human-in-the-loop

Human-in-the-loop observability tracks approval requests by emitting structured span attributes and timing metrics for AI agent workflows. This captures wait times, outcomes, and routing decisions to improve auditability and safety across synchronous and asynchronous operations.

Capturing human escalation events requires emitting structured span attributes at decision points within agent workflows. This skill instruments escalation handling and auto-override logic, logging outcomes and timing metrics for post-mortem analysis.

Yes, this instrumentation provides framework integrations for LangGraph, CrewAI, and similar toolchains. It emits structured spans for approval, clarification, and escalation events to achieve end-to-end observability across these agent frameworks.

Instrument your agent to emit human-in-the-loop spans at approval, clarification, and escalation points. The emitted span metrics capture SLA-aware routing and workflow decisions, which then drive downstream actions within your tool calls.

Yes, you can log clarification requests and feedback by emitting structured span attributes at agent decision points. This captures human input and outcomes, providing audit trails and timing metrics for synchronous and asynchronous feedback loops.

Observability for human-in-the-loop patterns is needed to improve auditability and safety in agent workflows. It provides structured capture of approval, clarification, and escalation decisions, enabling SLA-aware routing and post-mortem analysis through timing metrics.