Here’s what I’ve heard again and again: it's gotten much easier to build a proof-of-concept AI agent, but turning that prototype into a high-quality, scalable, and cost-effective product is still a massive chore, involving endless trial-and-error with prompts, models, tools, testing, analysis, etc.

We built Gensee to automate that "last mile" from prototype to production.

Here’s how it works:

- You provide the GitHub link to your project, a Docker image, or a Zipped package of your agent source to Gensee. They can be written in any framework or without a framework, as long as it’s in Python. No code modification or annotation needed.

- We handle input/output identification, model/tool call identification, test case generation, metrics generation, testing, automated optimization, server provisioning, containerization, tool/model calling, and endpoint creation to get it live as an API.

- You see detailed evaluation results with customized metrics and test cases, all fully automated.

- We optimize your agent automatically to achieve better quality, cost, and/or latency. You can download our optimized agent code, all transparent.

- You can choose any optimized or original agent configurations to serve. Simply copy the API endpoint to your frontend code calling the agent.

To support fellow developers, we give every new user 500 free monthly credits, enough to cover one to two agent deployment, optimization, and initial model and tool usage costs. If your usage grows, it becomes a cost-efficient pay-as-you-go service that scales with you.

We're still in beta and would love to get your feedback. Do you prefer no-code agent generation instead of source code uploading? Should Gensee also run your frontend and other code in addition to agents? Any other optimization goals you have? Any key missing features?

Thanks for taking a look!

Access Gensee: https://platform.gensee.ai