News from the AI & ML world
DeeperML
@www.marktechpost.com
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The development of AI agents capable of performing human tasks on computers is gaining momentum, with a particular focus on multi-agent communication systems. Several research labs and companies are actively exploring this area, aiming to build agents that can effectively coordinate and collaborate. A key aspect of this research involves establishing robust communication protocols that enable seamless interaction between multiple AI agents. Recent articles highlight the progress being made in constructing code using these multi-agent communication systems, paving the way for more sophisticated and autonomous AI applications.
Mistral AI recently released its Agents API, providing public access through La Plateforme for developers to create autonomous agents. This API allows agents to plan tasks, utilize external tools, and maintain long-term context. The interface comes equipped with connectors for Python execution, web search, Flux 1.1 image generation, and a document library. The Agents API supports the mistral-medium-latest and mistral-large-latest models, allowing agents to delegate subtasks to each other via the Model Context Protocol, creating coordinated workflows across multiple services.
A tutorial was recently released which provides a coding guide to building scalable multi-agent communication systems using the Agent Communication Protocol (ACP). This guide implements ACP by building a flexible messaging system in Python, leveraging Google's Gemini API for natural language processing. The tutorial details the installation and configuration of the google-generativeai library, introduces core abstractions, message types, performatives, and the ACPMessage data class for standardizing inter-agent communication. Through ACPAgent and ACPMessageBroker classes, the guide demonstrates how to create, send, route, and process structured messages among multiple autonomous agents, also showing how to implement querying, requesting actions, broadcasting information, maintaining conversation threads, acknowledgments, and error handling.
References :
Mistral AI recently released its Agents API, providing public access through La Plateforme for developers to create autonomous agents. This API allows agents to plan tasks, utilize external tools, and maintain long-term context. The interface comes equipped with connectors for Python execution, web search, Flux 1.1 image generation, and a document library. The Agents API supports the mistral-medium-latest and mistral-large-latest models, allowing agents to delegate subtasks to each other via the Model Context Protocol, creating coordinated workflows across multiple services.
A tutorial was recently released which provides a coding guide to building scalable multi-agent communication systems using the Agent Communication Protocol (ACP). This guide implements ACP by building a flexible messaging system in Python, leveraging Google's Gemini API for natural language processing. The tutorial details the installation and configuration of the google-generativeai library, introduces core abstractions, message types, performatives, and the ACPMessage data class for standardizing inter-agent communication. Through ACPAgent and ACPMessageBroker classes, the guide demonstrates how to create, send, route, and process structured messages among multiple autonomous agents, also showing how to implement querying, requesting actions, broadcasting information, maintaining conversation threads, acknowledgments, and error handling.
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References :
- Communications of the ACM: The Rise of the AI-Enabled Agentic Internet
- www.marktechpost.com: A Coding Guide to Building a Scalable Multi-Agent Communication Systems Using Agent Communication Protocol (ACP)
- Stack Overflow Blog: Article discussing integration of AI agents.
- Orases: The Roadmap to Successful AI Agent Implementation
- Analytics Vidhya: 8 Things to Keep in Mind while Building AI Agents
- orases.com: The Executive’s Guide To Organizational AI Agent Integration & Implementation
- www.madrona.com: Why CISOs Need Agentic Security — And Why We Invested in Impart
- hackernoon.com: AI Agents for Beginners: Building Your First AI Agent
- Salesforce: The New Collaborative Workforce: Humans and Digital Agents
- learn.aisingapore.org: Not Everything Needs Automation: 5 Practical AI Agents That Deliver Enterprise Value
- Orases: The Executive’s Guide To Organizational AI Agent Integration & Implementation
- thenewstack.io: Deploying A Secure Enterprise Agentic AI: MCP + Agent2Agent
- AI Accelerator Institute: What exactly is an AI agent – and how do you build one?
- AI News | VentureBeat: Agent-based computing is outgrowing the web as we know it
- Analytics Vidhya: Build a Conversational AI Agent with Rasa
- www.marktechpost.com: A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and Gemini
- futurumgroup.com: The post appeared first on . Analysts Mitch Ashley, Nick Patience, and Keith Kirkpatrick at Futurum share their insights on Microsoft Build 2025, revealing Microsoft's strategic pivot to agentic AI and its profound implications for the future of software development.
- www.microsoft.com: With Microsoft Dynamics 365, organizations are embracing a modern, AI-first approach that redefines productivity and customer engagement.
Classification:
- HashTags: #AIAgents #AgenticAI #MultimodalModels
- Company: AI Labs
- Target: Enterprises
- Product: AI Agents
- Feature: Agent Communication
- Type: AI
- Severity: Informative