AI Agent Basics

Build production-grade AI agents with modern architectures and patterns.

When to Use This Skill

Invoke this skill when:

• Designing new AI agent systems
• Implementing ReAct or Plan-and-Execute patterns
• Building autonomous task-solving agents
• Integrating cognitive loops into applications

Parameter Schema

Parameter	Type	Required	Description	Default
task	string	Yes	What agent capability to build	-
architecture	enum	No	single, multi, hybrid	single
framework	enum	No	langchain, langgraph, custom	langgraph
complexity	enum	No	basic, intermediate, advanced	intermediate

Quick Start

# Basic ReAct Agent
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-sonnet-4-20250514")
agent = create_react_agent(llm, tools=[search, calculator])
result = await agent.ainvoke({"messages": [("user", "What is 25 * 4?")]})

Core Patterns

1. ReAct Agent

# Thought → Action → Observation loop
graph = StateGraph(AgentState)
graph.add_node("think", reason_node)
graph.add_node("act", action_node)
graph.add_node("observe", observation_node)

2. Plan-and-Execute

# Create plan → Execute steps → Verify
planner = create_planner(llm)
executor = create_executor(llm, tools)

3. Reflexion

# Execute → Reflect → Improve
agent_with_reflection = add_reflection_layer(base_agent)

Troubleshooting

Issue	Solution
Agent loops forever	Add max_iterations limit
Wrong tool selected	Improve tool descriptions
Context too large	Implement summarization
Slow responses	Use streaming

Best Practices

• Start with simple single-agent before multi-agent
• Always add circuit breakers (max iterations)
• Use verbose mode for debugging
• Implement human-in-the-loop for critical decisions

Related Skills

• llm-integration - LLM API configuration
• tool-calling - Function calling implementation
• agent-memory - Memory systems

References

• LangGraph Docs
• Anthropic Agent Patterns