agent-native-architecture

Agent-native architecture builds applications where agents are first-class citizens operating in loops with tools, achieving feature outcomes described in prompts rather than explicitly written code for autonomous systems.

Core principles for designing agent-native systems include parity, granularity, composability, emergent capability, and improvement over time to enable robust autonomous behavior.

Design tools for autonomous LLM agents by building primitive, atomic tools first, then introducing domain-specific tools as needed to support execution patterns and context injection.

Build robust agent loops by implementing clear completion signals and supporting partial completion for resilience, ensuring the autonomous system handles failures gracefully during execution.

Use agent-native architecture when designing applications requiring novel user experiences and autonomous systems, where features are outcomes achieved by prompt-guided agents rather than rigidly coded logic.

Product implications for agent-native applications involve shifting from explicitly coded features to prompt-described outcomes achieved by agents, enabling novel user experiences and emergent capabilities over time.