Knowledge
Curated sources, metadata, access controls, freshness checks, and retrieval evaluation.
Applied AI / Customer support
A grounded AI resolution agent with clear human escalation
This illustrative AI scenario combines access-aware retrieval with bounded help desk tools. It is designed to assist support specialists first and expand autonomy only when evaluation justifies it.
Illustrative solution scenario. Outcomes below are modeled targets, not claimed client results.
01 / The challenge
The operational problem behind the brief.
Support specialists search across product documentation, resolved tickets, and internal notes before they can respond. Answers vary by product version and customer entitlement, while experienced staff repeatedly handle similar diagnostic requests.
The organization wants faster resolution without allowing an AI system to invent policy, expose restricted content, or issue consequential account changes without approval.
Non-negotiable constraints
- Knowledge articles have mixed quality and ownership
- Retrieval must respect customer and role permissions
- Account actions require explicit approval
- Quality must be evaluated by product area and request type
02 / The solution
A system designed around exceptions and control.
A retrieval pipeline curates approved content, attaches product and permission metadata, and measures whether the correct sources are found. The agent drafts answers with citations and uses diagnostic tools only within a defined support workflow.
A specialist interface shows retrieved evidence, proposed steps, and escalation reasons. Offline evaluation covers representative requests, missing knowledge, conflicting documents, prompt injection, and unauthorized action attempts before any customer-facing release.
Agent orchestration
Bounded planning, structured responses, approved tools, and explicit escalation policies.
Specialist workspace
Citations, conversation context, action approval, editing, and feedback capture.
Quality operations
Task-level test sets, traces, safety checks, cost monitoring, and sampled human review.
REFERENCE STACK
PythonOpenAILangGraphFastAPIPostgreSQLpgvectorZendesk APIAzure AI
03 / Projected outcome
Objectives that can be tested in a pilot.
These values define how the illustrative solution could be evaluated. A production baseline and observation period would be required to verify realized outcomes.
Target faster handling
A modeled pilot objective for eligible request categories with adequate knowledge coverage.
Required citation accuracy
A release-gate example to validate on representative tasks before production use.
Unapproved account actions
Consequential tools remain behind explicit specialist authorization.
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