Agent Context Engineering Governance
Agent context engineering governance is the set of runtime controls that determine what content, including retrieved data, tool outputs, and inter-agent messages, is allowed into an AI agent's active context window, and how that content is validated, scoped, and logged before it influences the agent's decisions or actions.
Components of an Agent Context Pipeline
An agent's context window is assembled from several distinct sources, each carrying a different level of trust. Understanding these layers is the starting point for governing them.
- 1
System Instructions
Baseline behavioral instructions and role definitions set by the application, typically the most trusted layer of context.
- 2
Retrieved Content (RAG)
Documents or data pulled from internal or external sources at query time, often treated as trusted by the model despite originating outside the application boundary.
- 3
Tool and Function Outputs
Results returned from tool calls or API invocations, which can contain unvalidated content if the underlying data source is compromised.
- 4
Conversational and Persistent Memory
Short-term session state and longer-term stored memory that can carry forward earlier context, including anything not filtered at ingestion.
- 5
Inter-Agent Messages
Output from one agent that becomes input context for another agent in multi-agent workflows, propagating errors or injected content without human review.
- 6
Protocol-Exposed Resources and Tools
Context and capabilities exposed to a model through standards like the Model Context Protocol, where authorization and validation logic is left to