--- title: Do spam filters catch AI-written email? metaTitle: Does AI-written email get flagged as spam? description: Filters do not detect authorship, they detect patterns. What actually triggers them in agent-sent mail, which AI writing habits look like bulk, and how to write so a real message reads as one. date: 2026-07-29 author: MCPmailer tags: Deliverability --- The worry is understandable and slightly misplaced. Mailbox providers are not running an AI detector on your body text and filing the results. They are doing what they have always done: judging the sender's reputation, the authentication, and whether this message resembles mail that recipients have previously not wanted. ![A message judged on authentication, reputation, and pattern rather than authorship](/blog/does-ai-written-email-get-filtered/hero.webp "Three things get judged, and none of them is who wrote the words.") Agent-sent mail does get filtered more often than human mail, but not for the reason people assume. ## What filters actually weigh Roughly in order of impact: 1. **Authentication and alignment.** Does SPF or DKIM pass on the domain in the From address. Fail this and nothing else matters much, per [SPF, DKIM, and DMARC for an agent subdomain](/blog/dmarc-record-generator-agents). 2. **Domain and IP reputation.** History, volume shape, and how recipients have reacted to your mail before. 3. **Engagement.** Opens, replies, deletions without reading, and complaints. Replies are the strongest positive signal available. 4. **Similarity across recipients.** Whether many people are receiving substantially the same message. 5. **Content signals.** Link patterns, image-heavy HTML, tracking pixels, attachment types, and phrases associated with mail people report. Authorship appears nowhere on that list. What does appear is a set of things AI-written mail is unusually prone to. ## The habits that look like bulk **Templated structure with a name swapped in.** A model given a task and a list will produce twenty messages with the same skeleton. That is exactly what similarity detection is for, and it does not care that a language model rather than a mail merge produced it. **A recognisable house style.** Restating the question, an apology, a closing paragraph about valuing the relationship. Not penalised for being AI, penalised for reading like marketing rather than correspondence. **Volume that arrives at machine speed.** A person sends thirty messages over a day. An agent sends thirty in a minute, and a burst from a domain with little history is the shape of a compromised account. **Marketing furniture nobody asked for.** Tracking pixels, shortened links, an HTML signature with three images. Every one of those pushes a conversational message toward the promotions bucket. **Perfect, generic prose with nothing specific in it.** The one genuine content signal in the AI direction: a message that could have been sent to anyone reads as mail that was. ![Twenty near-identical messages beside twenty specific ones](/blog/does-ai-written-email-get-filtered/similarity.webp "Similarity across recipients is the signal. The author is not.") ## Write so a real message reads as one - **Be specific in the first sentence.** An order number, a date, a detail from the thread. Specificity is both good writing and the clearest evidence this message was meant for this person. - **Reply rather than send where you can.** A reply arrives in an existing thread with an engagement history attached, which is the best position mail can be in. - **Vary because the content varies,** not through synthetic randomisation. Spinning synonyms is a 2011 trick that helps nothing. - **Keep it plain.** Markdown delivered as text plus HTML, short paragraphs, no image signature, no tracking pixel unless you have a reason. - **Match length to the message.** A two-line question deserves a two-line answer, and a four-paragraph reply to it is its own signal. - **Drop the closing flourish.** "Please do not hesitate to reach out" is filler in any register. Most of that overlaps with writing well, which is convenient: the same edits that make an agent's mail less irritating make it less filterable. ## What is coming Providers have tightened bulk sender requirements steadily: authentication, one-click unsubscribe, and complaint rate thresholds are now enforced rather than encouraged. None of that is aimed at agents specifically, and all of it is satisfied by the setup described in [deliverability for agent senders](/blog/agent-email-deliverability). Disclosure that a message was machine-generated is a live regulatory question rather than a filtering one, covered in [the compliance floor](/blog/ai-email-compliance-gdpr-canspam). Saying so has not been observed to hurt delivery, and it avoids the worse outcome of a recipient discovering it later. ## Diagnosing your own case If your agent's mail is landing in spam, work in this order rather than rewriting the copy first. 1. Check authentication on a real delivered message, not just the DNS. 2. Check whether it is one recipient's rule or a whole provider. 3. Check the domain's age and recent volume shape. 4. Check bounce and complaint rates for the period. 5. Read three of the agent's messages as a stranger and ask whether they resemble each other. 6. Only then change the content. Content is the last lever because it is the smallest one. The order and the reasoning are in [deliverability for agent senders](/blog/agent-email-deliverability). ## Questions ### Do spam filters detect AI-written email? Not as such. They weigh authentication, sender reputation, engagement, similarity across recipients, and content patterns. Authorship is not a signal they check. ### Why does my agent's mail land in spam then? Usually authentication or alignment problems, a domain with no history, volume arriving in bursts, or many near-identical messages. Those are the same reasons human-sent bulk mail lands there. ### Does saying the message is from an AI hurt deliverability? No observed effect on filtering, and it avoids the worse outcome of a recipient discovering it mid-thread. The regulatory direction is toward disclosure anyway. ### Should I randomise wording to avoid similarity detection? No. Synthetic variation is an old trick that does not work. Vary because the content genuinely differs per recipient, or send fewer messages. ### Do tracking pixels hurt? They can, by pushing a conversational message toward the promotions category. Open tracking is off by default here for that reason; turn it on only when you have a use for the data. ### What is the single biggest lever? Sending mail people reply to. Replies are the strongest positive engagement signal available, which is why inbound-first agents rarely have a deliverability problem at all. ## Related - [Deliverability for agent senders](/blog/agent-email-deliverability) - [SPF, DKIM, and DMARC for an agent subdomain](/blog/dmarc-record-generator-agents) - [Bounces, complaints, and suppression](/blog/bounces-suppression-agent-email) - [A warmup plan for a new agent sending domain](/blog/email-warmup-plan-agents)