agent-native-architecture

Agent-native architecture designs applications where autonomous agents are the primary interface, moving beyond traditional code-centric development. It relies on principles like Parity, Granularity, and Composability to define features through prompts and primitive tools.

Design event-driven agents by using architectural patterns that allow autonomous systems to react to triggers. This approach integrates UI components and selects appropriate model tiers to ensure efficient execution of complex tasks.

Yes, agent-native architecture supports Git-based self-modification. This pattern allows autonomous agents to interact with and modify their own application codebase directly through Git workflows to achieve improvement over time.

The core principles include Parity, Granularity, Composability, Emergent Capability, and Improvement Over Time. These concepts guide prompt-based feature definition and the design of primitive tools for autonomous systems.

Select LLM model tiers based on task complexity within the agent-driven workflow. Agent-native architecture patterns guide efficient model tier selection to balance execution speed and autonomous reasoning capabilities.

Use prompt-based feature definition when building applications where users interact primarily through natural language. This approach allows autonomous agents to handle complex tasks dynamically rather than relying on hardcoded logic.