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	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 (0.7.0), 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