agent-native-architecture

Prompt-native agent architecture moves decision logic into prompts and primitive tools instead of hard-coding behavior, enabling AI agents to reason through outcomes and adapt dynamically rather than execute fixed workflows.

To build autonomous agents, define desired outcomes in prompts, select simple primitives like read, write, and list, and inject dynamic app context such as resources and capabilities to drive adaptive behavior.

Use prompt-native design for MCP-style servers when you need agents to handle dynamic contexts and self-modifying systems safely, utilizing primitives and CRUD-like governance rather than rigid, pre-programmed workflows.

Yes, prompt-native architecture supports safe self-modification by applying specific controls and CRUD-like governance, allowing autonomous agents to adapt over time while maintaining safe execution boundaries.

Relying on prompts and primitives shifts complexity from code to context management, requiring careful injection of dynamic app context and robust shared workspaces to prevent unpredictable agent behavior and ensure action parity.

Yes, shared workspaces coordinate actions across prompt-native agents by providing a common environment for reading, writing, and managing dynamic context, ensuring synchronized behavior without hard-coded dependencies.