Individual skill
ai-agents-pro
Design and build LLM agents: ReAct loops, tool schemas, planning, memory, guardrails, evals, and human-in-the-loop. Use when building autonomous multi-step AI systems.
skills/ai-agents-pro/SKILL.md
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# AI Agents Pro
A builder's guide to LLM agents that actually complete tasks: the observe–think–act loop, well-designed tools, bounded execution, memory, guardrails, and evaluation. Framework-agnostic — the patterns work whether you hand-roll the loop or use an agent framework.
## Overview
An agent is an LLM in a loop: it receives a goal, reasons about next steps, calls tools, observes results, and repeats until done. The loop is simple; reliability is not. Production agents fail on ambiguous tool schemas, unbounded loops, prompt injection from tool outputs, and untested edge cases — not on model intelligence.
Design principle: **the agent is only as good as its tools and its boundaries.** Sharp tools with clear schemas, tight step/timeout budgets, and an eval set you run on every change beat clever prompting every time.
## When to use
- Automating multi-step workflows: research, data gathering, code changes, triage.
- Building assistants that act on the user's behalf (file ops, API calls, scheduling).