Agent activity, source quality, customer intent, and human review states were difficult to evaluate together.
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Case study 01 / AI agent platform
Scope brings AI agent operations into one inspectable command center.
A management system for AI agents, search workflows, source evidence, and operational knowledge, designed for teams that need visibility without slowing review work.
Overview
An operating layer for teams managing AI agents, evidence, and review workflows at scale.
AI operations leads, knowledge teams, support managers, and domain reviewers.
Product strategy, information architecture, workflow design, and visual system.
A command center that made agent behavior, evidence, status, and handoffs easier to inspect.
Challenge
AI output had to feel auditable without slowing operators down.
The core UX challenge was reducing ambiguity. Operators needed to understand what an agent did, which sources shaped the answer, where confidence was low, and when human judgment was required.
I treated the interface as an operational cockpit: dense enough for advanced work, but organized around repeatable patterns so status, evidence, and next actions stayed predictable.
A dense dashboard that still works as a command center.
The main view brings transfer reasons, RMA signals, issue distribution, and agent outcomes into one operating surface so teams can spot patterns before opening a detailed review.
Flow in motion
Interaction passes showing how operators move from signal to evidence without losing context.
Dashboard interaction
Keep operational signals close to the evidence.
The flow shows how status, charts, and issue evidence stay connected so operators can investigate without losing the dashboard context.
Process
From agent lifecycle mapping to a scalable review model.
01 / Research
Mapped agent states, failed searches, reviewer needs, and high-risk handoff moments.
02 / IA
Grouped agents, queries, sources, confidence, and review actions into a clear hierarchy.
03 / Flows
Designed paths for search review, escalation, evidence inspection, and agent monitoring.
04 / System
Created reusable patterns for status, confidence, evidence, and activity history.
05 / Prototype
Tested progressive disclosure and hover states for dense information without visual noise.
Confidence as interface language
Used confidence, source quality, and review state as first-class UI signals instead of hidden metadata.
Evidence before approval
Placed source trails next to decisions so operators could validate results before approving or routing them.
Escalation as a product state
Designed clear ownership and handoff states for moments where automation needed human judgment.
Final experience
Key surfaces that show how AI operations become reviewable product workflows.
01 / Issue intelligence
Make exceptions visible before they become escalations.
RMA trends, issue summaries, and escalation evidence sit beside the dashboard so teams can understand where automation needs human support.
02 / Evidence review
Review the original conversation without leaving the operating view.
A focused conversation preview lets operators validate the customer context before changing status, routing ownership, or approving the next action.
03 / Conversation signals
Turn conversation quality into readable signals.
Expression scores, sentiment breakdowns, and service markers help reviewers see patterns across user and assistant turns without reading every message first.
04 / Agent workspace
Give operators controls without breaking their flow.
The chat workspace keeps response shortcuts, source options, prompt settings, and conversation history visible enough for fast decisions and controlled handoffs.
Impact
A search and agent layer designed to reduce support friction and improve product discovery.
Reduced reliance on human operators by giving customers a more useful AI layer before a conversation needed escalation.
Turned customer questions, failed searches, handoff moments, and assistant outcomes into a clearer learning loop for product and operations teams.
Helped shoppers navigate large James Allen and Blue Nile catalogs by translating intent, budget, and uncertainty into more precise product discovery.
Instead of routing every abandoned bot conversation back to a person, the system gave customers a stronger self-service path before human review was needed.
Searches, gaps, successes, and friction points became inspectable signals that teams could use to improve prompts, content, matching logic, and service flows.
The OpenAI-powered engine translated natural customer input into precise API queries, helping shoppers find stronger jewelry options across large inventories.
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