An AI agent can look brilliant for ten minutes and lost after ten steps.
It starts with a clean plan. Then the agent reads docs, calls tools, rewrites files, summarizes a customer ticket, checks a policy, and tries to continue. Somewhere in that loop, it forgets why a decision was made. It repeats a tool call. It trusts an old fact. It pulls the wrong tenant preference. The output still sounds confident, but the job has drifted.
That is not only a model problem. It is a memory design problem.
If you are building production AI workflows, you need more than a bigger context window. You need an AI agent memory store: a controlled system for deciding what the agent remembers, what it forgets, what it retrieves, and what it is allowed to use.
Why Agent Memory Is Suddenly a Production Problem
