What problem does it solve?
Traditional applications hardcode features as static code, creating rigid systems that cannot adapt to user needs or discover latent demand. Agent-native architecture solves this by treating agents as first-class citizens that achieve outcomes through tool use and iterative loops, enabling emergent capability and continuous improvement without shipping code.
Core Features & Use Cases
- Five Core Principles: Parity, Granularity, Composability, Emergent Capability, and Improvement Over Time ensure agents can do anything users can do.
- Architecture Patterns: Event-driven agents, unified orchestrators, shared workspaces, and two-layer git for self-modifying systems.
- Tool Design: MCP primitives, dynamic capability discovery, CRUD completeness, and guidelines for graduating to domain tools.
- Execution Patterns: Explicit completion signals, partial completion with checkpoints, model tier selection, and context limit management.
- Product Implications: Progressive disclosure, latent demand discovery, approval flows, and capability visibility.
- Use Case: Design a research agent that searches academic databases, downloads papers, extracts text via OCR, and generates annotated bibliographies—all through prompt-defined outcomes rather than hardcoded workflows.
Quick Start
Use the agent-native-architecture skill to design a new system where agents can organize your files, manage your reading list, and automate workflows by operating in loops with atomic tools.
