Agent workflows

Agent workflows replace rigid, linear automation with autonomous loops where AI agents reason, use tools, and self-correct to hit high-level goals.

Modern agent workflows move beyond standard if-then logic by giving LLMs the autonomy to plan and execute multi-step tasks. Instead of a fixed script, an agent uses a reasoning loop (like the ReAct pattern) to assess a goal, select the right tool (such as a CRM API or SQL database), and evaluate the output. This shift allows teams to automate complex, non-linear processes like multi-document contract analysis or adaptive lead qualification. By implementing frameworks like LangGraph or CrewAI, developers can build stateful systems that maintain context over long-running tasks and include human-in-the-loop checkpoints for high-stakes decisions.

https://www.langchain.com/langgraph