What Machines Actually Need
When teams set out to “design for AI,” they often start by changing the interface: adding chat, summaries, or automation on top of existing products. This approach assumes that intelligence lives at the surface.
It doesn’t.
AI systems fail or succeed not because of how they speak, but because of what they are allowed to understand. The most important design work happens beneath the interface—in data structures, contracts, and constraints that make reasoning possible.
To design for machines, we must stop optimizing for attention and start optimizing for comprehension.
This is the shift from UX (User Experience) to RX (Reasoning Experience).
Designing for RX (Reasoning Experience)
Why optimizing for understanding beats optimizing for clicks
User experience is about guiding humans through choices. Reasoning experience is about enabling machines to arrive at correct conclusions.
These are not the same problem.
UX rewards:
Simplification
Emotional framing
Progressive disclosure
Aesthetic hierarchy
RX rewards:
Explicit structure
Deterministic meaning
Traceable logic
Stable relationships
A system optimized for RX answers questions like:
What does this thing mean?
What constraints apply?
What changes over time?
What actions are valid next?
When RX is weak, machines guess. They interpolate missing meaning from patterns, which is another way of saying they hallucinate.
Designing for RX means:
Making relationships explicit instead of implied
Encoding rules instead of describing them
Favoring schemas over prose
Click-through rate is irrelevant to a system that never clicks. What matters is whether the machine can understand the world well enough to act without inventing it.
Facts, Inference, and Opinion Must Be Separated
The most common cause of AI hallucination in production systems
Most AI failures are not model failures. They are data failures.
Specifically, they stem from collapsing three fundamentally different things into one undifferentiated blob:
Facts
Things that are verifiably true at a point in time.Inference
Conclusions drawn from facts, often probabilistic and context-dependent.Opinion
Judgments, sentiment, or preference.
Humans navigate these distinctions intuitively. Machines do not.
When a policy description, a customer review, and a marketing claim are all treated as “content,” an AI system has no reliable way to decide what can be asserted, what must be hedged, and what should be treated as anecdotal.
This is the root cause of most hallucinations in real systems:
The model is forced to invent certainty because the data does not encode uncertainty.
Separating fact, inference, and opinion—explicitly, structurally, and consistently—gives machines permission to reason honestly.
Facts should be timestamped and sourced.
Inferences should carry confidence and assumptions.
Opinions should be clearly labeled and bounded.
Without this separation, even the best models will confidently say the wrong thing.
Explicit Uncertainty Is a Feature, Not a Bug
How confidence calibration creates trust instead of friction
Human systems often treat uncertainty as weakness. Machine systems require it to function correctly.
An AI that never expresses uncertainty is not confident—it is dangerous.
In reasoning systems, uncertainty serves three critical roles:
It prevents overcommitment
Machines must know when not to act or when to ask for clarification.It enables correct language
“Usually,” “depends on,” and “based on current information” are not hedges—they are accuracy mechanisms.It supports trust calibration
Users learn when to rely on the system and when to double-check.
Explicit uncertainty is not about being vague. It is about being precise about what is known, what is inferred, and what is unstable.
Well-designed systems encode:
Confidence levels
Validity windows
Jurisdiction or context limits
Known exceptions
When uncertainty is explicit, machines can:
Choose safer actions
Defer when appropriate
Escalate to humans
Avoid hallucinating absolutes
Trust is not created by certainty.
Trust is created by predictable correctness over time.
Why APIs Must Express Intent, Not Objects
The end of CRUD as a primary product interface
Traditional APIs mirror databases. They expose objects and allow basic operations:
Create
Read
Update
Delete
This model works when humans assemble meaning manually. It fails when machines are expected to reason.
Machines do not think in objects. They think in goals, constraints, and actions.
Consider the difference between:
“Here is a booking object.”
“Determine whether this booking can be canceled without penalty.”
The first exposes data.
The second exposes intent.
Intent-driven APIs:
Encode business rules
Enforce constraints
Return explanations, not just records
Make permissible actions explicit
They answer questions like:
What can I do next?
Why did this change?
What prevented this outcome?
What tradeoffs exist?
This marks the end of CRUD as the dominant abstraction for intelligent systems. Objects still exist—but they are subordinate to capabilities and decisions.
An API designed for machines is not a data pipe. It is a reasoning surface.
The Deeper Pattern
Across all of these shifts, one principle holds:
Machines do not need more information.
They need better-shaped reality.
Reasoning systems succeed when:
Meaning is explicit
Uncertainty is encoded
Actions are constrained
Intent is first-class
Designing for RX is not about adding AI features. It is about reshaping products so that understanding precedes intelligence.
As AI systems take on more judgment and responsibility, the organizations that win will be those that stop asking:
“How do we make this easier to use?”
and start asking:
“How do we make this impossible to misunderstand?”
That is what machines actually need.