible for managing shared memory or resolving conflicts between agents. For instance, if two agents propose conflicting actions, the MCP host could use a timestamp-based rule to decide which one to proceed with.

This architecture gives you the best of both worlds:

Scalability & Performance from Ray Serve.
Coordinated & Context-Aware Communication from MCP.

And because you're building on these powerful, flexible foundations, you can create some seriously sophisticated systems. For businesses looking to implement this kind of AI-driven automation, the applications are endless. This is where a platform like Arsturn can play a crucial role. While Ray Serve & MCP provide the deep technical backbone, Arsturn helps businesses build no-code AI chatbots trained on their own data to boost conversions & provide personalized customer experiences. Imagine connecting an Arsturn-powered chatbot to a multi-agent backend. The chatbot handles the initial customer interaction, gathering information & understanding their needs. It then passes this structured data to your Ray Serve & MCP-powered system to execute complex tasks, like generating a personalized quote, troubleshooting a technical issue, or even processing an order across multiple internal systems. This creates a seamless, intelligent, & highly scalable solution.

A Look at a Real-World Example: Ant Group's Ragent

This isn't just theoretical. Ant Group, a major tech company, built a framework called Ragent for their AI agents, & it's built on Ray. Their setup uses:

Ray Core for the distributed workflows.
Ray Serve for deploying the agents & handling real-time computing.
Integrations with popular agent frameworks like LangChain & AutoGen.

They specifically point out how important it is in a multi-agent scenario to have dedicated memory formats for different groups of agents, which is something Ray's actor model handles beautifully. This is a powerful validation of this architectural pattern from a company operating at a massive scale.

Let's Wrap This Up

Honestly, building multi-agent systems is one of the most exciting frontiers in AI right now. But to do it right, you need to think like a systems architect from day one. You can't just hack together a bunch of scripts & hope for the best.

By using Ray Serve as your distributed computing engine, you get a solution that's not only powerful but also surprisingly easy to work with. It's Python-native, which means you don't have to be a distributed systems PhD to get started.

And when you layer the Model Context Protocol on top of that, you solve one of the biggest headaches in multi-agent systems: communication & context. You give your agents a shared language, which allows for much more sophisticated collaboration.

The combination of Ray Serve's scalability & MCP's structured communication is a potent one. It gives you a clear path to building multi-agent systems that are not just clever demos, but production-ready applications that can solve real-world problems.

So, if you're diving into the world of multi-agent systems, I'd seriously recommend giving this architecture a look. It might just be the foundation you need to build the next generation of intelligent applications.

Let me know what you think! Have you experimented with Ray Serve or MCP? I'd love to hear about your experiences.