AI detects escalation signals — negative sentiment, repeated contacts, VIP status — and routes to the right person with full context, automatically.
Escalations get lost in normal support queues, causing VIP customers and urgent issues to receive the same slow response as routine inquiries. By the time a manager notices a frustrated customer has contacted support three times, the relationship is already damaged. Manual escalation processes depend on individual agents recognizing patterns they often miss under workload pressure.
MultiMail's AI agent monitors incoming emails for escalation signals like negative sentiment, repeated contacts, and VIP flags. When an escalation is detected, the agent compiles a context summary from the full thread history and routes it directly to senior support or management. Monitored oversight keeps the team informed while ensuring zero delay on critical issues.
Your AI agent uses check_inbox and read_email to process new messages in real time. Each email is analyzed for escalation signals including negative sentiment, urgent language, and threat-to-churn indicators.
The agent uses search_contacts to look up the sender's history — how many times they've contacted support recently, their account tier, and any open issues. Repeated contacts within a short window trigger escalation.
Using get_thread, the agent pulls the complete conversation history so the escalation summary includes all prior interactions, not just the latest message.
The AI compiles a structured summary: customer details, issue timeline, sentiment trajectory, and recommended action. This gives the senior responder everything they need without reading dozens of emails.
The agent forwards the email with the context summary to the appropriate manager or senior agent using send_email, then sends an acknowledgment to the customer confirming their issue has been escalated.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
"cm"># Check inbox for new messages
inbox = requests.get(
f"{API}/mailboxes/support-mailbox-id/inbox",
headers=HEADERS,
params={"status": "unread", "limit": 20}
).json()
for msg in inbox["emails"]:
email = requests.get(
f"{API}/emails/{msg[&"cm">#039;id']}", headers=HEADERS
).json()
# Check contact history for repeat contacts
contacts = requests.get(
f"{API}/contacts/search",
headers=HEADERS,
params={"email": email["from"]}
).json()
recent_contacts = contacts.get("recent_tickets", 0)
if recent_contacts >= 3 or detect_negative_sentiment(email["text_body"]):
escalate(email, contacts)Check incoming emails for escalation triggers like repeated contacts and negative sentiment.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def escalate(email: dict, contact: dict):
"cm"># Get full conversation thread
thread = requests.get(
f"{API}/threads/{email[&"cm">#039;thread_id']}",
headers=HEADERS
).json()
summary = generate_escalation_summary(thread, contact)
# Forward to senior support with context
requests.post(
f"{API}/send",
headers=HEADERS,
json={
"from": "[email protected]",
"to": "[email protected]",
"subject": f"[ESCALATED] {contact[&"cm">#039;name']} - {len(thread['messages'])} contacts",
"text_body": summary,
"html_body": f"<h2>Escalation Summary</h2><p>{summary}</p>"
}
)
# Tag the original email
requests.post(
f"{API}/emails/{email[&"cm">#039;id']}/tags",
headers=HEADERS,
json={"tags": ["escalated", "high-priority"]}
)Compile the full thread history into a summary and route to a senior support agent.
"cm">// Using MultiMail MCP tools for escalation routing
"cm">// 1. Check for new support emails
const inbox = await mcp.check_inbox({
mailbox_id: "support-mailbox-id",
status: "unread",
limit: 20
});
for (const msg of inbox.emails) {
"cm">// 2. Read the full email
const email = await mcp.read_email({ email_id: msg.id });
"cm">// 3. Check contact history
const contacts = await mcp.search_contacts({
query: email.from
});
"cm">// 4. Get thread for context
const thread = await mcp.get_thread({ thread_id: email.thread_id });
if (contacts.recent_tickets >= 3) {
"cm">// 5. Tag and escalate
await mcp.tag_email({
email_id: email.id,
tags: ["escalated", "high-priority"]
});
await mcp.send_email({
to: "[email protected]",
subject: `[ESCALATED] ${contacts.name} - ${thread.messages.length} contacts`,
text_body: buildEscalationSummary(thread, contacts)
});
}
}Use MultiMail MCP tools to detect escalations and route them in an AI agent workflow.
AI monitors every incoming email in real time. Critical issues are routed to senior staff within seconds, not hours or days.
Senior staff receive a compiled summary of the entire conversation history, customer profile, and sentiment trajectory — no time wasted reading through old threads.
Unlike manual triage, AI can track patterns across multiple threads and time periods — catching the customer who has contacted three times in a week even if different agents handled each interaction.
Every email is evaluated against the same criteria regardless of workload or shift. No more escalations slipping through because an agent was overwhelmed.
Every escalation routing decision is logged with the signals that triggered it, providing data to refine your escalation criteria over time.
Email infrastructure built for AI agents. Verifiable identity, graduated oversight, and a 38-tool MCP server. Formally verified in Lean 4.