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To prevent AI agents from executing unsafe tool calls, you can use a PreToolUse safety hook that intercepts actions and applies deterministic risk classification plus intent-drift detection to block unsafe execution.

Uncertainty-aware consensus for legal citation verification works by routing documents through a 5-stage cascade—fast gate, consensus gate, verifier gate, critique, and self-consistency—to decide whether to accept, abstain, or escalate answers based on calibrated confidence thresholds.

Yes, you can enforce policy-as-code governance by routing autonomous tool execution through a policy gate that validates actions and records auditable decisions to a ledger for compliance and observability.

Yes, monitoring agent session stability requires configured MCP endpoints and worker URLs for consensus, RAG, law, and agent-control, which provide the telemetry needed to track Lyapunov V(t) stability and entropy.

Human-on-the-loop escalation in AI agent governance is a safety mechanism where the system abstains from low-confidence autonomous actions, exposing live telemetry like dissensus and drift to prompt human review before risky tools execute.

You should not use automated multi-oracle consensus without authorization secrets configured, as the safety pipeline requires valid endpoints for agent-control and consensus to properly execute its three-phase safety checks.