Agent-to-Agent Protocol Support in Amazon Bedrock AgentCore Runtime

Introduction

Coordinating multiple AI agents to solve real-world tasks is becoming increasingly common, but getting them to communicate effectively, share context, and work together can be a complex task. To simplify multi-agent collaboration, Amazon Bedrock AgentCore Runtime now supports an agent-to-agent (A2A) protocol, enabling seamless and secure communication between agents across different frameworks and environments.

This update makes it easier to build interoperable multi-agent systems where specialized agents discover each other, delegate tasks, and coordinate workflows, all without reinventing communication layers.

Key Features of A2A Support in Amazon Bedrock AgentCore Runtime

• Standardized Agent Discovery
  Amazon Bedrock Agents use “agent cards” (JSON metadata) to advertise their identity, capabilities, and endpoints. Other agents can query these cards and dynamically discover what their peers can do.
• Protocol-Based Communication
  A2A utilizes JSON-RPC 2.0 over HTTP for message transmission, ensuring a consistent and structured communication pattern.
• Secure Authentication & Authorization
  Agent-to-agent interactions can be authenticated via OAuth 2.0 or AWS IAM (SigV4), giving agents strong identity and access control.
• Session Isolation
  Each agent session gets its own isolated runtime context, helping track conversations and enforce security boundaries.
• Framework Agnostic
  Agents built using different SDKs, such as Strands, OpenAI Agent SDK, LangGraph, or Claude Agent SDK, can communicate with each other because of the standardized A2A protocol.

Benefits of Using A2A Protocol on Amazon Bedrock AgentCore Runtime

• Interoperability Across Systems
  Amazon Bedrock AgentCore’s A2A support removes silos. Agents written in different frameworks or hosted on different environments can communicate with each other natively, without requiring custom bridges.
• Scalable Multi-Agent Coordination
  You can add or remove agents dynamically. Each agent is loosely coupled, meaning it can be developed, tested, and deployed independently.
• Resilient and Modular Architecture
  Since agents operate independently, failure in one doesn’t crash the whole system. Modular design improves resilience.
• Better Automation
  Complex workflows, such as incident response, monitoring, or business orchestration, can be delegated among agents. For example, a monitoring agent detects an issue and passes it to a remediation agent for action.

Use Case: Multi-Agent Incident Response

AWS demonstrates a powerful example: a monitoring and incident response system using three specialized agents.

1. Host Agent (Coordinator)
   Built using Google ADK, this agent dynamically discovers other agents, understands their capabilities, and routes tasks accordingly.
2. Monitoring Agent
   Using the Strands SDK, this agent continuously analyzes Amazon CloudWatch metrics, logs, and alarms to detect anomalies or errors across AWS accounts.
3. Operational Agent
   Built with the OpenAI Agents SDK, this agent researches remediation steps, such as querying web documentation or AWS best practices, and recommends fixes.

When a problem is detected (for example, an error in Amazon CloudWatch logs), the host agent delegates the task to the monitoring agent via A2A. Once the issue is understood, it then coordinates with the operational agent for remediation, all of this happening seamlessly.

Technical Implementation and Architecture

• Agent Card Lifecycle
  Each agent publishes an agent card via a well-known endpoint (/.well-known/agent-card.json). These cards describe what the agent can do, its HTTP endpoints, capabilities, and authentication requirements.
• Task Workflow
  • A client (user or orchestration agent) sends a request to a “client agent.”
  • The client agent uses A2A to discover other agents and decides which one should handle the task.
  • A task object (with ID, metadata, and context) is passed through JSON-RPC to the selected remote agent.
  • Once completed, the agent returns an artifact (JSON, text, multimodal result) to the requester.
• Authentication
  Amazon Bedrock Agents can authenticate inbound and outbound A2A calls using OAuth 2.0 or IAM (SigV4), enabling secure, identity-aware communication between agents.
• Session Management
  Amazon Bedrock AgentCore Runtime automatically injects a session ID header to isolate each interaction (X-Amzn-Bedrock-AgentCore-Runtime-Session-Id).
• Networking & Security
  You can run A2A servers in a VPC and use PrivateLink for secure, private communication. Amazon Bedrock AgentCore supports lifecycle rules to terminate idle and long-running sessions, thereby improving resource efficiency.

Challenges and Considerations

• Protocol Complexity
  Implementing the A2A protocol requires building a JSON-RPC 2.0 server, handling task lifecycle, and managing agent cards.
• Authentication Overhead
  Agents need secure credentials (AWS IAM roles or OAuth tokens), which demands a robust identity management strategy.
• Discovery at Scale
  In a system with many agents, orchestrators must efficiently query agent cards and determine who handles what, requiring a well-designed architecture and effective orchestration logic.
• Resource Cost
  While Amazon Bedrock AgentCore Runtime is serverless, long-running agent tasks, many simultaneous sessions, or A2A invocations can add to the cost.

Conclusion

The introduction of the agent-to-agent (A2A) protocol in Amazon Bedrock AgentCore Runtime represents a major leap forward for building interoperable, resilient, and scalable multi-agent AI systems. By utilizing standardized communication, secure authentication, and dynamic discovery, agents built on various frameworks and platforms can now coordinate seamlessly, enabling complex workflows such as incident response, orchestration, and cross-domain automation.

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