ia-agent-native-architecture

# Agent-Native Architecture ## Core Principles Five principles govern agent-native design. For detailed explanations, examples, and test criteria, see [core-principles.md](./references/core-principles.md). | Principle | One-line test | |-----------|--------------| | **Parity** | Can the agent achieve every outcome the UI allows? | | **Granularity** | Changing behavior means editing prose, not refactoring code | | **Composability** | Can a feature be added by writing a new prompt, without new code? | | **Emergent Capability** | Can the agent handle open-ended requests it wasn't designed for? | | **Improvement Over Time** | Does the app work better after a month, even without code changes? | ## Focus Area Selection 1. **Design architecture** - Plan a new agent-native system from scratch 2. **Files & workspace** - Use files as the universal interface, shared workspace patterns 3. **Tool design** - Build primitive tools, dynamic capability discovery, CRUD completeness 4. **Domain tools** - Know when to add domain tools vs stay with primitives 5. **Execution patterns** - Completion signals, partial completion, context limits 6. **System prompts** - Define agent behavior in prompts, judgment criteria 7. **Context injection** - Inject runtime app state into agent prompts 8. **Action parity** - Ensure agents can do everything users can do 9. **Self-modification** - Enable agents to safely evolve themselves 10. **Product design** - Progressive disclosure, latent demand, approval ...