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 tools[action.name](*action.args)
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 (0.12+), 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 executionork: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