I helped design a few long-running flows where DMN rules decide routing but humans had to approve certain steps. The pattern that worked best for me was: DMN for deterministic routing, an AI agent that monitors state and pre-fills context for the human reviewer (using RAG to surface related docs), and an escalation agent that auto-escalates after a configurable timeout while logging decisions.
Practically, we selected smaller models for lightweight checks and a stronger model for complex summarization. We also added visual checkpoints and a restart-from-step feature so failed executions could resume without redoing earlier work. Training a handful of power users to manage the escalation thresholds avoided surprise escalations in production.
How have others balanced automated escalation vs human fatigue in long-running approvals?
i built a similar flow where an ai team monitored long tasks and routed approvals based on dmn outputs. the ai prefilled reviewer notes with RAG, escalated after a timeout, and logged everything for audits.
we used Latenode to orchestrate the agents and the dmn rules. the visual builder made it easy to add human steps and set restart points. that setup cut approval time and kept humans in control.
we used a two-tier timeout: first nudge to the reviewer, then escalate. the AI draft included summary, relevant doc snippets, and suggested decision rows. reviewers accepted most suggestions, which cut hands-on time.
In my deployments, the hardest part was balancing speed and noise. We implemented a confidence threshold on AI-suggested approvals: below threshold the task routed to a human immediately; above it the AI suggested the approval with supporting evidence. We logged every data point used in the decision and ran periodic audits to retrain prompts and adjust thresholds. To avoid reviewer overload, we batched similar low-risk approvals into a single review task and allowed bulk accept/decline actions with a required spot-check cadence. That mix preserved safety while dramatically reducing manual cycles.
My team standardized escalation policies: clear SLA windows, escalation paths, and an auditable log of AI suggestions. We kept human-in-the-loop for exceptions and high-impact decisions. Using model confidence and RAG-sourced evidence helped reviewers act faster without losing oversight.