Vikas Sahani

Posted on Jan 16 • Originally published at github.com

Exploring Google's Agentic Interoperability Stack: A Practical Guide

#ai #agents #google #interoperability

Exploring Google's Agentic Interoperability Stack: A Practical Guide

What you'll learn: How Google's interconnected agent tooling components work together, what problems they solve, and what becomes possible when you combine them.

🎥 Watch the Visual Walkthrough

What Is This Stack?

Google has released several agent-related tools and protocols that, when combined, form an ecosystem-like experience for building AI agents:

ADK (Agent Development Kit) - Framework for building agents
Google MCP Servers - Connections to Google services
A2A (Agent-to-Agent Protocol) - How agents communicate
A2UI (Agent-to-UI) - How agents generate interfaces
UCP (Universal Commerce Protocol) - How agents handle commerce

Important: This isn't a single product called "Google Agentic Ecosystem." These are separate components that work well together.

The Core Problem

Before: Building an AI agent that could search flights, check loyalty points, and complete checkout required:

Custom integrations for each service
Manual UI coding for each interaction
No way to delegate tasks to other agents
Complex payment authorization flows

After: With this stack, agents can:

Use standardized connections (MCP)
Generate UIs declaratively (A2UI)
Delegate to specialized agents (A2A)
Handle commerce flows (UCP)

The Five Components Explained

1. ADK (Agent Development Kit)

What it does: Provides the foundation for building multi-agent systems.

Simple Example:

from google.adk import Agent

# Create a specialized agent
travel_agent = Agent(
    name="TravelPlanner",
    model="gemini-2.5-flash",
    tools=[search_flights, book_hotels],
    instruction="Help users plan trips efficiently."
)

What becomes possible:

Build agents that orchestrate multiple sub-agents
Manage conversation state across interactions
Integrate with external tools and APIs
Handle errors and fallbacks gracefully

📚 Learn more about ADK

2. Google MCP Servers

What it does: Provides standardized connections to Google services using the Model Context Protocol (MCP).

Available Connections:

Google Maps (geocoding, directions, places)
BigQuery (data analysis)
Google Cloud Engine (VM management)
Firebase (real-time database)
Analytics (metrics, reporting)
...and many more

Key Insight: MCP is an open standard created by Anthropic. Google provides official server implementations, so agents can connect to Google services using a standardized protocol.

What becomes possible:

Agents can discover and use Google services automatically
No need to write custom API integrations
Works with any MCP-compatible agent framework

📚 Learn more about Google MCP Servers

3. A2A (Agent-to-Agent Protocol)

What it does: Enables agents from different vendors to communicate and delegate tasks.

Example Flow:

Travel Agent → (A2A) → Loyalty Agent → (A2A) → Payment Agent

What becomes possible:

A Google-built agent can delegate to a Microsoft-built agent
Agents can discover each other's capabilities
Tasks can be delegated with full context
Security is built-in (OAuth, IAM)

Real-World Scenario: Your travel agent (built with ADK) can delegate loyalty point checking to your company's existing Salesforce agent, without you having to rebuild that functionality.

📚 Learn more about A2A

4. A2UI (Agent-to-UI)

What it does: Lets agents generate native UIs declaratively instead of just returning text.

Before (text-only):

Agent: "I found 3 flights. Option 1: $450, Delta, 5h 30m..."

After (interactive UI):

{
  "type": "comparison_table",
  "columns": ["Airline", "Price", "Duration"],
  "rows": [
    {"airline": "Delta", "price": "$450", "duration": "5h 30m"},
    {"airline": "United", "price": "$480", "duration": "5h 15m"}
  ],
  "actions": [{"label": "Book Now", "handler": "book_flight"}]
}

What becomes possible:

Agents can present complex data visually
Users can interact with results (sort, filter, select)
UIs work across platforms (web, mobile, desktop)

📚 Learn more about A2UI

5. UCP (Universal Commerce Protocol)

What it does: Provides standardized primitives for agent-driven commerce.

Core Capabilities:

Product discovery
Cart management
Checkout flows
Payment authorization (with cryptographic proof)

Compatible with: Shopify, Stripe, PayPal, Adyen, Mastercard

What becomes possible:

Agents can handle end-to-end shopping experiences
Users maintain control over payment authorization
Works across different payment providers

📚 Learn more about UCP

How They Connect

The Stack (Bottom to Top)

Observability Layer - Logging, monitoring, debugging
Trust & Policy Layer - IAM, OAuth, consent management
A2A Inter-Agent Layer - Cross-vendor agent communication
MCP Tool Layer - Service integrations (Google MCP Servers)
Orchestration Layer - Multi-agent coordination (ADK)
UI Layer - Native UI generation (A2UI)

Commerce flows (UCP) work across all layers.

