Architect Email-to-Case triage agents for high-volume support queues

Real World Scenario

A SaaS vendor receives 15,000 support emails daily across product lines. Tier-0 agents must classify intent, extract account identifiers from messy threads, and route to product-specific queues without creating duplicate cases.

Expected Answer

• Parse email threads deterministically—strip signatures, quoted replies, and HTML before LLM classification • Map extracted entities (org ID, case number, product SKU) to structured action inputs with validation rules • Define confidence thresholds: auto-route above 85%, human review queue for ambiguous or multi-intent emails • Deduplicate using Message-ID, In-Reply-To headers, and existing open case correlation logic • Apply Einstein Trust Layer masking on email bodies before external LLM processing where required • Log classification rationale and extracted fields for audit when customers dispute misrouting • Measure auto-triage accuracy, duplicate case rate, and mean time to first human response separately from chat

Follow-Up Questions & Answers

Click to expand — each follow-up includes a direct, interview-ready answer

Handle it in layers. First: Parse email threads deterministically—strip signatures, quoted replies, and HTML before LLM classification. If that fails: Map extracted entities (org ID, case number, product SKU) to structured action inputs with validation rules. For edge cases in "How do you handle forwarded emails with multiple u…", Define confidence thresholds: auto-route above 85%, human review queue for ambiguous or multi-intent emails. Email agents differ fundamentally from chat—latency tolerance is higher but context noise is worse. Document the decision in an ADR and align with enterprise standards.

Architect Perspective

Email agents differ fundamentally from chat—latency tolerance is higher but context noise is worse. Invest in pre-LLM parsing pipelines; never send raw MIME to the model.