Email Automation Agent

Problem

Email teams handle 200-500 messages daily with repetitive follow-ups and inbox spikes around product launches and incidents. Average response time stretches to 6-12 hours, while customers expect replies within 1 hour.

Solution

Build an agent that classifies inbound intent, generates policy-compliant reply drafts, applies confidence-based sending rules, and routes complex threads to the appropriate human owner.

Implementation Steps

1. Classify incoming threads

   Route each email by intent (support, billing, sales, feedback), urgency level, and required owner using an LLM classifier.

   Tip: Define a measurable success metric and review weekly to improve quality and cost.

   # Email intent classification
   result = await classify_email({
       subject: email.subject,
       body: email.body[:2000],
       sender_history: get_sender_context(email.from)
   })
   # Returns: {intent, urgency, suggested_owner, confidence}

2. Build response template library

   Create approved response templates per intent category with variable placeholders for personalization. Include tone guidelines per urgency level.

   Tip: Maintain separate templates for first response vs follow-up. First responses need more context; follow-ups should be concise.

3. Generate draft responses

   Use the classified intent and matching template to generate a personalized response draft with relevant context from the thread history.

4. Apply confidence-based sending rules

   Auto-send when classification confidence exceeds 0.85 and the response matches a well-tested template. Queue all others for human review.

5. Track outcomes and optimize

   Measure response time improvement, customer satisfaction, escalation rate, and auto-send accuracy. Tune confidence thresholds monthly.

Recommended combos

FAQs