AI adjusts email content, timing, and sequence based on engagement signals. Monitored mode handles volume while you optimize strategy.
Static drip campaigns send the same sequence regardless of how recipients engage. Interested prospects get the same slow cadence as disengaged ones, while uninterested recipients receive irrelevant content that drives unsubscribes. Managing adaptive sequences manually across thousands of contacts is impossible.
MultiMail's AI agent manages multi-step drip campaigns that adapt based on recipient engagement. It adjusts content, timing, and sequence progression based on opens, clicks, and conversion signals. Monitored mode handles high-volume sending while marketers review performance and make strategic adjustments.
When a contact enters a campaign trigger (signup, download, event), the AI enrolls them in the appropriate drip sequence using add_contact with campaign-specific tags.
Before each email in the sequence, the AI evaluates the contact's engagement — opens, clicks, replies, and conversion events. This determines which email to send next.
The AI selects the best next email based on engagement, personalizes content to the contact's interests and behavior, and sends via send_email.
Marketing reviews campaign metrics in the monitored log — open rates, click rates, and conversion by sequence step. Insights drive strategic adjustments to content and timing.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def next_drip_email(contact: dict, engagement: dict) -> dict:
"""Select next email based on engagement."""
if engagement["clicked_pricing"]:
return {"subject": "Ready to get started?",
"body": "Since you checked out pricing..."}
elif engagement["opened_last"]:
return {"subject": "3 ways teams use MultiMail",
"body": "Here are the most popular use cases..."}
else:
return {"subject": "Did you see our latest feature?",
"body": "We just launched webhooks..."}
def run_drip_step(contact: dict):
engagement = get_engagement_data(contact["id"])
email = next_drip_email(contact, engagement)
response = requests.post(
f"{API}/send",
headers=HEADERS,
json={
"from": "[email protected]",
"to": contact["email"],
"subject": email["subject"],
"text_body": f"Hi {contact[&"cm">#039;name']},\n\n{email['body']}"
}
)
return response.json()Run a drip campaign that adjusts the next email based on recipient engagement signals.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
"cm"># Enroll new signup in drip campaign
def enroll_in_drip(email: str, name: str, campaign: str):
response = requests.post(
f"{API}/contacts",
headers=HEADERS,
json={
"email": email,
"name": name,
"tags": [f"drip-{campaign}", "drip-step-1"]
}
)
return response.json()
"cm"># Progress contact to next step
def advance_drip(contact_id: str, current_step: int):
requests.post(
f"{API}/contacts/{contact_id}/tags",
headers=HEADERS,
json={"tags": [f"drip-step-{current_step + 1}"]}
)
"cm"># Find contacts at a specific step
step_2 = requests.get(
f"{API}/contacts/search",
headers=HEADERS,
params={"tags": "drip-onboarding,drip-step-2"}
).json()Add contacts to a drip campaign and track their progression through the sequence.
"cm">// Drip campaign management via MCP
"cm">// 1. Enroll contact in campaign
await mcp.add_contact({
email: newUser.email,
name: newUser.name,
tags: ["drip-onboarding", "drip-step-1"]
});
"cm">// 2. Find contacts ready for next step
const step1 = await mcp.search_contacts({
tags: ["drip-onboarding", "drip-step-1"]
});
"cm">// 3. Send personalized drip email
for (const contact of step1.contacts) {
await mcp.send_email({
from: "[email protected]",
to: contact.email,
subject: "3 ways teams use MultiMail to save 10 hours/week",
text_body: `Hi ${contact.name}, since you signed up 5 days ago...`
});
}Manage drip campaigns using MultiMail MCP tools.
AI adjusts the next email based on engagement signals — engaged contacts accelerate through the funnel while cold contacts receive re-engagement content.
Personalized, behaviorally-triggered emails convert 3-5x better than static sequences that ignore recipient engagement.
AI stops sending to disengaged contacts before they unsubscribe, preserving your sender reputation and contact list quality.
Monitored mode handles thousands of drip emails autonomously while marketers review aggregate performance metrics and adjust strategy.
Email infrastructure built for AI agents. Verifiable identity, graduated oversight, and a 38-tool MCP server. Formally verified in Lean 4.