Planning for Agents

AI agents often struggle with reliable planning, which involves deciding on both immediate actions and a sequence of long-term steps to complete a task. To overcome this, developers are building custom "cognitive architectures" that hardcode the reasoning logic for the agent. The most successful agents use domain-specific architectures tailored to a particular problem, effectively removing the burden of high-level planning from the LLM. While models will become better at general reasoning over time, these custom architectures, like those built with LangGraph, will remain crucial for creating controllable and dependable agents for specialized tasks.