agent-native-architecture

Agent-native architecture treats agents as first-class actors by defining atomic primitives like read_file and write_file, using prompt-driven behavior for parity, and orchestrating agents in unified loops to achieve emergent, disciplined capabilities.

To build self-modifying systems, define atomic tools like read_file and write_file, craft a system prompt that governs behavior and exposes features, and run a unified orchestrator that loops the agent until completion within a shared workspace.

Yes, agent-native orchestration requires a shared workspace to keep humans and agents in sync, plus dynamic context injection to expose runtime state directly to prompts, ensuring agents operate with current, accurate information during their loops.

The best way to orchestrate agents is using a unified orchestrator that lets the agent loop until completion, combined with atomic primitives and dynamic context injection to expose runtime state, ensuring disciplined and emergent behavior.

Yes, you can use this agent-native approach to create MCP tools and design autonomous agents by defining atomic primitives, crafting prompt-driven behavior for composability, and running unified orchestration loops within a shared workspace.

You should avoid unified orchestration for agent-native systems if your environment lacks atomic primitives like read_file and write_file, or cannot support a shared workspace and dynamic context injection to expose runtime state to prompts.