Deeper dive
Weekly eval harnesses compared draft quality against a frozen golden ticket set—no silent regressions when prompts moved.
Case studySoftware FactoryClearline Telecom
LangChain Copilot for Tier-1 Support Resolution
Grounded answers from tickets, KB, and runbooks — agents escalate with evidence packs, not guesses.
Project overview
Problem
Agents rewrote the same troubleshooting steps daily; tribal knowledge lived in Slack threads outside the ticketing system.
Solution
LangChain pipelines retrieve approved articles and prior solved tickets, compose draft replies with citations, and route uncertain cases through a structured escalation chain.
Key metrics
92%
Suggested replies accepted
<2s
KB retrieval latency
18%
Escalation reduction
100%
Answers with citations
System architecture
Vector index over curated KB; deterministic tools for billing lookups; supervisor pattern for chained checks before send. No auto-close without human approval.
Workflow
Ticket intake
Normalize payloads from Zendesk plus attachment text extraction.
Retrieve & rank
Hybrid search with recency boosts for policy changes.
Draft & cite
Structured JSON output with reasoning traces for QA sampling.
Human send
One-click approve/edit; rejected drafts feed few-shot refinement.
Results & impact
Customers saw consistent answers during outages. Coaches finally had grounded examples for training instead of screenshot archaeology.