Use A2A to connect agents across different frameworks and teams

2026-02-11 · Source: DeepLearningAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

A new course introduces the Agent-to-Agent (A2A) protocol, an open standard for agent discovery and communication, developed in partnership with Google Cloud and IBM Research. A2A addresses the complexity of integrating agents built with diverse frameworks and by different teams, establishing a client-server model where A2A clients send requests to A2A servers hosting AI agents. Announced by Google Cloud in April 2025 and donated to the Linux Foundation in June 2025, A2A is open-source and community-governed, having merged with IBM's Agent Communication Protocol (ACP). The course teaches building A2A-compliant agents, including an insurance assistant, an EL research agent using Google Agent Development Kit, and a doctor matching agent with Langraph. It also covers chaining agents in sequential workflows and dynamic orchestration using IBM Research's BAI framework, with deployment on the open-source Agent Stack infrastructure.

Key takeaway

For AI Engineers building multi-agent systems, adopting the A2A protocol simplifies integration challenges across different frameworks and teams. You should explore A2A to standardize agent communication, enabling easier reuse of existing agents and modular updates without system-wide refactoring. Consider deploying your A2A agents on Agent Stack for streamlined management and sharing within your organization.

Key insights

A2A standardizes agent communication, enabling collaboration and reuse across diverse frameworks and teams.

Principles

• Standardization simplifies agent integration.
• Open-source governance fosters adoption.
• Client-server model supports modular agent design.

Method

Build A2A-compliant agents by encapsulating them in a server, interacting via an A2A client, then chain them in sequential or dynamically orchestrated workflows, and deploy on Agent Stack.

In practice

• Reuse existing agents across projects.
• Update individual agents without refactoring.
• Delegate tasks to specialized remote agents.

Topics

• A2A Protocol
• Agent Communication
• Multi-Agent Systems
• Open-Source Protocols
• Agent Deployment

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• What Is the A2A Protocol? — A2A standardizes AI agent collaboration, enabling secure, interoperable communication and task management across platforms.
• Retrofit, don’t rebuild: Agentic overlays for transforming legacy enterprise services — Agentic overlays enable legacy REST services to participate in A2A communication without costly refactoring.
• Agentic AI’s challenge is getting agents to act like a team, not a crowd — Effective multi-agent AI systems require dedicated coordination infrastructure to ensure agents work as a team, not in isolation.
• The Agent Client Protocol Overview — ACP standardizes AI agent-client communication, enabling seamless integration across diverse coding assistants and IDEs.
• Article: Architecting Agentic MLOps: A Layered Protocol Strategy with A2A and MCP — Layering A2A for communication and MCP for capabilities enables robust, extensible multi-agent MLOps workflows.
• AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility — Agentified Agent Assessment (AAA) uses judge agents and standardized protocols (A2A, MCP) for open, reproducible, and interoperable agent evaluation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by DeepLearningAI.