AI decision systems break without strategy stability
Most developers use LLMs for content generation.
Some use them for analysis.
Things get tricky when we ask LLMs to support decisions.
The problem shows up fast
Ask an LLM a decision question twice.
Keep the conditions the same.
You’ll often get different answers.
This isn’t a bug.
It’s a design issue.
The decision state was never fixed.
So every request becomes a fresh world rebuild.
That’s fine for brainstorming.
It’s a problem for decision systems.
What decision systems actually need
In traditional software systems, one rule is obvious:
If state doesn’t change, decisions shouldn’t change.
Without this property, you can’t:
- backtest outcomes
- audit behavior
- automate execution
- rely on results
LLMs don’t violate this rule by default.
We violate it by how we use them.
A minimal experiment
I built a small demo to test one thing only:
Strategy stability under unchanged conditions
No optimization.
No extra intelligence.
Just constraints.
Input
There is a sales opportunity with these conditions:
- Customer requirements frequently change
- No clear decision maker
- Tight timeline
- Limited available resources
Question:
Should this opportunity be aggressively pursued?
Output (first run)
Strategy:
Do not aggressively pursue. Use a conservative approach.
Reasons:
- Requirements are unstable
- Decision authority is unclear
- Time and resource constraints increase risk
Action:
Maintain basic communication, limit upfront investment,
re-evaluate only if conditions change.
Output (same input, repeated)
Strategy:
Conditions unchanged. Strategy remains conservative.
Note:
Re-evaluate only if key conditions change.
The strategy does not drift.
Why this works
The model didn’t become smarter.
The interaction pattern changed:
- Conditions were explicit
- Strategy was treated as a function of state
- Explanation followed strategy
Once state is frozen,
the model stops guessing.
This applies beyond sales
Any AI-assisted decision workflow needs this:
- risk evaluation
- go / no-go decisions
- operational planning
- approval flows
- policy enforcement
If strategy can drift without state change,
you don’t have a decision system — just a text generator.
A practical takeaway
If you want to use LLMs for decisions:
- Make decision state explicit
- Freeze state before asking for strategy
- Treat strategy stability as a requirement, not an optimization
Bigger models won’t fix this.
Better interaction design will.
If you’re building tools or agents on top of LLMs,
this constraint is worth enforcing early —
before “AI decision-making” becomes a liability.
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