agent-native-architecture

Agent-native architecture designs software where AI agents operate in loops, choose actions, and complete outcomes rather than calling single functions. It gives agents the same capabilities as users through capability parity, shared workspaces, and dynamic context injection.

Designing MCP tools involves defining atomic, primitive tools that agents can use to perform real work. This includes structuring shared workspace files, injecting dynamic runtime context, and ensuring capability parity so agents know what exists and can operate it.

Best practices for agent guardrails include implementing completion signals, partial progress handling, model-tier selection, and approval gates. Prompt-based behavior refinement also ensures safer execution of self-modifying workflows.

Yes, prompt-driven feature behavior is a core component of agent-native architecture. It allows you to control and refine how autonomous agents operate within loops and complete outcomes using dynamic context injection.

Completion signaling and checkpointing are production guardrails used to manage autonomous agent loops. They ensure partial progress is handled safely and allow for model-tier selection and approval gates during execution.

You should use agent-native architecture when building apps where AI agents need to do real work, not just answer questions. It suits systems requiring autonomous loops, shared workspaces, tool design, and self-modifying workflows.