DEPLOY-5 - AGENTIC AI GOVERNANCE FRAMEWORK

Agentic AI systems—those that autonomously plan, execute multi-step actions, invoke external tools,and operate with delegated authority—present governance challenges that existing frameworks do notadequately address. Unlike assistive AI that responds to individual queries, agentic systems createaction chains with real-world consequences: modifying files, executing code, sending communications,and committing resources without per-action human approval. Current governance approaches often target different risk profiles or evaluation layers.Benchmark-based evaluation reports average performance but misses tail-risk failures. Ethics reviewsaddress intent but not operational failures. Security audits check perimeter controls but not emergentbehaviors from tool composition. Accountability structures assume human decision-makers at eachstep. The result: systems that pass compliance requirements but create unmonitored paths toorganizational harm. Three specific gaps distinguish agentic risk from traditional AI risk: Composition effects: Individual tools may be safe, but combinations create emergent capabilities.File read + web search + shell execution can become a data exfiltration path that no single-toolanalysis would catch. Causal opacity: When incidents occur, reconstructing what happened is often impossible. Agentframeworks log outcomes but not the tool parameters, state transitions, or authorization contextneeded to understand failures. Aggregate risk: Many organizations deploying similar systems against shared infrastructurecreates correlated failure risk. A vulnerability in a dominant model provider affects thousands ofdeployments simultaneously.