agent-orchestration

# Agent Orchestration Comprehensive patterns for building and coordinating AI agents -- from single-agent reasoning loops to multi-agent systems and framework selection. Each category has individual rule files in `rules/` loaded on-demand. ## Quick Reference | Category | Rules | Impact | When to Use | |----------|-------|--------|-------------| | [Agent Loops](#agent-loops) | 2 | HIGH | ReAct reasoning, plan-and-execute, self-correction | | [Multi-Agent Coordination](#multi-agent-coordination) | 3 | CRITICAL | Supervisor routing, agent debate, result synthesis | | [Alternative Frameworks](#alternative-frameworks) | 3 | HIGH | CrewAI crews, AutoGen teams, framework comparison | | [Multi-Scenario](#multi-scenario) | 2 | MEDIUM | Parallel scenario orchestration, difficulty routing | **Total: 10 rules across 4 categories** ## Quick Start ```python # ReAct agent loop async def react_loop(question: str, tools: dict, max_steps: int = 10) -> str: history = REACT_PROMPT.format(tools=list(tools.keys()), question=question) for step in range(max_steps): response = await llm.chat([{"role": "user", "content": history}]) if "Final Answer:" in response.content: return response.content.split("Final Answer:")[-1].strip() if "Action:" in response.content: action = parse_action(response.content) result = await tools[action.name](*action.args) history += f"\nObservation: {result}\n" return "Max steps reached...