chapter nine

9 Governance and operations

 

This chapter covers

  • Governance as a harness for context engineering.
  • Human-in-the-loop (HITL) as a design pattern for context-aware systems.
  • Operational disciplines for context engineering, including LLMOps and ContextOps.
  • Challenges of deploying context-aware systems.

Chapter 1 introduced the concept of a harness that surrounds the core context engineering stack. As explained in Chapter 8, this harness includes evaluation and observability components that ensure the system behaves as intended and provides visibility into its internal processes. This chapter completes the picture by analyzing the governance and operational components. These layers are not specific to context engineering, but they are essential for any production-grade AI system. They provide the organizational structures, policies, and processes that ensure AI systems are developed and operated responsibly. Governance and operations are especially critical for context-aware systems because they directly influence how context is managed and assessed.

9.1 Governance

9.1.1 Privacy

9.1.2 Safety and security

9.1.3 Accountability

9.1.4 Transparency

9.1.5 Fairness

9.1.6 Human oversight

9.2 Human-in-the-loop

9.3 Operations

9.3.1 Adoption models

9.3.2 DevOps for AI systems

9.3.3 LLMOps

9.3.4 PromptOps

9.3.5 RAGOps

9.3.6 AgentOps

9.3.7 Context as code

9.3.8 Deployment

9.4 Hands-on

9.4.1 Automated PII redaction with Microsoft Presidio

9.4.2 Output validation with JSON Schema

9.4.3 Bias detection for fairness

9.4.4 Basic HITL interaction pattern

9.4.5 Model fine-tuning

9.4.6 AI gateway with LiteLLM

9.4.7 Context as code with versioned prompt templates