Harness
Private AI: governed agent work inside your environment.
Harness Framework
Next-Generation AI Infrastructure
The Harness transforms how businesses use AI by bringing intelligence directly to your data without moving sensitive information outside your control. It addresses the critical decision point for traditional enterprises: how to harness artificial intelligence without compromising data security or requiring massive technical transformation.
Private Data Governance: Data never leaves the premises and access is human-governed with granular controls.
Private AI Inference: Leverage Interval's on-premise, open source inference for highly privacy-strict workflows tailored for your business and cost-optimized.
Swappable Architecture: Harness components are configurable so agent boundaries can shrink/expand as needed and allow you to bring your own models/providers or use local private AI inference.
Developer Enablement: Developers can quickly ship integrated data applications with strict controls using the Workbench.
Harness Workbench
The Workbench is the interface and tooling ecosystem that joins governed data access, private AI models, explicit human control, and an auditable proof layer.
This allows users to build applications directly embedding enriched data sources, gated by the same RBAC policies that govern Harvest data access across an organization.
Delivery Surfaces
The Workbench integrates with the Portal or any modern developer tool like Cursor or Pi-dev. A developer or even non-technical user can quickly build software using inherited user permissions and retain full control over their workflows.
Control Graph
At its core, the Workbench defines a graph that allows for granular, configurable control flow per task. It allows the user to define when human intervention is a gate at any point in the build process. It also determines the coordination of specific, agentic tasks like adversarial review, fan-outs, and loops.
Each node in the graph is an individual stage-gate. Some nodes are deterministic, others are agent tasks.
Agent Loadouts
For agent tasks, nodes are pre-determined loadouts that employ an adapter architecture. Each node runs in an ephemeral runtime with declared resource limits.
A swappable model is paired with governed context, permissions, tool use, and token budgets. This allows for the distinction between an early planning cycle using a powerful frontier model that doesn't have direct access to private data versus an execution cycle using a low-cost, local model that can access private data and make code changes.
Proof Layer
Any and all actions performed by an agent or human are captured by audit logs that enable a proof layer - answering questions like:
Who approved this workflow?
What data was the AI granted access to?
Where did some specific metric come from and what was the data lineage?
The Workbench integrates directly with Interval's core permissioning and audit primitives.
Last updated