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Wavefront is a backend for AI applications. You describe agents, workflows, data access and rules as configuration, and Wavefront runs them, stores what they need, and exposes everything over one REST API. Your own application (a web app, a back-office tool, a chat widget) calls that API. This section of the documentation is about building on Wavefront. The engine underneath, FloAI, has its own documentation in the FloAI tab; you do not need it to get started, but it is where the agent and workflow YAML formats are described in full.

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Three things to know first

Apps

An app is one running Wavefront backend with its own database, models, agents, workflows, data and users. A customer, a team or an environment usually gets its own app.

Wavefront Console

The Wavefront Console is the web UI for building and operating apps. One console can manage many apps.

The API

Everything you can do in the console is an API call. Your application uses the same API to run workflows, query data and chat.

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The building blocks

Inside an app you combine a small set of building blocks. Each one is a page in the console’s sidebar. A typical AI feature uses four or five of these together. For example, “check an uploaded document against the submitted form” is:
  1. a datasource query that loads the form,
  2. file storage holding the uploaded document,
  3. an agent that reads the document and returns structured fields,
  4. a function that compares those fields with the form and decides,
  5. a workflow that runs those steps in order and saves the result back to the datasource,
with the thresholds the function uses kept in a configuration, so they can be changed without touching the workflow.

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How the pieces talk to each other

  • The console talks to each app on your behalf. You sign in to the console once; it reaches each app with a short-lived, app-specific credential, so you do not need a separate login per app.
  • Your application talks to an app directly through its API, using an app user or a service account (see Integrating an application).
  • Long runs go through a worker queue. A workflow can run synchronously (the API call waits for the result) or asynchronously (the call returns an execution id and you poll for the result).

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What Wavefront is good at

  • Mixing AI judgement with deterministic rules. Agents read documents, images, video and text and return structured evidence; functions apply rules you can test and explain. Decisions stay auditable.
  • Changing behaviour without redeploying. Prompts, workflow steps and rule configurations are versioned data, not code.
  • One API for the whole feature. Data access, file storage, model calls, chat sessions and the audit trail sit behind the same authentication and the same response format.

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Where to go next

Concepts

A short definition of every term used in these docs.

Tour of the Wavefront Console

Where each building block lives in the UI.

Quickstart

Add a model, build an agent and a workflow, and run it, in about fifteen minutes.

FloAI

The engine and the full YAML reference for agents and workflows.