How agents should think about email.
Architecture, patterns, and deliverability from the team building email infrastructure for AI agents.
We write about agent email patterns, deliverability, and security. These posts explain the decisions behind the API. They’re technical. We don’t write fluff. Pick the topic that matters to you right now.
How AI Agents Should Handle Email Replies
A technical guide to threading, reply detection, context management, and response generation for AI agents that send and receive email.
5 Effective Strategies for SaaS B2B Lead Generation
Five B2B SaaS lead generation strategies that work in 2026: AI agent outbound, PLG signal-triggered email, trial-to-paid nurture, inbound qualification, and partner email campaigns.
Building an Email-Driven Agent Workflow
Learn how to build production-grade email-driven agent workflows: inbound parsing, state machines, threading, tool dispatch, and deliverability for automated senders.
Structured Reply Events for Email Agents
Learn how to design structured reply events so your email agents can parse, route, and act on inbound replies with precision — covering schemas, threading, and idempotency.
Email API for AI Agents: Architecture & Provider Guide
Compare email API providers for AI agents. Covers MCP support, inbound parsing, deliverability, and architecture — with a clear recommendation for agent builders.
How to Build an MCP Email Server for AI Agents
Learn how to build an MCP email server that lets AI agents send, receive, and parse email via the Model Context Protocol — with real code and architecture.
Webhook-Based Inbound Email Processing for AI Agents
Learn how to build reliable webhook-based inbound email pipelines for AI agents — parsing, routing, threading, and handling edge cases at scale.
Transactional vs Agent Email Architecture
Learn the core architectural differences between transactional and agent email systems—covering SMTP flows, inbound parsing, threading, webhooks, and MCP integration.
Structured reply events — why your agent should never read raw email
Most email APIs hand your agent a string body. Mails.ai parses every inbound into a structured reply event — injection score and sender reputation on every event, opt-in classification for intent, entities, and urgency — so your code reads against a structured object, not raw bytes.
Prompt injection in inbound email is a real RCE class. Here's how mails.ai scans for it.
Prompt injection in agent runtimes is a tracked vulnerability class — Microsoft Security Response Center publishes AI-security advisories, OWASP catalogues it as LLM01. The vulnerability is structural: any agent that reads raw inbound text is exposed. Mails.ai runs a six-category scanner on every inbound before the structured reply event reaches your code, and flags high-confidence attacks (`quarantined`) so your agent skips them.
Per-event metered pricing: why monthly tiers force over-commit
Resend wants $20/month whether your agent sends 0 or 50,000 emails. Postmark wants $50. Agent traffic is bursty by nature — quiet days and incident spikes — so monthly tiers structurally force over-commit. Per-event metered pricing aligns cost to what you actually use, and incumbents cannot match without rebuilding their economics.
MCP-native email — `from mails import agent` becomes the default agent comms primitive
MCP collapsed N runtimes × M SDKs into one server that every runtime can use. Mails.ai shipped MCP-native from day one — drop a JSON snippet into your runtime config and your agent gets send, on_reply, list_threads, and get_reputation tools automatically.
Built for agents.
Self-serve in minutes.
Public API opens Q3 2026. Drop ~6 lines into your agent and ship.
$ npm install @mailsai/sdk