I built a platform that turns any API into an AI agent in 60 seconds — here's why
A few months ago...
A few months ago I was building internal tools for a client and kept running into the same wall:
Connecting an LLM to a real API always meant writing a ton of glue code.
- Define the tools
- Handle auth
- Parse the spec
- Wire up the chat loop
Every time, from scratch.
So I built Orqis to solve it for myself. Then I figured other people probably had the same problem.
What it does
You paste an OpenAPI spec URL.
Orqis reads it, generates a set of typed tools for every endpoint, and spins up a conversational agent that can call your API in natural language.
No code. No prompt engineering. No LangChain boilerplate.
What you get
- A visual graph of your agent's tools
- A playground to test it before deploying
- A dashboard to run real tasks
- Stored API headers (auth tokens, etc.) encrypted at rest
- Human-in-the-loop confirmation for dangerous operations (DELETE, etc.)
The whole thing — from spec URL to working agent — takes under a minute.
What I learned building it
The hardest part wasn't the AI.
It was making the agent reliable:
- Sending complete payloads
- Surfacing real errors instead of hallucinating success
- Handling partial API responses gracefully
Most of the iteration was on the agent runtime, not the UI.
What's next
- Multi-step workflows (chain agents)
- Webhook triggers
- Shareable agent templates
Feedback
Would love feedback from anyone who’s:
- Tried to solve a similar problem
- Built on top of LangGraph / LangChain
- Hit the same reliability walls
Try it
