A2A Net

Automatically optimize an AI agent from your B2B SaaS APIs

SaaS

Developer Tools

Artificial Intelligence

Today I’m launching A2A Net, the platform for B2B SaaS to automatically optimise an AI agent from their APIs that their customers can use in their website, app, Slack, Teams, Copilot, and Gemini Enterprise! Despite “SaaSpocalypse” being one of the best terms to come out of 2026, I’m optimistic about the future of SaaS. There’s no doubt that it’s easier to build software now, but that doesn’t change economics: one company can create software for one hundred companies cheaper than one hundred companies can create that software for themselves. That one company will also be specialised, and as a result will be able to solve the problem better, provide better support, etc. What’s more, SaaS stands to benefit more than any other industry from AI, because SaaS and AI both run on computers, AI can add value to every corner of SaaS! This is especially true for SaaS categories that are used frequently or have lots of data, records, or workflows, e.g. GTM, ATS, customer success, CRM, project and work management, marketing operations, finance and accounting, customer support and service desk, HRIS, procurement, etc. That’s not to say that it’s easy to add AI to SaaS... Everyone who’s had to build and deploy an AI agent to production knows: it’s hard to build and maintain production-ready agents. Prompt changes fix one thing and break another, and building good evals is genuinely difficult. Coding agents help, but require constant input from an AI Engineer. Prompt optimisation algorithms like GEPA exist, but the optimiser requires a numerical score and natural language feedback. For that, you need to build good evals, which are not only difficult and time-consuming to create, but typically represent a small subset of real-world tasks. This is why constant input from an AI Engineer is required, to give feedback to the coding agent. The coding agent is the optimiser, and the AI Engineer is the evaluator. A2A Net solves this problem by taking the AI Engineer out of the loop. Agents are built and tested in the same way. But, instead of telling a coding agent what to improve, you rate the agent’s sessions. Those ratings are used to teach an LLM-as-a-judge to rate sessions like you. That is then used to automatically optimise the agent. The automatic optimiser, and features like code mode, and file upload and download, make it easy to build and maintain production-ready agents that your customers can use to perform actions, analyse data, complete complex multi-step workflows, and more. If you have an OpenAPI spec or an MCP server, you can create an agent for your B2B SaaS in 5 minutes by going to A2A Net’s landing page, entering your website, and clicking “Create agent”! One customer found that the agent that was built for them from A2A Net’s landing page was better than the agent they had been building for months. Another deployed an agent to their customers in a week, who were using it to complete complex multi-step workflows, like uploading unstructured spreadsheets with 50K+ cells into their platform! I'd love to know, if you're a SaaS founder, product manager, or engineer, have you added an AI agent to your product? If you haven't, why not? If you have, what were your biggest pain points? Is a Slack, Teams, Copilot, or Gemini Enterprise agent on the roadmap?