Imported from yonatangross/orchestkit (src/skills/agent-orchestration/SKILL.md) via skills.sh. Install upstream with npx skills add yonatangross/orchestkit --skill agent-orchestration. Copyright stays with the author (MIT).

Agent Orchestration

Comprehensive patterns for building and coordinating AI agents -- from single-agent reasoning loops to multi-agent systems and framework selection. Coordination and multi-scenario categories have individual rule files in rules/ loaded on-demand; loop and framework tutorials live upstream (see Upstream coverage), with house defaults in references/ork-delta.md.

CC native /workflows (2.1.154): Claude Code now ships dynamic workflows — ask Claude to create a workflow and it orchestrates tens-to-hundreds of agents in the background; view runs with /workflows. This is complementary to the patterns here: use CC /workflows for large-scale, fire-and-forget background fan-out (you check back later); use the bounded foreground Agent Teams / Task-tool patterns below when ≤8 agents must coordinate within a single skill invocation via shared memory (handoff files, mesh messaging). Different scale, not a replacement.

Ask only when genuinely blocked (CC 2.1.154): CC now reserves the multiple-choice question prompt for decisions it genuinely cannot make itself, rather than asking when it already has enough context to proceed. When orchestrating agents, don't gate progress on an AskUserQuestion the lead can resolve from available context — reserve prompts for true branch points (irreversible actions, missing requirements). This complements ork's voice-friendly decision guidance.

Quick Reference

Total: 4 rules across 4 categories. Loop and framework tutorials moved to first-party sources; the rescued house defaults live in references/ork-delta.md.

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.

Upstream coverage (do not restate)

Local tutorials for these topics were retired; consult the first-party source and keep only house deltas in references/ork-delta.md.

References

• references/ork-delta.md - House defaults and dated decisions rescued from retired tutorials
• references/framework-comparison.md - Condensed framework decision matrix and use-case table
• references/langgraph-implementation.md - LangGraph 1.2+ implementation of the multi-scenario orchestrator
• references/claude-code-instance-management.md - Running 3 parallel Claude Code instances for scenario demos

Key Decisions

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)
  • Note (CC 2.1.161): parallel tool calls now fail independently — a failed Bash no longer cancels siblings in the same batch. This caveat still applies at the agent-orchestration level, not to tool batches; claude agents rows now show done/total for fanned-out work.
  • Note (CC 2.1.157): claude agents honors the agent field in settings.json for dispatched sessions; --agent <name> overrides it — pin the agent type explicitly when dispatching.
• 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