Adaptive Interfaces is an open framework for designing how intelligent systems interact with complex environments.

This work focuses on the interface layer, the boundary where knowledge, capability, and intent are exchanged between:

• humans and AI systems
• AI systems and codebases
• AI systems and tools (APIs, CLIs, services)
• AI systems and other AI systems

Intelligent systems act within environments that have:

• existing structure
• implicit conventions
• explicit constraints
• domain-specific expectations

Correct behavior is a function of alignment with the interface through which that capability is applied.

Adaptive Interfaces studies how systems:

• infer the structure of an environment
• adapt to its constraints and patterns
• act in ways that are consistent with local expectations

This project focus is intelligent systems interacting with existing systems.

Key areas include:

• Codebase capability inference
• Engineering pattern inference
• Interface surface modeling

• Skills and task definitions
• Constraints and boundaries
• Decision discipline

• Entering and aligning to new environments
• Pattern alignment and constraint adherence
• Handling ambiguity and conflicting signals

• Measuring behavioral correctness
• Assessing alignment with local engineering practices
• Reproducibility and accountability

This project is:

• Not prompt engineering
• Not tied to a specific model or vendor
• Not a wrapper around existing APIs
• Not limited to LLMs

While current work focuses on modern AI systems (including LLM-based agents), the framework is model-agnostic and durable.

Adaptive Interfaces aims to establish:

• structured methods for environment-aware AI behavior
• reusable specifications for agent interaction
• evaluation frameworks for behavioral correctness
• a shared foundation for adaptive systems engineering

Early-stage, specification-first.