Agentic AI is reshaping how we think about software. Instead of writing rigid pipelines, we now compose agents that plan, reason, and call tools to accomplish goals.
Why agentic frameworks matter
Traditional LLM apps treat the model as a single function call. Agentic frameworks like LangGraph treat it as a node in a graph — with memory, branching, and tool use.
- State graphs let you model long-running decisions
- Tool calling turns the LLM into an orchestrator
- Human-in-the-loop checkpoints add safety
A simple example
from langgraph.graph import StateGraph
graph = StateGraph(MyState)
graph.add_node("plan", plan_node)
graph.add_node("execute", execute_node)
graph.add_edge("plan", "execute")
That's enough to start building. The real power comes when you add conditional edges and persistent state.