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

👉 https://www.orqisai.com