Agent Interoperability Working Group Plan | DRAFT

[ratnopamc]

Focus Group Information

Focus Group Name: Agent Interoperability Working Group

Mission: To establish effective agent-to-agent communication patterns by evaluating and implementing standard protocols for enterprise-grade agentic systems.

Problem We Are Trying To Solve:

As agentic AI systems grow in complexity, effective communication between agents across different subsystems becomes crucial. Organizations face challenges in implementing standardized agent-to-agent interactions, including:

• Choosing appropriate communication protocols and architectures
• Implement inter-agent messaging (direct messaging, broadcasting, API and standard protocol based interaction)
• Implementing security measures and guardrails
• Optimizing for latency, scalability, and performance
• Integrating components like Agent Gateways and Mesh systems to facilitate session fan-out, bidirectional SSE, protocol negotiation or per-agent tenancy

There's a need for practical guidance on implementing these systems effectively in enterprise environments, considering various protocols and architectural patterns.

Purpose

The Agentic AI Interoperation and Communication focus group is dedicated to developing and promoting open standards for effective communication and interaction between AI agents and systems. This initiative encompasses areas and industry standards such as Agent-to-Agent (A2A) Protocol for seamless inter-agent communication, Model Context Protocol (MCP) for standardized tool access, and unified agent communication mechanisms and messaging formats. By collaboratively establishing these standards and providing reference architectures, the community aims to create a robust foundation for interoperable AI systems.

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Working Group Members:

Core Team
Harish Rao
Ratnopam Chakrabarti
Michael Zayats

Members
Gary Lerhaupt
Roman Nersisyan
Sivakumar N
John Parello
Open
Open
Open
Open

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Goals: What We Want to Achieve

[Primary Goal]: Build repository of architecture patterns, best practices and reference implementations(blueprints) focusing on agent-to-agent communication.

• Create code artifacts demonstrating inter-agent communication patterns:

  • Supervisor-based communication
  • Pub-Sub mechanisms
  • Peer-to-Peer interactions
  • Agentic Mesh architecture

• Provide best practice guide documents on:

  • Protocol evaluation and comparison (A2A, MCP, ACP)
  • Security guardrails and best practices
  • Performance optimization strategies
  • Scalability benchmarks
  • Agent discovery and mesh integration
  • Message format and data handling

[Secondary Goal]: Foster Community Adoption and Collaboration

• Create a collaborative platform for ongoing refinement of protocols and standards
• Follow an open contribution model for community members to submit improvements and extensions to blueprints
• Share case studies, reference implementations from the field through community meetings, open webinars and blogposts (adhering to the specific org standards and guidelines)

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Core Areas of Depth

Protocol Architecture & Standards

• Protocol Evaluation: Systematic comparison of A2A, MCP and ACP protocols
• Message Format Standardization: JSON-LD, Protocol Buffers, or custom schemas for agent communication
• Transport Layer Standards: HTTP/2, WebSockets, gRPC, or message queues (Kafka, Nats)
• Discovery Mechanisms: Service registry patterns, DNS-SD, or distributed discovery protocols

Agent Orchestration, Communication and Integration Patterns

• Supervisor-Agent Hierarchies: Central coordination with delegated task execution
• Peer-to-Peer Networks: Decentralized agent collaboration without central authority
• Publish-Subscribe Systems: Event-driven communication for loose coupling
• Agent Mesh/Agent Gateway Architecture: Mesh/Gateway patterns adapted for agents for protocol mediation, agent discovery, security enforcement, circuit breaking, retry and routing logic implementation.
• Request-Response Patterns: Synchronous communication with timeout handling
• Integration Patterns: Third party and external API Integration, multi-cloud and hybrid deployment

Data Handling & Context Management

• Context Propagation: Maintaining conversation state across agent interactions
• Data Serialization: Efficient encoding/decoding of complex data structures
• Schema Evolution: Backward compatibility for protocol updates
• Large Payload Handling: Streaming or chunking for big sized data transfers
• Context Isolation: Preventing data leakage between agent conversations

Performance & Scalability

• Latency Optimization: Connection pooling, caching, and geographic distribution
• Load Balancing: Distributing requests across agent instances
• Circuit Breaker Patterns: Fault tolerance and graceful degradation
• Resource Management: Memory, CPU, and network resource allocation
• Horizontal Scaling: Auto-scaling agent populations based on demand

Operational Excellence

Observability: Logging, monitoring and distributed agent tracing
Health Checks: Agent availability and capability monitoring and graceful de-commissioning
Alerting Systems: Proactive issue detection and notification
Agent Registry: Centralized catalog of available agents and capabilities
Configuration Management: Dynamic agent configuration and feature flags
Security: Enterprise Grade Security for Agents including authN/authZ, message encryption, audit and compliance standards

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Implementation Roadmap & Timeline

Phase 1 (Months 1-3): Foundation

• Protocol evaluation
• Implement core agent communication patterns
• Agent Gateway/Mesh reference architecture

Phase 2 (Months 4-6): Core Patterns

• Communication pattern implementations
• Performance optimization guidelines
• Basic Operational Guidance

Phase 3 (Months 7-9): Enterprise Features

• Advanced security and compliance controls
• Complex agent interaction and integration patterns
• Implement Security & Governance requirements

Phase 4 (Months 10-12): Community & Adoption

• Reference implementations
• Case studies, best practices and blogposts

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🎯 Success Metrics

Technical Metrics:

• Reference implementations for each agent communication pattern
• Best Practice Documents for latency/throughput, cost and scalability
• Evaluation checklist for operational excellence

Community Metrics:

• Number of contributed blueprints
• Adoption rate across organizations
• Community engagement (meetings, contributions)