Entry point: /faion-net — invoke this skill for automatic routing to the appropriate domain.

AI Agents Skill

Communication: User's language. Code: English.

Purpose

Specializes in AI agent development and orchestration. Covers autonomous agents, multi-agent systems, frameworks, and MCP.

Context Discovery

Auto-Investigation

Check these project signals before asking questions:

Discovery Questions

question: "What type of agent are you building?"
header: "Agent Architecture"
multiSelect: false
options:
  - label: "Single autonomous agent"
    description: "One agent with tools (ReAct, plan-and-execute)"
  - label: "Multi-agent system"
    description: "Multiple agents collaborating/delegating"
  - label: "Agentic RAG"
    description: "Agent-driven document retrieval"
  - label: "MCP integration (Claude tools)"
    description: "Model Context Protocol for Claude Code"

question: "Which agent framework?"
header: "Framework"
multiSelect: false
options:
  - label: "LangChain"
    description: "Most mature, extensive tooling"
  - label: "LlamaIndex"
    description: "Best for data/document agents"
  - label: "Custom implementation"
    description: "Direct API calls to LLM"
  - label: "Claude MCP (native)"
    description: "Claude-native tool protocol"

question: "What tools/capabilities does the agent need?"
header: "Agent Capabilities"
multiSelect: true
options:
  - label: "Web search"
    description: "Search internet for information"
  - label: "Code execution"
    description: "Run Python/JS code safely"
  - label: "Database queries"
    description: "Query SQL/NoSQL databases"
  - label: "API calls"
    description: "Call external REST/GraphQL APIs"
  - label: "File operations"
    description: "Read/write files, search codebase"

Scope

Quick Start

Methodologies (26)

Agent Fundamentals (4):

• ai-agent-patterns: Core patterns, memory, planning
• agent-patterns: ReAct, chain-of-thought, reflection
• agent-architectures: System design, components
• autonomous-agents: Loops, decision-making, persistence

Multi-Agent (4):

• multi-agent-basics: Fundamentals, communication
• multi-agent-patterns: Delegation, collaboration
• multi-agent-design-patterns: Hierarchical, peer-to-peer

LangChain (7):

• langchain-basics: Setup, chains, components
• langchain-chains: LCEL, sequential, routing
• langchain-memory: Conversation, summary, entity
• langchain-workflows: Complex flows, branching
• langchain-agents-architectures: Agent types, tools
• langchain-agents-multi-agent: Multi-agent with LangChain
• langchain-patterns: Production patterns

LlamaIndex (3):

• llamaindex-basics: Data connectors, indexes
• llamaindex-indexes-queries: Query engines, retrievers
• llamaindex-agents-eval: Agent implementation, evaluation

MCP & Tooling (4):

• mcp-model-context-protocol: Protocol fundamentals
• model-context-protocol: Specification
• mcp-ecosystem: Available servers, tools
• mcp-ecosystem-2026: Latest developments

Governance (2):

• ai-governance-compliance: Frameworks, best practices
• eu-ai-act-compliance: Risk tiers, requirements
• eu-ai-act-compliance-2026: Latest updates

Advanced (2):

• agentic-rag: Agent-driven retrieval (duplicated in RAG)
• reasoning-first-architectures: Extended thinking patterns

Agent Architectures

ReAct Pattern

Input → Thought → Action → Observation → Thought → ... → Answer

Plan-and-Execute

Input → Plan → Execute Step 1 → Execute Step 2 → ... → Synthesize

Reasoning-First

Input → Extended Thinking → Plan → Execute → Answer

Code Examples

Basic ReAct Agent (LangChain)

from langchain.agents import create_react_agent, AgentExecutor
from langchain_openai import ChatOpenAI
from langchain.tools import Tool

tools = [
    Tool(
        name="Calculator",
        func=lambda x: eval(x),
        description="Math calculator"
    )
]

llm = ChatOpenAI(model="gpt-4o")
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = executor.invoke({"input": "What is 25 * 17?"})

Multi-Agent System

from langchain.agents import initialize_agent, Tool
from langchain_openai import ChatOpenAI

# Define specialized agents
researcher = ChatOpenAI(model="gpt-4o")
writer = ChatOpenAI(model="gpt-4o")

# Orchestrator delegates tasks
orchestrator = initialize_agent(
    tools=[
        Tool(name="research", func=research_agent),
        Tool(name="write", func=writer_agent)
    ],
    llm=ChatOpenAI(model="gpt-4o"),
    agent="zero-shot-react-description"
)

result = orchestrator.invoke("Research AI trends and write a summary")

MCP Server Integration

import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=[{
        "name": "get_weather",
        "description": "Get weather data",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            }
        }
    }],
    messages=[{"role": "user", "content": "Weather in NYC?"}]
)

LlamaIndex Agent

from llama_index.agent import ReActAgent
from llama_index.llms import OpenAI
from llama_index.tools import QueryEngineTool

llm = OpenAI(model="gpt-4o")

tools = [
    QueryEngineTool.from_defaults(
        query_engine=query_engine,
        name="docs",
        description="Documentation search"
    )
]

agent = ReActAgent.from_tools(tools, llm=llm)
response = agent.chat("How do I use embeddings?")

Multi-Agent Patterns

MCP Ecosystem (2026)

EU AI Act Compliance

Related Skills

AI Agents v1.0 | 26 methodologies