Build Cross-Language Multi-Agent Team with Google’s Agent Development Kit and A2A

2026-06-22 · Source: Google Developers Blog - AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Google's Agent Development Kit (ADK) and the open Agent2Agent (A2A) protocol enable the creation of robust, cross-language multi-agent systems, as demonstrated by a Contract Compliance Multi-Agent Pipeline released on June 22, 2026. This pipeline features a Python agent leveraging Gemini for contract term extraction and a Go agent for deterministic compliance validation against corporate policy. The A2A protocol facilitates agent discovery via Agent Cards and communication through JSON-RPC 2.0, allowing agents built in different languages to interoperate without code rewriting. The ADK's "RemoteA2aAgent" abstraction seamlessly integrates remote A2A services into local sub-agents, orchestrating specialized agents to overcome limitations of monolithic AI systems like context degradation, large blast radius, and poor testability. An open-source application provides a three-panel operations cockpit with network fault simulation, showcasing resilience and isolated auditing capabilities.

Key takeaway

For AI Engineers building robust, production-grade multi-agent systems, adopting Google's ADK and the A2A protocol is crucial. This approach allows you to integrate agents developed in different languages, like Python for LLM tasks and Go for deterministic validation, into a cohesive pipeline. You can enhance system resilience by implementing fail-safe mechanisms, such as routing to manual review upon agent failure. Consider decomposing monolithic agents into specialized microservices to improve testability and reduce blast radius.

Key insights

Cross-language multi-agent systems using ADK and A2A enhance resilience and modularity for production AI.

Principles

• Decompose monolithic AI agents into specialized microservices.
• Use open protocols for cross-language agent interoperability.
• Implement fail-safe patterns for production system resilience.

Method

Orchestrate specialized agents sequentially using ADK's "RemoteA2aAgent" to wrap A2A-compliant services, communicating via a shared "ToolContext.state" dictionary.

In practice

• Deploy Python for cognitive tasks, Go for deterministic validation.
• Use Agent Cards for agent capability discovery.
• Route failures to human review for critical workflows.

Topics

• Multi-Agent Systems
• Agent2Agent Protocol (A2A)
• Google ADK
• Cross-Language Collaboration
• Contract Compliance
• Microservices Architecture

Code references

• GoogleCloudPlatform/generative-ai

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Google Developers Blog - AI.