agent-native-architecture

Agent-native architecture is a design approach where autonomous agents are primary citizens driving agentic loops. You need it when building self-modifying systems or applications where agents manage user workflows and learn over time through natural language commands.

Designing atomic tools for autonomous agent systems requires focusing on granularity and composability. The framework provides principles and checklists for tool design, execution patterns, and context injection to ensure agents can operate effectively within agentic loops.

Refactoring an existing codebase into an agent-native application is supported by the framework. It offers guidance on migrating current systems by applying principles like parity and composability, addressing testing strategies and mobile patterns during the transition.

The core principles for building agent-first applications include parity, granularity, and composability. These principles guide the comprehensive framework covering tool design, execution patterns, context injection, and product implications for systems driven by autonomous agents.

Context injection in LLM agent loops involves managing the information passed to the model during execution. The framework guides handling context alongside tool design and execution patterns to maintain effective autonomous operation and emergent capabilities.

Product design implications for autonomous systems with agents center on user trust and emergent capabilities. The framework offers insights into product strategy, ensuring applications driven by agentic loops effectively manage workflows and learn over time.