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:

Signal	Where to Check	What to Look For
Dependencies	package.json, requirements.txt	langchain, llamaindex, anthropic (MCP)
Agent code	Grep for "agent", "tool", "ReAct"	Existing agent implementations
MCP config	mcp.json, claude_desktop_config.json	MCP servers configuration
Tools/functions	Grep for "function", "tool_def"	Available agent tools

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

Area	Coverage
Agent Patterns	ReAct, plan-and-execute, reasoning-first
Autonomous Agents	Agent loops, memory, tool use
Multi-Agent	Coordination, communication, delegation
Frameworks	LangChain, LlamaIndex agent implementations
MCP	Model Context Protocol, Claude tools
Governance	EU AI Act compliance, safety

Quick Start

Task	Files
Basic agent	ai-agent-patterns.md → agent-patterns.md
Autonomous agent	autonomous-agents.md → agent-architectures.md
Multi-agent	multi-agent-basics.md → multi-agent-patterns.md
LangChain agents	langchain-agents-architectures.md
MCP integration	mcp-model-context-protocol.md → mcp-ecosystem-2026.md

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

Pattern	Use Case
Hierarchical	Manager delegates to specialists
Peer-to-Peer	Agents collaborate as equals
Sequential	Chain of agents, each refines
Parallel	Multiple agents work simultaneously

MCP Ecosystem (2026)

Server	Purpose
filesystem	File operations
postgres	Database queries
puppeteer	Web automation
github	GitHub API access
slack	Slack integration

EU AI Act Compliance

Risk Tier	Requirements
Unacceptable	Banned (social scoring, manipulation)
High-risk	Conformity assessment, documentation
Limited-risk	Transparency obligations
Minimal-risk	No obligations

Related Skills

Skill	Relationship
faion-llm-integration	Provides LLM APIs
faion-rag-engineer	Agentic RAG integration
faion-ml-ops	Agent evaluation

---

AI Agents v1.0 | 26 methodologies