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 2 HIGH ReAct reasoning, plan-and-execute, self-correction

Multi-Agent Coordination 3 CRITICAL Supervisor routing, agent debate, result synthesis

Alternative Frameworks 3 HIGH CrewAI crews, AutoGen teams, framework comparison

Multi-Scenario 2 MEDIUM Parallel scenario orchestration, difficulty routing

Total: 10 rules across 4 categories

Quick Start

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 toolsaction.name history += f"\nObservation: {result}\n" return "Max steps reached without answer"

Supervisor with fan-out/fan-in

async def multi_agent_analysis(content: str) -> dict: agents = [("security", security_agent), ("perf", perf_agent)] tasks = [agent(content) for _, agent in agents] results = await asyncio.gather(*tasks, return_exceptions=True) return await synthesize_findings(results)

Agent Loops

Patterns for autonomous LLM reasoning: ReAct (Reasoning + Acting), Plan-and-Execute with replanning, self-correction loops, and sliding-window memory management.

Key decisions: Max steps 5-15, temperature 0.3-0.7, memory window 10-20 messages.

Multi-Agent Coordination

Fan-out/fan-in parallelism, supervisor routing with dependency ordering, conflict resolution (confidence-based or LLM arbitration), result synthesis, and CC Agent Teams (mesh topology for peer messaging in CC 2.1.33+).

Key decisions: 3-8 specialists, parallelize independent agents, use Task tool (star) for simple work, Agent Teams (mesh) for cross-cutting concerns.

Alternative Frameworks

CrewAI hierarchical crews with Flows (1.8+), OpenAI Agents SDK handoffs and guardrails (0.7.0), Microsoft Agent Framework (AutoGen + SK merger), GPT-5.2-Codex for long-horizon coding, and AG2 for open-source flexibility.

Key decisions: Match framework to team expertise + use case. LangGraph for state machines, CrewAI for role-based teams, OpenAI SDK for handoff workflows, MS Agent for enterprise compliance.

Multi-Scenario

Orchestrate a single skill across 3 parallel scenarios (simple/medium/complex) with progressive difficulty scaling (1x/3x/8x), milestone synchronization, and cross-scenario result aggregation.

Key decisions: Free-running with checkpoints, always 3 scenarios, 1x/3x/8x exponential scaling, 30s/90s/300s time budgets.

Key Decisions

Decision Recommendation

Single vs multi-agent Single for focused tasks, multi for decomposable work

Max loop steps 5-15 (prevent infinite loops)

Agent count 3-8 specialists per workflow

Framework Match to team expertise + use case

Topology Task tool (star) for simple; Agent Teams (mesh) for complex

Scenario count Always 3: simple, medium, complex

Common Mistakes

• No step limit in agent loops (infinite loops)

• No memory management (context overflow)

• No error isolation in multi-agent (one failure crashes all)

• Missing synthesis step (raw agent outputs not useful)

• Mixing frameworks in one project (complexity explosion)

• Using Agent Teams for simple sequential work (use Task tool)

• Sequential instead of parallel scenarios (defeats purpose)

Related Skills

• ork:langgraph

• LangGraph workflow patterns (supervisor, routing, state)

• function-calling

• Tool definitions and execution

• ork:task-dependency-patterns

• Task management with Agent Teams workflow

Capability Details

react-loop

Keywords: react, reason, act, observe, loop, agent Solves:

• Implement ReAct pattern

• Create reasoning loops

• Build iterative agents

plan-execute

Keywords: plan, execute, replan, multi-step, autonomous Solves:

• Create plan then execute steps

• Implement replanning on failure

• Build goal-oriented agents

supervisor-coordination

Keywords: supervisor, route, coordinate, fan-out, fan-in, parallel Solves:

• Route tasks to specialized agents

• Run agents in parallel

• Aggregate multi-agent results

agent-debate

Keywords: debate, conflict, resolution, arbitration, consensus Solves:

• Resolve agent disagreements

• Implement LLM arbitration

• Handle conflicting outputs

result-synthesis

Keywords: synthesize, combine, aggregate, merge, summary Solves:

• Combine outputs from multiple agents

• Create executive summaries

• Score confidence across findings

crewai-patterns

Keywords: crewai, crew, hierarchical, delegation, role-based, flows Solves:

• Build role-based agent teams

• Implement hierarchical coordination

• Use Flows for event-driven orchestration

autogen-patterns

Keywords: autogen, microsoft, agent framework, teams, enterprise, a2a Solves:

• Build enterprise agent systems

• Use AutoGen/SK merged framework

• Implement A2A protocol

framework-selection

Keywords: choose, compare, framework, decision, which, crewai, autogen, openai Solves:

• Select appropriate framework

• Compare framework capabilities

• Match framework to requirements

scenario-orchestrator

Keywords: scenario, parallel, fan-out, difficulty, progressive, demo Solves:

• Run skill across multiple difficulty levels

• Implement parallel scenario execution

• Aggregate cross-scenario results

scenario-routing

Keywords: route, synchronize, milestone, checkpoint, scaling Solves:

• Route tasks by difficulty level

• Synchronize at milestones

• Scale inputs progressively