Today, agents work in silos. Tomorrow, your CRM agent will negotiate with your customer's procurement agent. Here is how A2A communication reshapes enterprise software.
AI agents today operate within single applications. Your email agent drafts replies. Your CRM agent updates records. Your analytics agent generates reports. But they don't talk to each other. Agent-to-agent (A2A) communication is the next frontier: a world where an agent in one system can discover, negotiate with, and delegate tasks to agents in other systems. Google's A2A protocol, announced in early 2025, is the first serious attempt at standardizing this. This guide covers what A2A means, how it works, and what it means for the future of enterprise SaaS.
Consider a simple business process: a customer requests a custom quote. Today, this involves:
Steps 2-5 are manual because the CRM agent cannot talk to the pricing agent, which cannot talk to the email agent. A2A eliminates these manual handoffs.
Each agent publishes an "Agent Card" -- a JSON document describing what the agent does, what tasks it can handle, and how to communicate with it. This is similar to how APIs publish OpenAPI specs, but for AI agents.
# Example Agent Card (published at /.well-known/agent.json)
{
"name": "PricingAgent",
"description": "Generates custom pricing quotes based on product configuration and volume.",
"capabilities": ["quote_generation", "discount_calculation", "term_negotiation"],
"endpoint": "https://pricing.company.com/a2a",
"authentication": "oauth2",
"input_schema": { ... },
"output_schema": { ... }
}
When Agent A needs a capability it doesn't have, it discovers Agent B's card, sends a structured task request, and waits for the result. The communication uses a standardized protocol:
For complex tasks, agents negotiate. The sales agent proposes a discount. The pricing agent checks against margin rules. The compliance agent validates the terms. Each agent operates within its authority boundaries, and the conversation converges on a result that all parties accept.
A central orchestrator agent routes tasks to specialist agents. The orchestrator knows all available agents and decides which one to delegate to. This is the simplest pattern and works well for enterprise workflows with a clear coordination point. Similar to how LangGraph orchestrates tool calls, but across independent agent systems.
Agents discover and communicate directly without a central coordinator. More scalable but harder to debug and monitor. Suitable for cross-organization agent communication where no single party controls the orchestration.
An agent marketplace where specialist agents register their capabilities and other agents discover and hire them for specific tasks. Think of it as an "app store" for agent capabilities.
When agents from different organizations communicate, security boundaries are critical:
The Strategic Shift:
SaaS products will compete not just on features, but on the quality of their AI agent's capabilities and how well it integrates with other agents. An ERP system whose agent can negotiate with a shipping provider's agent, a payment processor's agent, and a compliance checker simultaneously wins over one that requires manual coordination.
Building production-ready agents today is the foundation for A2A readiness tomorrow. Start with our production agent blueprint, add function calling for API integration, and design your agent's capabilities as discoverable services.
APIs are deterministic: you call a specific endpoint with specific parameters and get a predictable response. A2A adds reasoning: the sending agent decides what to ask, the receiving agent decides how to handle it, and both can negotiate and adapt. It is the difference between calling a function and having a conversation with a colleague.
MCP (by Anthropic) standardizes how an agent connects to tools and data sources within a single system. A2A standardizes how agents communicate across systems. They are complementary: MCP gives an agent its tools, A2A lets it collaborate with other agents.
Build production agents now and design them with clear capability boundaries, structured inputs/outputs, and proper authentication. This makes them A2A-ready when standards mature. Don't wait for the standard to finalize before building the agent.
We design agent architectures ready for the multi-agent future. From internal automation to cross-organization A2A communication.
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