Mar 12, 2026
Agentic AI workflows for back-office triage and escalation
A back-office team handling inbound case load needed autonomous AI agents to read, classify and route work — with humans owning the edge cases.
Cases triaged before human touch
↑↑
Duration
10 weeks
Team
3 people
Challenge
Volume grew faster than headcount could match, but the operating leaders rejected fully automated decisions on anything customer-facing.
Solution
We designed an agentic workflow: an LLM-powered classifier reads each case, an autonomous agent drafts the action, a routing layer hands it to a human only when confidence drops below the agreed threshold. Human ownership stays on every edge case and every escalation path is logged.
Outcome
The team absorbed materially higher volume on the same headcount, the escalation log gave leadership a real-time view of where the agent struggles, and the model became a calibration target instead of a black box.
What was happening
Inbound volume kept rising while the operating team stayed flat. Classification and first-pass routing was eating 60-70% of the analyst day and leaving little time for the actual judgment calls.
Earlier automation attempts had failed because they tried to remove humans from edge cases — exactly the calls the team needed to keep owning.
What changed
We separated the work into three roles. An LLM-powered classifier reads every inbound case. An autonomous agent drafts the action and pulls supporting context. A routing layer hands the case to a human only when the agent's confidence drops below the threshold the team agreed on.
The threshold is a tunable, not a constant. The escalation log shows leadership where the agent struggles, and the team adjusts confidence rules from operating evidence, not from a vendor playbook.
- LLM classifier as the first-pass reader, not the decision-maker
- Autonomous agent drafts the action; humans approve edge cases
- Confidence threshold tuned from a logged escalation history
AI Transformation, Process Optimization & Cost Efficiency