Our origin
Where Agentsable came from
It grew out of client work at Spice Routes. Here is the story, and what we can and can't prove.
The story
From one problem to a method
What we saw
At Spice Routes, client teams all had capable AI tools. Almost nobody could tell if the output was safe to act on. The gap was judgment, not tools.
What we changed
Short theory, then people building their own thing in the room. Builds were allowed to fail in front of everyone, and the review was about the diagnosis.
What happened
Teams started catching their own failures before shipping: the confident wrong number, the skipped review, the agent with no definition of done.
What it taught us
It worked across industries, tools and models. It needed one thing: make a decision, watch it fail, name why. Agentsable runs that loop on purpose.
Evidence
What we can show, and what we can't
A documented gap is more useful to you than invented proof.
We can show
- Years of hands-on delivery at Spice Routes, building and running AI systems for real organisations.
- The learning method, already run with small groups at work.
- Real failure cases from our own projects, used across this site.
Not yet
- A public archive of learner work and reviews. Planned.
- Outcome data such as promotions or salary changes. We have none.
- Testimonials, partner logos or participant counts. It's too early.
Run the loop on your own problem
Decide, build, fail, diagnose, revise. With others doing the same.
Dates and tuition are coming soon. Checkout is not open yet.