Trusted Intelligence #3: We shipped the platform under the platform
What's happening in AI, from the team making it safe to use on sensitive data.
For most of the past three months, our engineering team has been doing something quite invisible. A project we have been calling “Patagonia”, internally. A near-complete rewrite of the layer underneath everything we ship. New data model, new auth, new API surface, new lines drawn between what is platform and what is workspace. The kind of work that makes a product look exactly the same on the outside while every assumption underneath gets replaced.
This week, we’re super excited to announce that the first two things that ride on top of it landed in production at the same time. Alongside them, two pieces went up on our Substack that we never sent by email. We are doing that now. Four items. Two you can use. Two we want you to read.
In this issue:
You can now build Aimable into your own product.
Your AI agents play by the same rules as your people.
Ian on the Aimable Lab.
And Arjé on what actually breaks when you run agents.
Thanks for reading!
You can now build Aimable into your own product
The Aimable Platform API is live. One endpoint. Your developers stop calling OpenAI or Anthropic directly and start calling Aimable instead. What they get back is what they always wanted: a clean answer from the model of your choice. What happens in between is the part that used to keep your CISO up at night. Personal data filtered out on the way in and restored on the way back. Your rules enforced. Your model routing applied. Every call written into the same logbook your auditor already knows how to read.
Most enterprise AI tools treat governance as a feature. A toggle in the admin panel. “Enable PII redaction.” The trouble with governance as a feature is that developers route around it. Not because they are reckless, but because features add friction and people building things optimise for speed. So they call the model directly. They skip the toggle. The audit log gets a hole in it, and nobody notices until the auditor does.
The Aimable Platform API takes a different bet. Governance is not a feature on top of the model. It is the layer the model is called through. Every chat in the Workbench, every n8n agent, every Python script, every Office add-in: same gateway, same rules, same logbook. Bart Evers, our CTO, put it more simply on the day it went live: “This is the first time we sell to two audiences with the same product.” The team building, and the organisation that needs to know what the team built is safe.
Send your developers to: https://docs.aimable.ai/
Your AI agents play by the same rules as your people
The second thing that shipped this week is a full agent governance layer on top of the Platform API. Your automated agents, the ones running in n8n, in OpenClaw, in custom Python, or in a partner tool, can now operate as first-class users of your Aimable Space. They authenticate. They get scoped permissions. They appear in the audit log next to your human users. They follow the same policies, with the same data protection in front of every model call.
This is the part of the manifesto we have been waiting longest to make concrete. Humans and agents play by the same rules. A nice line in the brand book. A different thing to actually wire up. For most of the last year, the agent story was a slide. This week it is a feature.
What it changes in practice: when someone in your organisation, or at one of your vendors, builds a workflow that calls AI, you do not have to choose between letting it through ungoverned or blocking it. You give it an agent identity inside the relevant Space. It gets the same ground rules as the people working there. One rulebook. No shadow AI. No parallel governance regime for the automated half of your work.
Take a look in our Lab
A couple of weeks ago, Ian Zein, our CEO, launched a new page on our site: Aimable Lab. It is where we put the things we are building before they land in the core product. Not roadmap slides. Working surfaces, each with its own page and an animated demo of how the thing actually behaves.
Five things live there today. A hosted agent that runs shifts inside your Space. Notes grounded line by line in the meeting transcript they came from. Skills, a packaged recipe of prompt, tools, policies, and output schema that people and agents call the same way. Office add-ins that bring Aimable into Word, Excel, and PowerPoint. And a canvas where you highlight a section of a document and ask for a change in place.
The running thread across all five: yours, not theirs. These surfaces plug into the Space and policies your team already has, instead of spinning up a parallel vendor world.
→ Read Ian’s full post: Take a look in our Lab
The agent works on day one. The trouble starts in week three.
Last week our commercial lead, Pim, could not find a meeting note. The internal agent that usually drops them into our Slack had been silent for five days. Nobody noticed it was broken. Pim assumed the agent had skipped his meeting.
Arjé, our CPO, went looking. What had happened was not an AI problem. A key the agent depended on had expired. The agent went into a retry loop. The retry loop wrote logs. The logs filled the disk. The host went down, and a few other things went with it.
Three failures like that, in three different agents, including one at home (Rosie, the family agent that lives in our WhatsApp). None of them dramatic. None of them about the model. All of them about the things that quietly drift in the weeks after the agent goes live: keys expiring, model versions shifting under you, dependencies you forgot the agent had, log files filling disks. Field notes from three of Arjé’s quietly failing agents, and where he is landing on what an AI platform is actually for.
→ Read Arjé’s full post: The agent works on day one. The trouble starts in week three.
Trusted Intelligence is published every other Friday by the Aimable team. From links we share, conversations we have, and things that make us think.
If you enjoyed it, please forward it to a colleague who is trying to figure out AI for their organisation.
Next issue: a deeper look at one Skill we built with a finance customer, plus what is moving in Europe. Thanks for reading.