agent-native-architecture

To build an agent-native architecture, make autonomous agents the primary execution unit by designing a unified orchestrator loop. Define a dynamic system prompt describing features as sections, and provide primitive tools like read_file and bash to enable self-modifying workflows.

Parity-driven tooling ensures every UI action has a corresponding agent tool to maintain workflow consistency. This approach guarantees that autonomous agents can execute the same operations available to human users, enabling seamless self-modifying workflows.

Start building self-modifying workflows by providing primitive tools like read_file, write_file, list_files, bash, and store_item. Run a unified orchestrator loop that processes user requests by calling these atomic primitives until a completion signal is produced.

Yes, you can use MCP tooling to evolve agent capabilities dynamically. This approach enables autonomous agents to safely graduate workflows to domain tools or code, allowing open-ended task composition through atomic primitives.

Agents fail to compose open-ended tasks when prompts lack feature descriptions as sections and judgment criteria. To fix this, define a dynamic system prompt that describes features clearly and specifies judgment criteria for the orchestrator loop.

No, you do not need hard-coded functions for every feature. Agent-native architectures design loops where features are outcomes achieved by autonomous agents using atomic primitives, enabling flexible and open-ended task composition.