Architecture

Platform Overview

AI Governance Platform provides a control plane for AI systems. 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.

The architecture keeps frameworks and infrastructure at the edge. Runtime systems can call into AI Governance Control Plane, but AI Governance Control Plane is not the runtime itself.

Architectural Planes

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

Audit Plane

The Audit Plane captures workflow and node-level execution records. It stores the evidence required to understand what happened during an AI workflow and to reconstruct that workflow later.

Replay Plane

The Replay Plane reconstructs historical workflow executions from persisted audit state. Replay exists to support investigation, debugging, and governance review rather than to rerun live traffic.

Evaluation Plane

The Evaluation Plane turns workflow outputs into evaluation results. Providers such as TruLens sit here behind provider contracts. Evaluation exists to create governance evidence, not to be the platform boundary.

Job Execution Plane

The Job Execution Plane records and executes long-running governance work such as evaluation, experiment, replay, and drift analysis jobs. It owns AI Governance Control Plane job semantics such as idempotent submission, queued/running/succeeded/failed states, worker leases, retry attempts, cancellation, and immutable result references. It is not a workflow engine.

Persistence Plane

The Persistence Plane stores governed artifacts and evidence through repository contracts. Each repository type is backed by a factory class that selects the concrete implementation based on runtime configuration. SQLite is the current durable reference implementation. The architecture treats storage as replaceable.

Asset Plane

The Asset Plane catalogs versioned prompts, models, datasets, and evaluation providers. It distinguishes managed dataset/provider records from prompt/model identities observed in runtime or evaluation evidence. Dataset metadata is relational; immutable dataset bytes are stored through an S3-compatible artifact contract. Asset versions project into the ontology so experiments, evaluation runs, replay, policies, and decisions can be traced to their exact governed inputs.

History Plane

The History Plane retrieves persisted evaluations and organizes them into history views, trends, summaries, and comparisons. It provides the longitudinal view needed for governance decisions.

Governance Plane

The Governance Plane analyzes change over time. It compares evaluations, calculates drift, and produces governance-oriented artifacts from historical data. This plane interprets evidence; it does not execute deployments. Governed outcomes are represented by the GovernanceDecision domain model.

Policy Engine

The Policy Engine evaluates active, versioned policy rules against captured evidence. Rules are structured, priority-ordered, and deterministic; their matching outcome, condition trace, reason, policy version, and evidence references are persisted with the governance decision. It is a decision control plane, not an AI runtime or deployment system. See Policy Engine for the rule model and lifecycle.

Governance Ontology Plane

The Governance Ontology Plane provides a semantic graph describing relationships between governed assets. It is the authoritative business model of AI Governance Control Plane; all storage models, APIs, graph projections, UI views, and AI reasoning are derived representations.

Settings Control Plane

The Settings Control Plane is a typed configuration resource with explicit SYSTEM, ORGANIZATION, and PROJECT scopes. Effective configuration follows environment > project > organization > system > default. Each scoped update uses compare-and-set with an expected version.

End-to-End Data Flow

The core experiment governance flow is:

Experiment
  → Experiment Candidate
  → Evaluation Run
  → Evaluation Result
  → Evaluation History
  → Comparison / Drift
  → Ranking
  → Leaderboard
  → Recommendation

Replaceable Components

AI Governance Control Plane keeps provider and storage choices behind contracts:

• Evaluation providers are pluggable.
• Repository implementations are pluggable, selected via factory classes.
• SQLite is a current durable backend, not the architectural center.
• REST, CLI, SDK, or MCP adapters can sit above services and framework-neutral APIs.

Open-Core Extensions

AI Governance Control Plane remains the authoritative control plane. It can be consumed as a versioned dependency by separately released plugins that add or replace declared capabilities through stable contracts. The core never imports or conditionally detects a specific enterprise package.

The supported extension surface includes provider resolution, composition-based decorators, ordered lifecycle hooks, versioned domain events, and controlled REST route contributions. Provider replacement and route replacement are explicit; duplicate ownership fails startup.

See Extensibility for the public contracts, Python package entry-point setup, compatibility requirements, and runtime diagnostics.

REST APIs

The current REST control plane exposes:

• /health and /ready for service checks
• /api/v1/providers for provider discovery
• /api/v1/prompts, /api/v1/models, /api/v1/datasets for registry reads, observations, and dataset upload
• /api/v1/evaluations for synchronous evaluation submission and persisted result reads
• /api/v1/experiments for experiment creation, candidates, runs, candidate comparisons, and leaderboards
• /api/v1/governance for comparison, drift analysis, and report lookup
• /api/v1/jobs for async governance job submission, status lookup, listing, cancellation, retry, and result references
• /api/v1/settings for typed configuration management
• /api/v1/decisions for governance decision evaluation and retrieval
• /api/v1/policies and /api/v1/policy-schema for policy authoring, lifecycle, and simulation

Non-Goals

The architecture intentionally does not make AI Governance Control Plane responsible for:

• Prompt deployment
• Model deployment
• Prompt authoring and model serving
• Dataset labeling and data-engineering workflows
• Runtime promotion
• Infrastructure management
• Web-server ownership
• CI/CD orchestration

Those systems can consume AI Governance Control Plane recommendations, but they remain outside the platform boundary.