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Method Demonstration — Fictional Company

HelioDesk: Support Intelligence System

How the EIA framework mapped a support workflow, designed bounded agents with explicit governance, and projected measurable margin improvement.

Executive Summary

HelioDesk (fictional) is a mid-market B2B SaaS company facing rising support volume and increasing cost-to-serve. Leadership needed a safe, measurable path to embed intelligence into support workflows without degrading quality or creating ungoverned automation.

The Problem

  • ■ Time-to-first-response drifted upward as ticket volume increased
  • ■ Routing accuracy was inconsistent, creating rework loops and extra touches
  • ■ Escalation decisions created noise for Engineering and frustration for Support
  • ■ Leadership refused ungoverned automation, especially for customer commitments

Approach — Pillar by Pillar

Pillar I

Workflow Architecture

Mapped ticket flow from intake to resolution. Identified decision density hotspots in triage and escalation. Quantified latency drivers: context gathering and handoff friction. Identified rework loops caused by misrouting and incomplete evidence bundles.

HelioDesk Ticket Workflow Ticket Created Triage M Context Gather L Draft L/M Resolution Escalation
Pillar II

Agent & Tooling Design

Designed four bounded agents embedded inside the ticketing workflow with explicit human gates.

Context Agent
Read-only evidence bundle
Drafting Agent
Drafts — never sends alone
Triage Agent
Routing — human confirms
Evaluation Agent
Quality scoring + drift
HelioDesk Agent Architecture SOURCE SYSTEMS Ticketing System Knowledge Base Product Telemetry BOUNDED AGENTS MEDIUM Triage Agent Routing recommendations LOW Context Agent Read-only evidence bundle LOW/MED Drafting Agent Never sends without approval EVAL Evaluation Quality + drift flags feedback loop Human Gate REQUIRED Audit Logs + Metrics

Boundary rule: No agent makes SLAs, refunds, or product promises without explicit human approval.

Pillar III

Governance & Risk Control

Applied risk tiers (L/M/H) with matching control gates. Retained audit logs for customer-facing drafts and routing recommendations. Defined monitoring signals: acceptance rate, misrouting rate, escalation quality, cost. Defined incident protocol: detect → triage → rollback → remediate. Target governance maturity: Level 3–4 (audit trails + drift detection).

Pillar IV

KPI & Value Measurement

Time-to-first-response
6.2h → 2.5h
Draft acceptance rate
62% → 80%
Misrouting rate
18% → 8%

Kill criteria: If draft acceptance fails to exceed 70% after six weeks, restrict to context gathering until quality stabilizes.

Sequenced Roadmap

Phase 1 0–6 weeks

Context + drafting (approve-to-send) → Audit logs + policy filters

Phase 2 6–12 weeks

Routing recommendations → Human confirm + variance review

Phase 3 12–20 weeks

Escalation recs + evidence bundle → Tier 2 approve + audit trail

Ongoing

Quarterly calibration + governance step-up → Incident drills + change policy

What Was Delivered

  • Workflow Systems Map + Decision Node Inventory
  • Agent Architecture Blueprint + Authority/Escalation Matrix
  • Governance & Risk Control Framework + Monitoring Plan
  • KPI Alignment + Economic Impact Model (ROI + kill criteria)
  • 6–12 month sequenced roadmap with governance gates

Architecture, governance, and measurement were delivered. Implementation remained the client's responsibility.

Fictional case study. For method demonstration only.

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