I built an AI agent that kept forgetting what it was doing halfway through complex tasks.

Classic problem. The agent would start a 10-step research task, get to step 6, lose track of the original goal, and start going in circles.

Turns out, agent memory is a much harder problem than chatbot memory. A chatbot needs to remember the conversation. An agent needs to remember its plan, its progress, the results of previous steps, what failed and why, and what it should try next.

What finally worked: a structured scratchpad where the agent writes its current plan, updates it after each step, and reviews it before deciding the next action. Think of it as the agent's notebook.

Combined with: short-term buffer for the last 3-5 actions, a long-term store for completed subtask results, and explicit "reflection" steps where the agent pauses to assess whether it's still on track.

LangGraph's checkpointing handles the infrastructure. But the memory DESIGN — what to remember, when to forget, how to summarize — that's the engineering challenge.

The agents that feel intelligent aren't the ones with the best language model. They're the ones with the best memory architecture. This is the frontier of agent development right now.