agent-native-architecture

Agent-native architecture shifts decision logic from code into prompts, using atomic tools like read_file and bash to enable robust, looped agent behavior with transparent outputs.

Build agent workflows by combining atomic primitives through prompts rather than writing new code, utilizing dynamic context injection to accumulate data and refine agent behavior over time.

Yes, UI parity ensures every interface action has a corresponding agent tool, enabling the agent to perform identical tasks via prompts within a shared workspace.

Automate research loops by designing with atomic tools and prompts, allowing the orchestrator to handle open-ended requests and discover useful patterns through emergent capability.

Yes, you can improve agent behavior over time by accumulating context and refining prompts, leveraging emergent capability to address open-ended requests without code changes.

Avoid atomic tools when tasks require tightly coupled, monolithic workflows, as this architecture relies on composability and dynamic context injection for open-ended domain requests.