agent-design

To design LLM agent tools effectively, follow principles for building robust interfaces that integrate seamlessly with your agent system. This skill provides tool design principles to ensure reliable performance and prevent context rot during execution.

The best way to evaluate LLM agent performance is to build evaluation frameworks early in the development lifecycle. This skill emphasizes establishing robust evaluation frameworks to measure agent reliability and optimize performance before deployment.

Context engineering prevents context rot by offering strategies that optimize token usage and manage information flow within the LLM context window. This skill provides context management techniques to maintain agent reliability during complex tasks.

You need an orchestrator-worker pattern when your AI assistant requires complex task delegation and dynamic workflow management. This skill guides architecture selection to promote simplicity while addressing challenges in agent reliability and performance optimization.

Complex LLM agent architectures introduce limitations like context rot, reduced reliability, and performance bottlenecks. This skill addresses these challenges by promoting simplicity in architecture design and providing strategies for robust evaluation and production readiness.