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AI-Native Problem Framing Framework¶

type: concept

confidence: high

updated: 2026-07-12

status: compiled

namespace: ai-native-product-surfaces

sources: 3

The AI-Native Problem Framing Framework is the reusable lens for deciding whether a product surface is genuinely AI-native or merely has AI attached.

Core frame¶

Define the system before picking models:

Environment — what data describes the world?
Actions — what can the system do?
Goal — what is success or what is optimized?
Constraints — what must never be violated?
Agency constraints — what must remain visible, steerable, inspectable, interruptible, or user-owned?

Bad framing creates bad AI. Environment, action space, objective, constraints, and agency boundaries define the intelligence problem.

World Model Control Surfaces extend the frame into a planning/review loop: observed state -> candidate actions -> predicted outcomes -> objective/guardrail score -> recommended next safe step. Use it when an AI-native surface needs to expose what the system expects to happen before a human or agent acts.

Agency constraints come from Material Loop and Glass Interfaces: AI can shorten the path from idea to artifact, but the interface should not hide the judgment-bearing parts of the loop.

Interface mode after framing¶

After the environment/actions/goal/constraints frame is clear, use Interaction Mode Routing to choose which parts should be direct UI, agentic delegation, generative UI, or stable truth/routing.

This keeps AI-native product work from collapsing into either chatbot theatre or agentic overreach. The interface should preserve provenance, constraints, and human control where the domain requires them.

Product-surface use¶

For ai-native-product-surfaces, this framework prevents vague “add AI” product thinking. It asks whether the surface perceives a domain, chooses or prepares actions, improves the chance of achieving a goal, and respects hard constraints.

It is especially useful for comparing:

Planned Program Intel: event-program decision routing and institutional memory;
myAbode: real-estate prepared next actions under compliance and adoption constraints;
future surfaces that need prediction, optimization, and execution separated rather than collapsed into a black box.

After framing the AI system, use Product Management as System Steering to make the human product calls around decision tempo, ownership seams, stakeholder dynamics, scope, and ecosystem incentives.

Boundary¶

Do not blindly copy game/RL patterns into product domains. Real-world operational products have partial visibility, noisy outcomes, multiple stakeholders, and constraints that must be represented explicitly.

Source¶

Compiled from Knowledge/concepts/ai-native-problem-framing-framework.md.