OpenAI builds and deploys advanced AI models like GPT-4o for autonomous agents and workflows.
A2A Net
$225/moStarting price
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About A2A Net
A2A Net is a platform designed for B2B SaaS product and engineering teams to build, optimize, and deploy AI agents using existing APIs or MCP servers. The tool automates prompt optimization by leveraging real user conversations and customizable rubrics, enabling continuous improvement based on performance metrics. Agents can be published across multiple platforms, including websites, apps, Slack, Microsoft Teams, Copilot, and Gemini Enterprise, without requiring separate integrations for each. It provides staging environments for safe testing, supports variables and secrets for per-customer authentication, and includes features like generative UI, file handling, and a Python sandbox for custom code execution. The workflow begins with creating an agent from an OpenAPI spec or MCP server, followed by testing with real conversations, setting rubrics for evaluation, and automatically optimizing the agent. This approach reduces the need for constant AI engineer intervention and adapts to evolving models and requirements. A2A Net is particularly suited for teams looking to rapidly deploy production-ready agents while maintaining control over performance and user experience.
Key features
- Automatic optimization of agent prompts
- Production and staging environments
- Variables and secrets for per-customer authentication
- Generative UI for native platform components
- File upload and download support
- Python sandbox for custom code execution
- AG-UI and A2A protocol compatibility
- Customer credits and subscription limits
Use cases
- Deploying AI agents for customer support in Slack or Teams
- Integrating AI agents into B2B SaaS applications via website or app
- Building task-specific agents for enterprise workflows using existing APIs
Pros
- Automated prompt optimization using real conversations and rubrics
- Multi-platform deployment to websites, Slack, Teams, Copilot, and Gemini Enterprise
- Staging environment for safe testing and optimization
- Python sandbox for custom code execution and tool integration
- AG-UI and A2A protocol compatibility for multi-agent systems
Cons
- No free tier for customer-facing agents
- Requires existing APIs or MCP server to build agents
- Pricing tiers may be prohibitive for small teams
- Limited to B2B SaaS use cases
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Frequently asked questions about A2A Net
What is A2A Net and who is it designed for?
A2A Net is an AI agent builder platform designed for B2B SaaS product and engineering teams. It enables users to create, optimize, and deploy AI agents using existing APIs or MCP servers, with features tailored for production environments and multi-platform publishing.
How does A2A Net optimize AI agents?
A2A Net automates prompt optimization by analyzing real user conversations and applying customizable rubrics. The system uses LLM-as-a-judge techniques to rate sessions and refine the agent’s performance, continuously improving based on user feedback and predefined criteria.
What platforms can agents built with A2A Net be published to?
Agents can be published to websites, apps, Slack, Microsoft Teams, Copilot, and Gemini Enterprise. The platform supports native UI components and authentication methods required for each environment, streamlining deployment across multiple channels.
Does A2A Net support testing and staging environments?
Yes, A2A Net provides staging environments for testing prompts and tool calls without affecting production data. This allows teams to safely experiment and refine agents before deployment.
What integrations or customization options are available?
A2A Net supports variables and secrets for per-customer authentication, generative UI for native platform components, and a Python sandbox for custom code execution. It is also compatible with the AG-UI and A2A protocols, facilitating integration into multi-agent systems.
How can I get started with A2A Net?
To get started, users can create an agent from an OpenAPI spec or MCP server, test it with real conversations, set rubrics for evaluation, and then optimize and publish it. The platform offers a guided workflow to streamline the process from setup to deployment.
