Runtime Supervision

Runtime supervision refers to the monitoring and control systems that observe AI models and applications during active operation to detect and respond to failures, anomalies, or policy violations in real-time. This capability is essential for enterprise governance because it enables organizations to intervene quickly when models produce harmful outputs, drift from expected behavior, or violate compliance requirements without requiring complete system shutdown. Runtime supervision bridges the gap between pre-deployment testing and production safety, making it a critical component of responsible AI deployment in high-stakes business environments.

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ResearchGlobal2026-09-16

Indirect Prompt Injection via Tool Outputs Is Now the Core Agentic Control Gap

Implement Agentic Learning has published a practitioner governance guide for agentic AI systems. It identifies indirect prompt injection through tool outputs as the primary agent-specific threat and frames the absence of scoped agent identities as the enabling condition. The guide recommends structured outputs, per-boundary guardrails, capability-scoped tokens, and runtime guardian supervision as baseline enterprise controls.

agentic AI prompt injection tool boundary controls agent identity runtime supervision capability-scoped tokens indirect prompt injection