Overview

It records governed AI assets, evaluates AI configurations, persists execution evidence, reconstructs history, analyzes quality change, ranks experiment candidates, and produces recommendations. It does not own prompt deployment, model deployment, infrastructure orchestration, or CI/CD execution.

AI Governance Platform sits between the systems that run AI work and the organization that must account for it. It records what happened, evaluates it against explicit policy, produces a deterministic decision with supporting evidence, and stores an immutable trail for replay and investigation.

Latest Release

The current release expands the control plane from recording runtime observations to managing governed evaluation inputs and execution evidence. It adds:

• Managed, immutable prompt and model versions alongside observed runtime assets.
• Managed dataset lifecycle controls and consistent-dataset candidate comparisons.
• Tenant-scoped Runtime Connections and validated provider/model capability profiles.
• Candidate execution evidence, per-item evaluation results, cancellation, and progress visibility.
• A first-class Policy Engine with version lifecycle, simulation, condition traces, and explainable decisions.

Start with Asset Catalogs, the Evaluation Engine, and the Policy Engine for the detailed contracts and operational boundaries.

Architectural Planes

The system is organized into independent planes. Each plane owns a single responsibility and communicates only through explicit contracts:

• Audit Plane — captures workflow and node-level execution records for reconstruction and investigation.
• Replay Plane — reconstructs historical executions from persisted audit state.
• Evaluation Plane — turns workflow outputs into evaluation results using pluggable providers.
• Job Execution Plane — records and executes long-running governance work with durable lifecycle tracking.
• Persistence Plane — stores governed artifacts through replaceable repository contracts.
• Asset Plane — catalogs versioned prompts, models, datasets, and evaluation providers with explicit ownership and ontology-backed lineage.
• History Plane — retrieves and organizes evaluations into history views, trends, and comparisons.
• Governance Plane — analyzes change over time, calculates drift, and produces governance-oriented artifacts.
• Policy Engine — evaluates versioned, structured rules against captured evidence and persists explainable governance outcomes.
• Governance Ontology Plane — semantic graph describing relationships between governed assets.
• Settings Control Plane — typed configuration with SYSTEM, ORGANIZATION and PROJECT scopes.

See the architecture overview for a full breakdown of each plane and how they interact.

Policy Engine

The Policy Engine turns evidence into repeatable governance decisions. Policies are versioned and target governed entities such as experiment candidates, prompt versions, and model versions. Their rules have explicit priorities, conditions, effects, and reasons; the resulting decision preserves the matching rule, condition trace, evidence, explanation, and audit context.

Read the Policy Engine guide to understand rule authoring, simulation, lifecycle, outcome precedence, and decision review.

Interfaces

Every governance operation is available over a versioned REST API and as an auditable MCP tool. The MCP server exposes governed read and controlled-write operations to agents, while the REST APIs serve consoles, CLIs and service-to-service integrations.

Extensibility

AI Governance Platform exposes a versioned extension surface for independently released integrations. Plugins can add routes, providers, lifecycle hooks, and event subscribers through declared contracts; they do not modify AI Governance Platform source or depend on internal services.

This keeps the OSS control plane authoritative while allowing future commercial or organization-specific packages to evolve independently. Read the Extensibility guide for plugin lifecycle, compatibility, and operational rules.

Where to start

• Begin with Getting Started.
• Want the mental model? Read the Architecture overview.
• Building an integration? Start with the Extensibility guide.
• Ready to run something? Try the governed replay walkthrough for a complete API-driven product path, or the observed asset ingestion tutorial.