Your engineering team just built an AI agent that can access your CRM, generate reports, and create support tickets. It works great. But now your sales team wants their AI agent to collaborate with your support agent, and your marketing team's AI needs to work with both of them.
Suddenly you're staring at a custom integration nightmare.
Two protocols solve this problem, but they work in completely different ways: Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent Protocol (A2A). Understanding which one you need (or if you need both) will save you months of development time and prevent expensive architectural mistakes.
Here's what each protocol actually does and when to use them.
The core difference: what gets connected
[Image from Google]
MCP and A2A solve different but complementary problems in AI system integration.
MCP connects AI agents to tools and data (vertical integration). Think databases, APIs, file systems, business applications. It's like giving your AI agent hands so it can actually do things in your systems.
A2A connects AI agents to other AI agents (horizontal integration). It lets them discover each other, delegate tasks, and collaborate on complex workflows.
As Google puts it: "If MCP is the socket wrench, A2A is the conversation between mechanics."
|
Feature |
A2A (Google) |
MCP (Anthropic) |
|
Focus |
Agent-to-agent communication |
Agent-to-tool integration |
|
Type |
Horizontal integration |
Vertical integration |
|
Scope |
Multi-agent collaboration |
Single-agent capability expansion |
|
Interoperability |
Cross-platform, cross-vendor |
Mostly tied to an individual agent stack |
One AI agent accessing your CRM? That's MCP. Three different AI agents collaborating to handle a customer complaint? That's A2A.
MCP: Give your AI agents hands and eyes
Your AI agent is smart, but it can't actually do anything in your business systems. MCP fixes that.
Instead of building custom code every time you want your AI to connect to a new tool, MCP creates a standard way for any AI agent to talk to any business system.
How MCP works in practice
[Image from Generative AI Lead at AWS, Eduardo Ordax]
Let's say your customer support AI needs to help with a billing question:
- Customer asks: "Why was I charged twice for my subscription?"
- AI agent uses MCP to query your billing database in real-time
- AI agent uses MCP to check payment processor logs
- AI agent uses MCP to create a refund ticket in your support system
- AI responds with the answer and confirms the refund is processing
Without MCP, you'd need custom code connecting your AI to each system. With MCP, you write the integration once and any AI application can use it.
MCP's biggest advantages
Real-time data access: Your AI agents always have current information instead of outdated training data. No more "I don't have access to recent information" responses.
One integration, many applications: Write an MCP server for Salesforce once, and every AI agent in your company can use it.
Built-in security: Each MCP server controls exactly what the AI can access. Your finance AI can read budget data but can't modify it.
Bidirectional communication: AI agents can both read data ("What's this customer's order history?") and take actions ("Create a support ticket").
When MCP makes sense
Choose MCP when you need to:
- Connect a single AI agent to multiple business tools
- Give AI agents real-time access to your databases
- Enable AI to take actions (create tickets, send emails, update records)
- Standardize how AI accesses your existing systems
Red flags for MCP: If you're trying to coordinate multiple AI agents or need cross-platform agent collaboration, MCP won't solve that problem.
A2A: Let your AI agents collaborate
[Image from Google]
Your marketing AI is great at campaign analysis. Your sales AI excels at lead qualification. Your support AI handles customer issues perfectly. But what happens when a complex customer journey requires all three to work together?
A2A solves this by creating a standard way for AI agents to discover each other, delegate tasks, and share information.
How A2A works in practice
[Image from Google]
Here's a customer journey that requires multiple AI agents:
- Marketing AI identifies a high-value lead from campaign data
- Marketing AI uses A2A to delegate lead qualification to Sales AI
- Sales AI analyzes the lead and determines it needs technical consultation
- Sales AI uses A2A to request technical assessment from Support AI
- Support AI reviews technical requirements and sends findings back
- Sales AI combines all information and schedules appropriate follow-up
- Marketing AI receives final status and updates campaign tracking
Each AI agent stays independent but can collaborate when needed.
A2A's biggest advantages
Cross-platform collaboration: Your LangGraph marketing agent can work with your CrewAI sales agent and your custom support agent.
Task delegation: Agents can hand off specialized work instead of trying to do everything themselves.
Vendor independence: You're not locked into one AI framework or platform.
Scalable workflows: Add new specialized agents without rebuilding existing integrations.
When A2A makes sense
Choose A2A when you need to:
- Coordinate multiple AI agents across different teams or systems
- Break complex workflows into specialized agent roles
- Avoid vendor lock-in while scaling AI operations
- Enable cross-department AI collaboration
Red flags for A2A: If you have a single AI agent that just needs access to tools and data, A2A adds unnecessary complexity.
Why you'll probably need both
The most powerful AI setups use MCP and A2A together. Here's why:
MCP gives agents capabilities. A2A gives them colleagues.
Imagine a customer calls with a complex technical issue:
- Support Agent (using MCP) pulls customer history from your CRM
- Support Agent (using A2A) delegates technical analysis to Engineering Agent
- Engineering Agent (using MCP) queries system logs and error databases
- Engineering Agent (using A2A) reports findings back to Support Agent
- Support Agent (using MCP) creates escalation ticket and schedules callback
Without MCP, the agents couldn't access your systems. Without A2A, they couldn't collaborate.
Technical reality check
MCP implementation complexity
Getting started: Low complexity. Pre-built servers exist for common tools like Google Drive, Slack, and PostgreSQL.
Custom development: Medium complexity. You'll need to build MCP servers for proprietary systems, but the specification is straightforward.
Security considerations: High importance. MCP servers often need broad permissions, so implement fine-grained access controls and credential management from day one.
Current maturity: Production-ready. Companies like Snowflake and Cloudflare already have official MCP servers.
A2A implementation complexity
Getting started: Medium complexity. Google provides SDKs and sample projects, but multi-agent coordination requires careful planning.
Custom development: High complexity. Designing effective agent collaboration workflows isn't trivial.
Security advantages: Built-in privacy. Agents can collaborate without exposing internal logic or sensitive data.
Current maturity: Early adoption. Production-ready version expected later in 2025.
What this means for your AI strategy
Both protocols address the same fundamental problem: AI agents that work in isolation aren't nearly as valuable as AI agents that can connect to your systems and collaborate with each other.
MCP solves the "AI can't actually do anything" problem. Your agents get real-time data access and the ability to take actions in your business systems.
A2A solves the "AI agents can't work together" problem. Your specialized agents can collaborate on complex workflows without expensive custom integrations.
The companies that figure out both protocols will have AI systems that are dramatically more capable than their competitors. The companies that ignore them will be stuck with expensive, isolated AI projects that never deliver real business value.
Next steps to implementing MCP and A2A
If you're just starting with AI agents, begin with MCP. Get one agent working really well with your core business systems before trying to orchestrate multiple agents.
If you already have AI agents working independently, start planning for A2A. The production release in late 2025 will unlock collaboration patterns that are impossible today.
Either way, understanding both protocols now gives you a significant advantage in building AI systems that actually work in the real world.
Need help implementing MCP or A2A at your company? We can deploy teams of senior AI product experts and engineers in 4-6 weeks who know how to build agents. Let's talk about your specific use case.
MCP resources
A2A resources
