Agent-to-Agent (A2A) Protocols: Why They Matter for AI Product Discovery

Agent-to-Agent (A2A) communication is a set of emerging protocols that allow AI agents to discover, query, and exchange information about products and services autonomously. While still early, A2A is likely to become a significant channel for product discovery — especially for SaaS tools that AI agents use on behalf of their users.

What Is A2A?

A2A refers to the ability of AI agents to communicate directly with each other. Instead of a user asking ChatGPT 'What is the best project management tool?' and ChatGPT generating an answer from its training data, an A2A scenario looks like this: a user's AI assistant contacts a procurement agent, which queries multiple product agents, which return structured capability descriptions. The user gets a recommendation based on real-time, machine-readable product data — not cached training data.

Why Should SaaS Founders Care?

Today, most AI product discovery relies on training data and web search. But the trajectory is clear: AI agents are becoming autonomous purchasing assistants. When an AI agent can directly read your product's capabilities, pricing, and integration options in machine-readable format, you become discoverable in a way that static web pages cannot match.

• AI agents will compare products programmatically, not by reading marketing copy.
• Products with machine-readable capability descriptions will be discoverable; products without them will be invisible.
• The transition has already begun: OpenAI's function calling, Anthropic's tool use, and Google's agent frameworks all enable agents to interact with structured data.

What to Prepare on Your Site Today

You do not need to build an A2A API today. But you can take steps now that will make your product ready when agent-to-agent discovery matures.

1. Create a /llms.txt file: This machine-readable summary of your product, key pages, and capabilities is already recognized by several AI engines. It is the closest thing to an A2A-ready product description that exists today.
2. Use SoftwareApplication schema with detailed feature lists: AI agents will parse structured data before reading marketing copy. Make your features, pricing, and integration options machine-readable.
3. Build a /compare page with structured feature tables: When an AI agent compares products, it will favor pages that present comparison data in extractable formats (tables, lists, JSON-LD).
4. Expose an API capabilities page: If your SaaS offers an API, document it in a way that AI agents can read — OpenAPI spec, clear endpoint descriptions, authentication requirements.
5. Create an /integrations page with structured data: List every integration with clear names, categories, and descriptions. AI agents will use this to match your product to user workflows.

A2A and the Future of AEO

Answer Engine Optimization (AEO) today focuses on getting cited in AI-generated text answers. A2A extends this: it is about being discoverable and queryable by AI agents directly. The sites that build machine-readable product descriptions now will have a significant head start when A2A becomes mainstream.

Think of it this way: SEO was about being found by search engines. AEO is about being cited by AI answers. A2A is about being recommended by AI agents. Each layer builds on the previous one.

Start with a baseline. Run a free AEO audit to see how well AI engines currently understand your product. This is the foundation for A2A readiness.