Architecting Agentic MLOps: A Layered Protocol Strategy with A2A and MCP

What changed

The software industry is entering the "agentic era" where specialized AI agents need standardized communication patterns to discover, collaborate, and execute tasks effectively. Traditional monolithic agents and rigid pipelines are becoming bottlenecks as business requirements evolve rapidly.

Key developments:

• Agent-to-Agent (A2A) protocol emerges as a communication bus for AI agents across different systems
• Model Context Protocol (MCP) provides a universal interface for agents to discover and use tools and data sources
• The combination creates a layered architecture that decouples orchestration from execution
• This approach enables dynamic, goal-oriented operations instead of static, procedural automation

Provider comparison

A2A Protocol

• Purpose: Enables secure communication between AI agents from different vendors
• Mechanism: Uses Agent Cards to describe capabilities, JSON-RPC for messaging
• Governance: Under Linux Foundation for neutral, collaborative development
• Strengths: Interoperability, modular workflows, reduced vendor lock-in
• Limitations: Still emerging, requires agent adoption

MCP Protocol

• Purpose: Standardizes how AI systems connect to tools, services, and data
• Mechanism: Exposes Tools (actions), Resources (data), and Prompts (templates)
• Governance: Community-driven with security considerations
• Strengths: Universal integration, dynamic capability discovery, eliminates custom glue code
• Limitations: Security risks (prompt injection, tool poisoning) require additional safeguards

Layered Architecture Benefits

• Dynamic Discovery: Orchestrator discovers specialists without hardcoded knowledge
• Composable Capabilities: New tools added to MCP server automatically available to agents
• Clear Separation: Intent (orchestration) separated from execution (specialist agents)
• Adaptive Systems: Can handle new commands without rewriting core logic

Business impact

For organizations navigating AI complexity, this layered approach offers:

Scalability: Add new agents or capabilities without changing core communication logic Flexibility: Adapt to changing business requirements by composing capabilities Maintainability: Clear separation of concerns makes systems easier to understand and evolve Interoperability: Mix and match agents from different vendors seamlessly

The MLOps workflow example demonstrates practical value:

• Orchestrator agent breaks high-level goals into tasks
• Validation agent uses MCP tools to check model bias and performance
• Deployment agent uses MCP tools to deploy to appropriate regions
• Entire workflow adapts to new validation rules or deployment targets without code changes

This shift from static pipelines to dynamic agent collaboration represents a fundamental change in how organizations can approach automation. Instead of rewriting pipelines when requirements change, teams can simply add new agents or tools to the ecosystem.

Implementation Pattern

The article provides a complete code walkthrough showing:

1. MCP Server Setup: Exposes tools like fetch_model, validate_churn_model, and deploy_churn_model
2. MCP Client: High-level API for agents to discover and use capabilities
3. Task Management: Structured approach to break goals into executable tasks
4. Agent Implementation: Orchestrator, Validation, and Deployment agents following the pattern

Security Considerations

While MCP enables powerful integrations, it introduces risks:

• Prompt injection attacks
• Tool poisoning
• Unauthorized data access

The article recommends bundling MCP with security tools like MCPWatch for protection.

Beyond MLOps

The principles extend to any domain requiring dynamic collaboration:

• Customer service with specialized agents for different query types
• Content creation workflows with research, writing, and editing agents
• Data analysis pipelines with discovery, processing, and visualization agents

Getting Started

The official A2A Samples repository on GitHub provides runnable examples using these protocols, making it easier for developers to experiment with the concepts.

The layered A2A-MCP pattern represents a significant advancement in agent architecture, moving beyond isolated AI tools toward coordinated intelligence systems that can adapt and evolve with organizational needs.