If I told you that you could get AI infrastructure out of the box, what would you need?
by•
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:
You build the agent. We take care of everything around it.
What would you need?
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Let’s be real, creating a system for tracking payments and usage from the ground up is always a hassle. Figuring out how to incorporate per-token costs into user billing without going over budget is a challenge that often doesn’t get the recognition it deserves.
Kopai
@linda__jackk Exactly, we are actually building something called @Kopai . The point is to be the Cloud for AI Agents. Cloud basically gives you infra and maintains your billing out-of the box and we do the same for AI infrastructure.
It is pay-per use and creation has a pretty extensive free tier. You can also export it out and we maintain your billing. You can set your budget accordingly and it does not go over that. Would love if you could give it a try and give us some feedback
@meghna_bharadwaj What really matters to me is real-time observability that allows you to trace nested tool calls without introducing a ton of latency. Debugging those dynamic agent loops can get pretty chaotic in no time!
Kopai
@micah_mikee What if the orchestration and the harness came out of box, would you be able to trust it if the observability was shown side by side?
It sounds like a great idea, but I've noticed that state management is often the Achilles' heel of these all-in-one stacks. How effectively does it manage complex, long-running workflows that need to pause for human approval?
Kopai
@harry__burnss That's exactly what we are trying to build. We put a human in the loop before hand at evaluation so that they already get a feel for that.
However, what you say is very important. If you were to use it, would you expect the agent to know where to ask for approval/escalate, or would you want this to be more deterministic?
@meghna_bharadwaj I really like the idea of focusing on just the core logic without getting bogged down by the evaluation loop or RAG chunking pipelines. I'm curious to see how truly lock-in free or modular it can be.
Kopai
@millie_masonn Hi, Can you check out @Kopai . That is exactly what we are trying to do. Would love for some feedback so that we can improve on it
The item I'd add: versioned output contracts.
The moment an agent's output gets persisted, or cached, or read by an eval, its schema is a wire format. Rename a field and you haven't refactored, you've done a data migration, and the release that's still running has to be able to read what the new one writes. It's invisible on day one because you're the only consumer of your own JSON. We only call models with a strict JSON schema and never by parsing prose out of a response, and strict mode has edges you inherit as product constraints. It rejects minItems, so "at least ten of these" is something you handle in code, not something the schema can promise.
The other thing I'd want to know before adopting any of it: what happens to the run that succeeds and returns something wrong? Everything on the list treats a bad result as an error, and the expensive case is a clean 200 with a useless payload.