A2A Net

A2A Net is an AI agent builder for B2B SaaS teams. Connect APIs or an MCP server, test agents in real conversations, and publish to web, Slack, Teams, Copilot, or Gemini Enterprise.

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What A2A Net is

A2A Net is an AI agent builder for B2B SaaS products. It is designed for product and engineering teams that want to create a customer-facing agent from their existing APIs or an MCP server, then improve it with real conversations before publishing it to channels such as a website, app, Slack, Teams, Copilot, and Gemini Enterprise.

The product’s workflow starts with an OpenAPI spec or MCP server URL, moves into conversation testing and rubric-based scoring, and then uses automated optimization to refine the agent. The homepage also describes staging and production environments, variables and secrets, customer linking, generative UI, file transfer, and protocol compatibility with AG-UI and A2A.

Core capabilities

Build from APIs or MCP

Start from an OpenAPI specification or an MCP server URL, and include custom headers when connecting the source system. This makes the agent builder fit existing SaaS APIs instead of requiring a new backend.

Conversation-based testing

Chat with the agent in a playground, review where it fails, and add rubrics to score sessions. The workflow is designed around real conversations rather than static prompt editing alone.

Rubrics and session scoring

Use rubrics to evaluate whether the agent completes a task, communicates clearly, or uses the correct tools and parameters. Those scores feed the optimization process.

Automated optimization loop

Automatically optimize the LLM-as-a-judge and then optimize the agent itself. The homepage frames this as an ongoing improvement loop for conversational agents.

Multi-channel publishing

Publish the same agent to a website, app, Slack, Teams, Copilot, and Gemini Enterprise. The product also says agents meet requirements for Slack, Microsoft, and Google marketplace publishing.

Operational tooling for deployment and integrations

Support production and staging environments, customer-specific variables and secrets, generative UI, file upload and download, and a customizable Python code mode. The homepage also notes API, CLI, and MCP access.

Common use cases

Customer-facing product agent
Build a customer-facing assistant that can act on the company’s product APIs, not just answer questions. The source copy emphasizes actions, data analysis, and complex multi-step workflows for SaaS customers.
Agent testing and optimization
Validate the agent in a playground, score real sessions against custom rubrics, and iterate on the instructions before launch. This fits teams that want an explicit evaluation loop during development.
Multi-channel deployment
Prepare one agent for several channels, then publish it to the website, app, Slack, Teams, Copilot, or Gemini Enterprise. The same workflow is meant to reduce separate implementations for each surface.
Safer staging and release workflows
Use staging and production environments to test prompts and tool calls without affecting production data. The homepage presents this as part of the agent maintenance workflow.
Operational workflow automation
Build internal or external workflow assistants that can send or receive files and use customer-specific variables or secrets during authentication and tool calls. This supports more operational SaaS use cases than a simple chat bot.

Pros and Cons

Pros

Lets teams start from existing APIs or an MCP server instead of rebuilding an agent backend from scratch.
Uses real conversation testing and rubric-based scoring before optimization, which gives the workflow a concrete evaluation step.
Supports multiple publishing targets from one agent, including website, app, Slack, Teams, Copilot, and Gemini Enterprise.
Includes deployment-oriented features such as staging and production environments, variables and secrets, and file upload and download support.
States compatibility with AG-UI and A2A protocols, which may simplify integration into sites and multi-agent systems.

Cons

The pricing page at /pricing returns a 404 on the source domain, so public plan details are incomplete from the available pages.
Some capabilities are marked as coming soon, including code mode, customer credits, customer linking, and API/CLI/MCP access, which suggests parts of the platform are still in progress.
The source pages are light on documentation about setup limits, security controls, and support process, so those details remain unclear from the available evidence.

FAQ

Who is A2A Net for?

A2A Net is built for B2B SaaS product and engineering teams that want to turn existing APIs or an MCP server into a customer-facing AI agent. The homepage also contrasts it with support-focused agents by emphasizing actions, data analysis, and multi-step workflows.

How does the setup workflow work?

The homepage says you can create an agent from an OpenAPI spec or an MCP server URL, test it in real conversations, define rubrics, and then automatically optimize it before publishing it.

Where can the agent be published?

A2A Net says agents can be published to a website, app, Slack, Teams, Copilot, and Gemini Enterprise. It also mentions AG-UI and A2A protocol compatibility, plus marketplace publishing for Slack, Microsoft, and Google.

Can it be used for internal agents as well?

The homepage highlights customer-facing agents and also says A2A Net can be used to build internal agents. It does not provide a detailed boundary between those use cases on the source pages provided.

Is pricing available?

The pricing page on the domain currently returns a 404, but the homepage shows Free, Startup, Scale, and Enterprise tiers. The public page text also mentions a pay-as-you-go model with credits and a market-rate charge for LLM tokens.

Quick Facts

Category: AI agent builder
Primary users: B2B SaaS product and engineering teams
Input sources: OpenAPI specs or MCP server URLs
Publishing targets: Website, app, Slack, Teams, Copilot, and Gemini Enterprise
Protocols: AG-UI and A2A compatible
Source domain: a2anet.com

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