This whitepaper defines GenOps as the runtime control layer for production AI systems — covering authority control, governance as runtime evidence, and the operational architecture that separates governance from observability.
For code implementation, see the GenOps OpenTelemetry Extension: GenOps Repository
GenOps (Generative Operations) defines the governance and control layer that operates alongside observability for production AI systems. Rather than extending observability into AI, GenOps establishes a distinct governance plane — responsible for authority control, runtime policy enforcement, and producing governance evidence as a byproduct of production operations.
As AI systems become increasingly critical to business operations, the need to distinguish between observing system behavior and governing it becomes essential. GenOps addresses this by treating governance as a runtime concern: authority flows, compliance evidence, and operational controls are embedded in the production path, not bolted on after the fact.
The GenOps extension implements the runtime control plane concepts described in this whitepaper as an OpenTelemetry extension, providing real-world tooling for AI workload governance and observability. While this whitepaper explores the architectural patterns and theoretical frameworks, the GenOps extension offers the concrete implementation for immediate adoption in production environments.
This whitepaper covers:
- Authority Control - Runtime authority flows and delegation models for AI workloads
- Governance as Runtime Evidence - Compliance and audit artifacts produced as a byproduct of production operations
- Governance Plane vs Observability Plane - Separating what governs from what observes in AI system architecture
- Runtime Control Architecture - Design patterns for policy enforcement and operational control at the production boundary
- Scalability Considerations - Approaches for managing AI workloads at enterprise scale
- DevOps and Platform Engineers
- AI/ML Engineers and Data Scientists
- System Architects and Technical Leaders
- Engineering Managers overseeing AI initiatives
- Version: v0.2
- Status: Active development — refining runtime control and governance models with community feedback
- License: Apache 2.0
genops-whitepaper/
├── README.md # This introduction
├── LICENSE # Apache 2.0 license
└── paper/ # Whitepaper files
├── GenOps_Runtime-Control-for-Production-AI_v.0.2.pdf (current)
└── GenOps_Runtime_Control_Planes_v0.1.pdf (archive)
- v0.1 — Runtime Control Planes — Initial release outlining the operational foundation and control plane architecture for GenOps.
This whitepaper is open for community input, discussions, and collaboration. Future versions will incorporate feedback and expand on the foundational concepts presented here.