AI Agents

AI agents are LLM-powered systems that can plan, use tools, and take multi-step actions to complete complex tasks. In 2026, agent frameworks have matured enough for production use — here's how to build reliable ones.

Framework Comparison

Core Agent Architecture

Every agent needs four things:

1. LLM backbone — the model that reasons and decides (GPT-4o, Claude 3.5, Llama 3.3)
2. Tools — functions the agent can call (web search, code execution, database queries)
3. Memory — short-term (conversation history) and long-term (vector store)
4. Orchestration — the loop that decides when to call tools vs return a final answer

Quick Start with LangGraph

from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langchain_community.tools.tavily_search import TavilySearchResults

llm = ChatOpenAI(model="gpt-4o")
tools = [TavilySearchResults(max_results=3)]

agent = create_react_agent(llm, tools)
result = agent.invoke({"messages": [("user", "What's the latest on LangGraph?")]})
print(result["messages"][-1].content)

Learning Path

1. Single-tool ReAct agent — understand the think → act → observe loop
2. Multi-tool agent — add web search, code execution, database access
3. Memory patterns — conversation buffer, vector store for long-term recall
4. Multi-agent systems — supervisor + worker pattern with CrewAI or LangGraph
5. Human-in-the-loop — approval gates, interrupt and resume
6. Production — streaming, error recovery, observability with LangSmith