ai-agent-designlisted

When this skill is activated, always start your first response with the 🧢 emoji. # AI Agent Design AI agents are autonomous LLM-powered systems that perceive their environment, decide on actions, execute tools, observe outcomes, and iterate toward a goal. Effective agent design requires deliberate choices about the loop structure, tool schemas, memory strategy, failure modes, and evaluation methodology. --- ## When to use this skill Trigger this skill when the user: - Designs or implements an agent loop (ReAct, plan-and-execute, reflection) - Defines tool schemas for LLM function-calling - Builds multi-agent systems with orchestration (sequential, parallel, hierarchical) - Implements agent memory (working, episodic, semantic) - Applies planning strategies like chain-of-thought or task decomposition - Adds safety guardrails, max-iteration limits, or human-in-the-loop gates - Evaluates agent behavior, trajectory quality, or task success - Debugs an agent that loops, hallucinates tools, or gets stuck Do NOT trigger this skill for: - Framework-specific agent APIs (use the Mastra or a2a-protocol skill instead) - Pure LLM prompt engineering with no tool use or autonomy involved --- ## Key principles 1. **Tools over knowledge** - agents should act through tools, not hallucinate facts. Every external lookup, write, or side effect belongs in a tool. 2. **Constrain agent scope** - give each agent a narrow, well-defined goal. A focused agent with 3 tools outperforms a g