2h • 📚 Resources & Finds
Journey update part 2
The more I build this, the more I think people are slightly looking at AI agents the wrong way.
A lot of the conversation is still focused on:
“How do we make AI do more?”
“How do we make it autonomous?”
“How do we get it to complete tasks faster?”
But I’m starting to think the bigger question is:
How do we make AI work inside a controlled system?
Because once AI starts doing meaningful work, the real problem is no longer just intelligence.
It becomes governance.
Who planned the work?
Who implemented it?
Who reviewed it?
Who judged it?
Who approved it?
What evidence proves it worked?
What did it cost?
What happens when it fails?
What stops the same AI from marking its own homework?
What does the system learn from the mistake?
That is the part I find interesting.
Not just AI acting, but AI being accountable.
I don’t think the future is one giant agent doing everything by itself.
I think it looks more like a governed team of specialised workers:
Planner.
Implementer.
Reviewer.
Judge.
Operator.
Memory.
Cost control.
Evidence trail.
Each role has boundaries.
Each decision has proof.
Each action has a record.
Each failure becomes a lesson.
That is the difference between a clever demo and something that could actually run serious projects.
The real value might not come from making AI more independent.
It might come from making AI more accountable, more structured, and more aligned with how complex work actually gets done.
That’s the layer I’m building towards.
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Journey update part 2
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