Introduction
Functional AI is a runtime for building agents as Agentic Functions. Your prompt, agent, or multi-agent workflow becomes an Agentic Function you can define, test, version, and deploy like any other code: typed inputs, typed outputs, and a number on the door. We host it, keep it running, and you just call it.
The mental model
You write an fn.yml: a single declarative file that describes an Agentic
Function's inputs, outputs, model, and how it should be evaluated. Functional AI
compiles that into an immutable, versioned Agentic Function, and every
promoted version is a stable HTTP endpoint.
| Concept | What it is |
|---|---|
| Definition | A declarative fn.yml defining I/O, model, and evals |
| Version | An immutable, content-addressed snapshot of a definition |
| Agentic Function | A compiled, callable version — classify@v3 |
| Eval | A dataset + threshold that turns a change into pass/fail |
| Alias | A moving pointer like stable or canary routed at a version |
Why an Agentic Function
- Versioned by default. Every change produces a new immutable version you can diff and roll back.
- Tested before shipping. Evaluation is a command and a CI gate, not a vibe check.
- Callable over HTTP. Promote a version, get an endpoint that returns typed structured output plus metadata.
- Boring on purpose. The same workflow works for every Agentic Function you build.
Ready to build one? Head to the Quickstart.