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Most AI failures aren’t the model: Memory and Context Explained
Modern systems are not: Ask model → get answer
What they are is: Run a reasoning loop, use tools, update memory, validate results, and generate outputs.
Claude Code is an amazing product, in part because of great models, but also due to the application itself. It does a fantastic job of managing memory and context.
There are many other AI apps that demo really well, but then start to break down after you use them for a while. After a recent OpenClaw release, users once again encountered issues around memory bloat and context degradation. I’ve watched my OpenClaw burn through tokens. These are real challenges that every AI system faces.
Responses drift. The system forgets things you just told it. Even conversations that worked in testing start to go sideways. The first thought is usually to tweak the prompt. Perhaps upgrade to the latest model. But these things don’t always fix the issue.
Thus, the performance of your system is highly dependent on the workflow, your agent loop, and managing state. It’s not just model size or training anymore. Now you are dealing with a multi-layer problem, two key elements of which are context and memory. Until you get these issues right, your system is liable to break in unexpected ways.
