Wavefront AI

What is Wavefront AI?

Wavefront AI is an open-source middleware platform from Rootflo for integrating AI agents, workflows, frontend applications, and enterprise data pipelines. It acts as a shared connective layer so organizations can deploy different agent use cases without rebuilding authentication, data access, monitoring, and orchestration infrastructure for each application. The analyzed project overview labels the platform as under active development.

The architecture includes standardized REST and GraphQL APIs, enterprise identity integrations, agent- and data-level permissions, workflow and RAG components, ETL pipelines, and connections to databases, data warehouses, cloud storage, and enterprise applications. FloAI is listed as the library for agent building and agent-to-agent orchestration within the broader Wavefront platform.

How to Use Wavefront AI

1. Select an agent or workflow use case and the required frontend application.
2. Connect enterprise identity providers and define role-based access policies.
3. Register the databases, warehouses, cloud storage, knowledge bases, and APIs the agent may use.
4. Build agents and agent-to-agent flows with FloAI or connect another supported workflow framework.
5. Expose the application through the unified API and control panel.
6. Monitor performance, audit trails, guardrails, and evaluations through the observability layer.

Core Features

• Unified API layer: Standardizes development, deployment, and management across agent frameworks and use cases.
• Enterprise identity: Integrates with Google Auth, LDAP, Auth0, Okta, and Microsoft identity services.
• Granular permissions: Applies least-privilege access to both agents and individual data sources.
• Broad data connectivity: Connects warehouses, databases, cloud storage, and enterprise APIs.
• Model flexibility: Supports open-source, custom, and proprietary language models.
• Observability and evaluation: Tracks telemetry, performance, audit trails, and guardrail enforcement.
• No-code builder: Includes tooling for configuring agents, workflows, knowledge bases, and data connections.

Use Cases

• Audit and underwriting agents: Connect governed financial or operational data to specialist applications.
• Contact-center supervision: Support voice, conversational, and supervisor workflows.
• Business process automation: Coordinate agent actions across enterprise systems.
• RAG applications: Connect MCP-compatible sources and external knowledge bases.
• Shared enterprise AI platform: Reuse security, data, and observability services across multiple agent frontends.

Availability

The project overview describes Wavefront AI as open source and under active development. It lists FloAI under the MIT License and Wavefront under the GNU Affero General Public License version 3.

Frequently Asked Questions

Is Wavefront AI the same as FloAI?

FloAI is listed as a component library for building agents and agent-to-agent orchestration inside the wider Wavefront middleware platform.

Can access be restricted by agent and data source?

Yes. The architecture includes separate authorization layers and fine-grained role-based permissions.

Does Wavefront support private enterprise data?

Yes. The documentation lists data warehouses, databases, cloud storage, and enterprise services as supported source types.