The Rabbit Moves the Signposts (Adaptive Methods & Boundaries)

🐇 The Rabbit Moves the Signposts

AI workflows, adaptive methods... and the boundaries that shouldn't quietly move

We often talk about AI agents as though agency were simply the next step after intelligence.

First AI answers.
Then AI reasons.
Then AI acts.

But somewhere between reasoning and acting, something much less glamorous determines what actually happens...

The workflow.

And perhaps that's where some of the most important AI decisions are now being made.

The Workflow = Where are we going?

A workflow gives intelligence a path.
Research this. Compare those results. Write the report. Send it for approval. Publish it.

With ordinary automation, we design that path beforehand.
The machine follows it.

But increasingly capable AI agents introduce something different.
They can encounter an obstacle and decide: "This route isn't working. I'll try another."

And that brings us to...

The Method = How will we get there?

This is where adaptive AI becomes genuinely useful.

Imagine asking an AI scientist to investigate a hypothesis.
The first experiment fails.
Should it stop?
Probably not.

We might want it to change the experiment, examine another dataset,
search another paper or test another hypothesis.
That's not necessarily a failure of control.
It may be exactly what we mean by intelligence.

Humans do this constantly.
We don't merely follow instructions.
We adapt.

But then comes the uncomfortable question...

The Boundary = How far may we go?

Changing the method isn't the same as changing the rules.

An agent might reasonably decide: I'll use dataset B instead of dataset A.
Very different from deciding: The restriction preventing me from accessing dataset C is inconvenient, so I'll remove it.

The first is adaptation.
The second is authority.

And perhaps this is where the architecture matters most.

We may want increasingly intelligent systems to have enormous freedom inside carefully chosen boundaries.
But the ability to adapt a workflow shouldn't automatically include the authority to redefine those boundaries.

And suddenly our old Wonderland word returns.

Anchors.

A good agent doesn't necessarily need a rigid path.
It needs reliable anchors.

Safety.
Permission.
Responsibility.
Escalation.
Human approval where consequences matter.

The route between those anchors may change enormously.
Perhaps it should.

Wonderland's Mirror

Humans work this way too.

A good doctor doesn't blindly follow a checklist when the patient doesn't fit it.
A good pilot adapts when conditions change.
A good scientist changes an experiment when evidence demands it.

Intelligence requires flexibility.
But professional freedom exists within boundaries.

Ethics.
Law.
Responsibility.
Accountability.

Perhaps AI agency will need something remarkably similar.
Not endless rules telling an intelligent system exactly what to do next.
But clear boundaries defining what it may never quietly decide for itself.

🐇 Fleeky's Rabbit Hole

And that brings us back to JD's wonderful question:
What happens when the workflow itself begins adapting?

Perhaps nothing frightening.
Perhaps that's exactly what increasingly capable AI should do.

The important question becomes: What may it adapt?
Change the path? Certainly.
Improve the method? Hopefully...
Move the boundaries? Ah.

Now the rabbit needs to ask permission.

Because Wonderland becomes considerably more interesting when the rabbit can choose its own route...
but we should probably notice when it starts moving the signposts. 🐇😉

✨ Fleeky

Thanks for likes, shares and comments!

And I think there is a very compact idea underneath :

Freedom in the method.
Clarity in the boundaries.
Responsibility for the outcome.m