Meghna Bharadwaj

Not another chatbot builder. Not another connect your API to an LLM tool.
I mean the boring, painful infrastructure that you don't want to build every time you ship an AI agent.

Things like:

• Agent orchestration

• RAG

• 900+ integrations

• Evaluations

• Observability & analytics

• LLMOps

• Payments & usage tracking

Basically:

Hi Product Hunt Family,
We ve been building something we think the AI agent ecosystem is missing.

Domain experts have knowledge worth paying for, but their income is capped by their calendar. One consultation, one hour, one client at a time. AI agents change that math: package what you know once, and let it solve the same problem for hundreds of people without you in the room.

Then we ran into a problem.Building the agent turned out to be the easy part. The real questions were:

So, there obviously have been a lot of tools focused on building AI agents. Very few, however, are talking about what happens after you deploy them.

Coming from a software development background, this feels like the obvious next layer.

We have CI/CD, observability, testing, incident management, version control, and performance monitoring for traditional software.

What does that stack look like for AI agents?