Agentic AI
Operations OS
A single pipeline for a business whose support requests were scattered across channels, answered inconsistently, and escalated too slowly — replacing manual triage with a governed system that only lets the model touch the parts of the job that actually need judgment.
THE PROBLEM
Fragmented channels, slow escalation, no consistency.
Communication was fragmented across email, SMS, and chat. Manual checks were repeated on every request, escalation was slow and inconsistent, and follow-up depended on whoever happened to see the message. The fix: one operations layer connecting the intake channel, the system of record, the knowledge base, and the task queue — with the model handling only classification and drafting, and deterministic code handling everything else.
WHAT CHANGED
From manual triage to a governed pipeline.
| Scattered channels | One intake pipeline normalizes email, SMS, and chat into a single case format. |
| No live record check | Deterministic identity match against the system of record — never a guess. |
| Inconsistent answers | Every drafted response is grounded in keyword-based knowledge-base retrieval, not the model's memory. |
| Slow, inconsistent escalation | A case that needs a human gets a task the moment it's flagged — not whenever someone notices. |
| Risk of the model overreaching | High-risk actions — cancellations, refunds — always route to a human approval queue. The model drafts; it never executes. |
| No record of what happened | A full audit log, one line per pipeline step, for every case. |
| No way to know if quality holds | A checked-in evaluation suite catches regressions before they reach a real conversation. |
HOW IT'S BUILT
The model does two things. Code does the rest.
Two model calls, both schema-enforced with retry: one classifies the incoming request, one drafts a response. Everything else — permissions, record updates, workflow state, retries, the approval gate — is deterministic code with no model in the loop. That split is the entire point: the parts of the job that need judgment get judgment, and the parts that need to be reliable every single time don't depend on a model getting lucky.
Want the walkthrough?
Happy to run this end-to-end live and talk through what'd change for your actual workflow.