Agents and agent frameworks detect uncertainty but lack built-in workflows to alter behavior based on confidence levels — pausing execution, escalating to humans, or deferring decisions. Uncertainty signals are generated but treated as decorative metadata rather than actionable control signals. Existing frameworks offer no standard coordination layer for routing uncertain tasks to appropriate handlers, forcing each deployment to re-implement ad hoc escalation logic.

Agent frameworks detect uncertainty but have no standard way to pause, escalate, or reroute tasks based on confidence levels — every team rebuilds fragile ad hoc escalation logic from scratch.

Engineering teams deploying multi-step AI agents in production where errors are costly (finance, healthcare ops, customer support automation, DevOps).

Teams already pay for human-in-the-loop tooling (Humanloop, Scale AI) and observability (LangSmith, Braintrust) but nothing connects uncertainty detection to actual execution control — this is the missing coordination layer between monitoring and action.

Ship as a lightweight middleware SDK (Python/TS) that wraps any LLM call or agent step, intercepts confidence signals, and routes to configurable handlers (pause, escalate to human queue, defer, retry with different model) via a policy DSL; hosted dashboard for defining routing rules and monitoring escalation flows.

Subset of the $2B+ AI observability/orchestration market; every production agent deployment needs this, estimated 50K+ teams deploying agents by end of 2025.