👾 All About Building Multi-Agent Systems: Huge Updates!

Welcome to Edition #2 of Agents Made Simple

Some industry giants just released new open-source frameworks for building multi-agent AI systems. We’ll be diving into these and revealing exactly how they work. MCP is a new buzzword in the AI agent space. After this read, you’ll have a solid understanding of it.

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This week’s topics:

• Google, OpenAI, ElevenLabs, Amazon, Writer AI news

• Multi-agent systems are the future, MCP explained

• How to build AI agents with no code

AI Agent News Roundup

💥 Breakthroughs

📈 Investments¹

💰 Meta plans a $1 billion data center in Wisconsin.

🦾 Nvidia released Nemotron-Ultra, a 253B parameter open-source reasoning model outperforming DeepSeek R1 and Llama 4 Behemoth.

🅶 Google rolled out deep research on Gemini 2.5 Pro. Announcements from the Google Next 2025 conference: Google’s most powerful 7th-gen AI chip Ironwood coming later this year, Cloud WAN private network for business, quantum chip Willow reduces errors, and cost-efficient Gemini 2.5 Flash model.

🇪🇺 The EU’s “AI Continent Action Plan”: €200B to build 13 AI factories and aiming to triple data center capacity across Europe within seven years.

Why Multi-Agent Architectures Are the Future and the Role of MCP

AI agents are evolving beyond solo performers. Multi-agent systems now represent the next frontier in artificial intelligence. These systems, where multiple AI agents work together to solve complex problems, are gaining traction across industries.

What Is MCP and Why Should You Care?

Model Context Protocol (MCP) developed by Anthropic serves as the backbone of modern multi-agent systems. MCP is an open standard that connects AI agents to diverse data sources and tools without compromising security. Think of it as a universal connector for AI systems.

MCP works by creating standardized interfaces between agents and data sources. This protocol enables agents to access information from databases, APIs, or other business systems using a common language. For developers, this means less custom code and more reliable connections.

An agent using MCP can tap into enterprise data in real-time without maintaining separate copies. This reduces data duplication while ensuring agents always work with the most current information.

From Single Agents to Orchestrated Teams

Single-agent systems face clear limitations. They struggle with complex tasks requiring diverse expertise and often become bottlenecks during heavy workloads.

Multi-agent architectures solve these problems through specialization and collaboration. Each agent handles specific tasks it performs best. One agent might retrieve data while another analyzes it, and a third presents results to users.

Google Cloud recently unveiled their Agent Development Kit (ADK) which exemplifies this approach. ADK allows developers to build agents that collaborate through deterministic guardrails and orchestration controls. The companion Agent Engine provides a fully managed runtime to deploy these agents with enterprise-grade controls.

The benefits extend beyond mere efficiency. Multi-agent systems offer:

• Greater resilience as tasks distribute across multiple systems

• Enhanced scalability through parallel processing

• Improved accuracy as specialized agents handle their expert domains

• Greater adaptability to changing requirements

Real-World Multi-Agent Architectures in Action

Renault Group demonstrates the practical value of multi-agent systems. They built an agent using ADK that optimizes electric vehicle charger placement. This system analyzes geographical, zoning, and traffic data to prioritize infrastructure investments, reducing strain on human analysts.

Nippon Television implemented Agent Engine as the foundation for their video analysis AI. This implementation saved them an estimated month of development time while maintaining seamless connections with other Google Cloud products.

Revionics created a multi-agent system to help retailers set prices based on business logic. Their solution combines specialized agents for data retrieval with constraint application tools, automating entire pricing workflows through efficient agent collaboration.

Each case shows how multi-agent systems tackle problems too complex for single agents alone.

Common Questions About Multi-Agent Systems

How do multi-agent systems communicate with each other? The new Agent2Agent (A2A) protocol enables agents built on different frameworks and by different vendors to communicate seamlessly.

What security concerns exist with multi-agent systems? Multi-agent systems face threats like prompt injection attacks and unauthorized data access. Proper implementation includes agent output controls, permission management, data protection through secure perimeters, and comprehensive monitoring of agent behavior.

Which industries benefit most from multi-agent architectures? Financial services, healthcare, manufacturing, and retail show the strongest adoption. These industries deal with complex data ecosystems where specialized agents can drive significant efficiency gains.

What's next for multi-agent systems? Computer-use capabilities and code execution are coming soon to advanced agent platforms. Dedicated simulation environments will allow testing with diverse user personas and realistic tools before production deployment.

Moving Forward with Multi-Agent Systems

Understanding multi-agent architectures and MCP now provides a competitive edge in AI implementation. These systems represent the practical evolution of AI from theoretical models to working solutions for complex business problems.

The technology continues to mature rapidly. Google's partnerships with over 50 industry leaders show broad commitment to open standards in multi-agent systems. This collaboration promises more interoperable and powerful agent ecosystems in the near future.

What multi-agent use cases could transform your business? The framework exists today to start building systems that were impossible just months ago.

Have questions about implementing multi-agent systems in your organization? Reply to this newsletter to continue the conversation.

Tool Spotlight

👾 Build AI Agents with RelevanceAI

How to Build a Simple Email Lead Qualification Agent in Relevance AI

Want to automatically qualify leads from email inquiries? Here's how to build an AI agent that does the heavy lifting for you:

Step 1: Create a New Agent

• Sign up or log in to Relevance AI

• Click New Agent and type what you want your agent to do.

• The AI is writing the instructions for your agent and recommends tools.

Example instructions:

Step 2: Connect Required Tools

Connect these essential tools to your agent:

• Gmail integration (for sending emails)

• LinkedIn data extraction tool

• Website scraping capability

• Google Search connector

• Email domain checker

Step 3: Configure the Workflow

Set up your workflow with these decision points:

• Email domain check → branching logic

• If work domain → data enrichment sequence

• If free domain → terminate with unqualified tag

Step 4: Test Your Agent

Send a test lead through your system using both scenarios:

• A personal email (should be marked unqualified)

• A work email (should trigger research and email composition)

That's it! Your agent now automatically qualifies leads, researches companies, and sends personalized meeting requests – all without manual intervention.

Made By Agents Updates

More Resources

Blog: AI-driven business automation and practical strategies for growth
AI Tool Collection: Discover and compare the perfect AI solutions
Consultancy: I help you solve your problem or discover AI potential
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See you next time!

Tobias from Made By Agents

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