Email deliverability toolkit for humans and AI agents
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Thami Benjelloun

Launch planned for Wednesday: how should AI agents handle email deliverability?

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Mailwarm
•

A few years ago, we learned something painful while building startups:

Sometimes growth is not blocked by your product or your offer.
Sometimes growth is blocked by something invisible.

For us, that invisible thing was email deliverability.

We where sending thousands of legitimate emails.

We were working on the targeting, the copy, the follow-ups.
But a big part of them was landing in spam

So nothing was happening.

We built @Mailwarm in 2020 because of that problem. Since then, we’ve helped thousands of teams improve sender reputation, avoid spam, and understand what was happening behind the scenes.

But after years of onboarding calls and deliverability sessions, we kept seeing the same pattern:

People had tools.
They had dashboards.
They had data.

But they still didn’t know what was wrong or what to fix first.

That’s why we built mailX.

Your emails go to spam. mailX shows you why and how to fix it.

The web app is simple: run a domain check, get clear answers, and know what to fix.

But the part I’m most excited about is the agent layer.

We made mailX available through API + MCP, so AI agents can use it directly inside workflows.

And this opens a question I find really interesting:

If AI agents can write emails, send follow-ups, manage workflows, and help with outbound…

  • Should they also check email deliverability before sending?

  • Should they verify if a domain is healthy?

  • Should they monitor blacklist status?

  • Should they detect authentication issues?

  • Should they explain why emails may land in spam?

  • Should they suggest what to fix before campaigns break?

We’re launching mailX this Wednesday on Product Hunt.

Try it here: https://themailx.com
MCP endpoint: https://themailx.com/mcp

I have a question for you, founders:

What should an email AI agent check first before sending?

  1. Domain reputation

  2. Authentication setup

  3. Blacklist status

  4. Inbox placement risk

  5. All of the above

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Lakshminath Reddy Dondeti
Google over corrects on spam. Some of their rules are horribly broken.
Vidya Narayanan
@lakshminath_dondeti I'm super tired of Gmail policies
Lakshminath Reddy Dondeti
@hellovidya they block email from my own domain 😅
Daniel Nwankwo
AI agents should handle email like a real sender, not a spam machine. Good deliverability still comes down to the basics: warmed domains, SPF/DKIM/DMARC, low complaint rates, and emails people actually want to open. If agents start sending generic messages at random volume, inbox providers will punish them fast. The smartest setup is using AI for personalization and timing, not for sending thousands of low quality emails.
Thami Benjelloun

@daniel_nwankwo 100% agree.

AI should not be used to increase bad volume. It should be used to improve relevance, timing, and consistency.

The risk is that agents make sending easier, but if deliverability checks are not part of the workflow, they can also damage a domain much faster.

That’s exactly why I think agents need an email deliverability layer before and during sending.

Othman Katim

The cheapest growth lever you have isn’t a sharper offer or more leads. It’s making the pipeline you already paid for actually show up.

Nobody puts a number on it, If 30% of your cold emails land in spam, you didn’t lose 30% of your emails. You lost 30% of everything you paid to send them.

The list. The sending tools. The SDR hours. The copywriter. The follow-up sequences. You paid full price and quietly dumped a third of it in the trash.

Thami Benjelloun

@othman_katim I’ve seen this happen a lot. Teams spend weeks improving the offer, building better lists, rewriting sequences… and only later realize a big part of the pipeline was never even seen. That’s the painful part with deliverability.