Use Case 1: Consumer Shopping Assistant

The Scenario

User: "I want a coffee machine under $200"

How the Stack Enables This

Step 1: Orchestration (ADK)

Main agent understands intent
Breaks down into sub-tasks

Step 2: Product Search (MCP)

Connects to e-commerce APIs
Searches across multiple retailers

Step 3: Loyalty Check (A2A)

Delegates to user's loyalty agent
Retrieves available discounts

Step 4: UI Presentation (A2UI)

Generates comparison table
Shows products with prices and discounts

Step 5: Checkout (UCP)

Handles cart and payment
Applies loyalty rewards
Confirms purchase

What This Unlocks

For users: Complete shopping in one conversation
For developers: Build once, works across retailers
For businesses: Integrate with existing loyalty systems

📚 Full walkthrough

Use Case 2: Enterprise Workflow Automation

The Scenario

Automating expense approval across multiple systems (Workday, Stripe, email)

Traditional Approach

Employee submits in Workday
Manager gets email, logs into Workday
Finance processes in Stripe
Manual coordination between systems

With the Stack

# Employee submits
expense_agent.submit(amount=450, category="travel")

# HR Agent validates (via A2A → Workday)
hr_agent.validate_expense(expense_id)

# Manager Agent approves (via A2A)
manager_agent.approve(expense_id)

# Payment Agent processes (via A2A → Stripe)
payment_agent.process_payment(expense_id)

# Confirmation sent
notification_agent.send_confirmation(employee_id)

What This Unlocks

For employees: Submit once, automatic routing
For managers: Approve from any interface
For IT: No custom integrations between systems

📚 Full walkthrough

Use Case 3: Developer CI/CD Pipeline

The Scenario

Automating code review, security scanning, testing, and deployment

The Flow

Developer commits → GitHub
Code Review Agent analyzes (via MCP → GitHub API)
Security Agent scans (via A2A)
Test Agent runs tests
Deploy Agent deploys (via MCP → Cloud Run)
Monitoring Agent tracks performance

What This Unlocks

For developers: Faster feedback loops
For security teams: Automated vulnerability detection
For ops: Consistent deployment process

📚 Full walkthrough

Comparing Approaches

Feature	Google Stack	Anthropic MCP	OpenAI Swarm
Multi-agent orchestration	✅ ADK	❌ No framework	✅ Swarm (OpenAI-only)
Agent-to-agent protocol	✅ A2A (vendor-neutral)	❌ Not included	❌ Proprietary
Native UI generation	✅ A2UI (cross-platform)	❌ Text-only	✅ MCP Apps (web-only)
Commerce primitives	✅ UCP	❌ Not included	❌ Not included
MCP Server Ecosystem	✅ Official Google servers	✅ Created MCP standard	✅ Compatible
Open Governance	✅ A2A/UCP (Linux Foundation)	✅ MCP (Linux Foundation)	❌ Proprietary

Production Considerations

Before deploying agents, consider:

Trust & Security

How will users consent to agent actions?
What audit logging is needed?
How are tool permissions managed?
How is payment authorization secured?

Safety & Governance

How to prevent hallucinations?
What guardrails are needed?
How to handle agent errors?
When should humans intervene?

User Experience

What happens if agents fail?
How to monitor latency?
How to show agent reasoning?
What fallback UX is needed?

📚 Full readiness checklist

Deployment Options

1. Fully Managed (Google Cloud)

Best for: Startups, rapid prototyping
Pros: Zero infrastructure management
Cons: Vendor lock-in

2. Hybrid (Multi-Cloud)

Best for: Enterprises with existing cloud investments
Pros: Flexibility, vendor neutrality
Cons: Complex orchestration

3. On-Premises

Best for: Regulated industries (healthcare, finance)
Pros: Full data control
Cons: Higher operational overhead

📚 Reference architecture

Getting Started

1. Explore the Documentation

📚 Complete knowledge base

2. Try Official Quickstarts

3. Join the Community

Key Takeaways

For Product Managers

Understand what's possible with agent interoperability
Explore use cases relevant to your domain
Consider how agents could improve user workflows

For Engineers

Learn how components work together
Experiment with official quickstarts
Build prototypes to test feasibility

For Executives

Evaluate strategic fit for your organization
Understand open standards vs. proprietary approaches
Consider vendor neutrality benefits

Sources

All information is sourced from official documentation:

ADK: Google Developers Blog (April 2025)
Google MCP Servers: Google Cloud Blog (Dec 2024)
A2A: Linux Foundation (June 2025)
A2UI: Google Developers Blog (Dec 2025)
UCP: Google Developers Blog (Jan 2026)

Author & Disclaimer

Author: Vikas Sahani

GitHub: @VIKAS9793

Repository: google-agentic-interop-stack

Disclaimer

This is an independent educational/research project. All third-party names, trademarks, protocols, documentation, and referenced materials belong to their respective owners. This project is not affiliated with or endorsed by Google, Anthropic, or other referenced organizations.

Last Updated: January 16, 2026

License: MIT

💬 Discussion

What use cases are you most excited about? How might you use these components in your projects? Share your thoughts in the comments! 👇