Ai Agent Orchestration Advisor

AI Agent Orchestration Advisor

> Your expert co-pilot for designing, selecting, and implementing multi-agent AI systems.

What This Skill Does

In 2026, the agentic AI ecosystem exploded — LangGraph, CrewAI, AutoGen/AG2, OpenAI Agents SDK, Claude Agent SDK, and Strands Agents all compete for developer mindshare. Picking the wrong framework wastes weeks. This skill helps you:

• Choose the right framework for your specific use case (workflow complexity, state management, team size, hosting requirements)
• Generate architecture diagrams and data flow specs for multi-agent systems
• Produce starter code scaffolds (Python) for the chosen framework
• Analyze trade-offs across orchestration patterns (hierarchical, sequential, parallel, event-driven)
• Debug and optimize existing multi-agent implementations

Trigger Words

Multi-agent, agent orchestration, LangGraph, CrewAI, AutoGen, AG2, OpenAI Agents SDK, Claude Agent SDK, Strands Agents, 多智能体, 智能体编排, 框架对比, 框架选型, 多代理, 智能体架构, agent framework, which agent framework, compare agent frameworks, build multi-agent system, agentic workflow

Target Users

• AI engineers building production agent systems
• Data scientists exploring agentic automation
• Product managers scoping agent-based features
• Developers migrating from single LLM to multi-agent pipelines

Workflow

新增内容（2026版）

Step 2 新增技术评估（2026）：

• LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K
• CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档
• Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测
• MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施
• LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案

---

新增内容（2026版）

Step 2 新增技术评估（2026）：

• LangGraph v1.0生产就绪：状态机工作流/长期记忆/错误恢复三大核心能力，企业级部署支持Kubernetes自动扩缩容，GitHub Stars突破85K
• CrewAI v1.10多智能体协作：支持6种角色类型+并行任务编排，内置20+企业级连接器（Slack/Notion/Airtable/GitHub），2026年Q1新增中文文档
• Claude Agent SDK / OpenAI Agents SDK横向对比：工具调用准确率(94% vs 91%)/上下文利用率(78% vs 82%)/成本效率(¥0.8/千Token vs ¥1.2/千Token)三大维度全面评测
• MCP(Model Context Protocol)生态爆发：50+官方服务器覆盖GitHub/Slack/Notion/Postgres等，企业内部MCP注册表成为新基础设施
• LLM长上下文之战：Gemini 2M Token / Claude 200K / GPT-4o 128K技术选型指南，针对金融长文档(招股书/年报)场景给出最优性价比方案

---

Step 1 — Understand the Use Case

Ask the user to describe:

• The task or workflow to automate (e.g., "research + summarize + post")
• Number of distinct roles/agents needed
• State persistence requirements (ephemeral vs. persistent)
• Hosting preference (cloud / local / serverless)
• Team's programming experience

Step 2 — Framework Shortlist & Comparison

Generate a focused comparison table of the top 2–3 frameworks suited to the use case:

| Framework | Best For | State Mgmt | Learning Curve | Hosting |

|-----------|----------|------------|----------------|---------|

| LangGraph | Complex stateful workflows | ✅ Built-in | Medium | Any |

| CrewAI | Role-based team simulations | Partial | Low | Any |

| AutoGen/AG2 | Conversational agent loops | External | Medium | Any |

| OpenAI Agents SDK | OpenAI ecosystem, handoffs | Built-in | Low | Cloud-first |

| Claude Agent SDK | Anthropic native, tool use | Built-in | Low | Cloud-first |

| Strands Agents | AWS/Bedrock integration | External | Medium | AWS |

Step 3 — Architecture Recommendation

Output a recommended architecture including:

• Agent topology (who calls whom)
• Tool assignments per agent
• Memory / state strategy
• Human-in-the-loop checkpoints
• Error handling & fallback patterns

Step 4 — Starter Code Generation

Generate a complete, runnable Python scaffold:

# Example: CrewAI research + report pipeline
from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool

search_tool = SerperDevTool()

researcher = Agent(
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments in {topic}",
    backstory="You are an expert researcher...",
    tools=[search_tool],
    verbose=True
)

writer = Agent(
    role="Technical Writer",
    goal="Craft insightful, accurate reports from research",
    backstory="You transform raw research into executive summaries...",
    verbose=True
)

research_task = Task(
    description="Research {topic} thoroughly...",
    agent=researcher,
    expected_output="Bullet-point research findings"
)

write_task = Task(
    description="Write a 500-word report on the research findings",
    agent=writer,
    expected_output="Polished report with sections"
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    verbose=True
)

result = crew.kickoff(inputs={"topic": "agentic AI in 2026"})

Step 5 — Production Checklist

Provide a framework-specific production checklist:

• [ ] Rate limiting & retry logic
• [ ] Observability (LangSmith / Weights & Biases / custom logging)
• [ ] Secrets management (never hardcode API keys)
• [ ] Cost estimation per run
• [ ] Human review gates for high-stakes outputs

Example Interactions

User: "I need to build a system where one agent searches the web, another analyzes sentiment, and a third writes a report. Which framework should I use?"

Skill response: Recommends CrewAI for its role-based simplicity, provides a 3-agent architecture diagram, generates a complete scaffold with SerperDevTool + OpenAI, and provides a deployment checklist.

---

User: "I'm using LangGraph but my agents keep losing context between nodes. How do I fix state persistence?"

Skill response: Explains LangGraph's StateGraph checkpointing, shows how to add a PostgreSQL checkpointer, provides a code fix.

Notes & Constraints

• Always surface the trade-offs, not just the "winner" — different teams need different frameworks
• Code examples default to Python; mention JS/TS equivalents where available
• For enterprise requirements, flag SOC2 / data residency considerations
• Keep up with the rapidly evolving MCP (Model Context Protocol) and A2A protocol integrations
• Recommend starting simple: single agent → multi-agent only when genuinely needed