agent-native-architecture

Prompt-native agent design defines outcomes in prompts rather than hard-coded functions, enabling self-modifying systems. You outline features as prompts, identify the primitives (read/write/list operations) the agent needs, and apply governance controls like approval gates and health checks to safely manage autonomy and evolution.

Coding behavior locks features into static code paths; prompt-native design treats features as dynamic prompts interpreted at runtime. This enables rapid iteration, flexible problem-solving, and self-modification without redeployment. Primitives serve as the capability foundation the agent uses to act on prompt-defined outcomes.

Yes. MCP server patterns work with prompt-native architecture to support self-modifying agents. You define capabilities through prompts and primitives, allowing the agent to adjust its approach dynamically. Governance safeguards—rollback mechanisms, approval gates, and health checks—ensure safe autonomous evolution.

Governance for prompt-native agents includes approval gates (human or automated checkpoints before changes), health checks (validation that modifications work correctly), and rollback capabilities (revert to prior states). These controls balance agent autonomy with trust and safety requirements.

Core primitives are read, write, and list operations. These fundamental capabilities enable the agent to interact with its environment, access data, and execute outcomes specified in prompts. Feature requirements map directly to which primitives the agent must have access to.

Yes. Since features live in prompts rather than code, you can iterate quickly without recompilation or redeployment. This approach is ideal for exploratory agent work, testing new behaviors, and adapting to changing requirements in real time.