agent-native-architecture

Agent-native architecture treats agents as first-class execution units that autonomously operate in loops to achieve outcomes. It uses atomic primitives and dynamic context injection so agents can combine behaviors without hard-coded workflows.

Parity-driven design ensures every user action has a corresponding agent tool and documented capability in prompts. You provide atomic primitives and shared workspace patterns so agents can autonomously combine them to achieve complex outcomes.

You build MCP-style tooling by defining clear tool primitives and shared context that agents use autonomously. Self-modifying patterns emerge by observing user requests and iteratively evolving prompts and tools to improve agent capabilities over time.

Yes, agent-native architecture supports context-driven execution by injecting dynamic app state into prompts. This enables real-time decision making, partial completion, and resume capabilities for autonomous workflows.

Use granularity and composability by providing atomic primitives that agents combine into complex behaviors without hard-coded workflows. Organize these primitives with modular references and tooling guidelines to ensure safe, scalable agent-driven workflows.

Agent-native architecture is not suitable when your application requires deterministic, hard-coded workflows rather than emergent outcomes. If features cannot be expressed as autonomous loops with tool primitives, a traditional execution model is more appropriate.