"The Longer You Use It, the Dumber It Gets"... The Solution to AI Agent Memory Problems Is This

AI Agents Forget Older Information as Conversations Lengthen
'Memory Engineering' Emerges as a Solution

As AI agents (virtual assistants) have revealed performance degradation during prolonged conversations with users, memory management is emerging as a key challenge in the AI industry.

According to the AI industry on April 21, companies such as OpenAI and Anthropic are increasing the context window—the amount of information AI models can retain during conversations—but there are still limitations. Claude Opus 4.7, ChatGPT-5.4, and Gemini 3.1 Pro currently support up to 1 million tokens, which is equivalent to about 50,000 lines of code, eight English novels, or roughly 200 minutes of podcast content.

When AI models exceed this limit, they begin deleting the oldest information, causing them to forget the initial parts of the conversation. This leads to 'context rot,' where the AI forgets initially agreed-upon conditions with the user or starts with a blank slate when a new conversation begins. As a result, the AI fails to maintain the core functions of a personal assistant, which needs to remember conversations and adapt in real time to the user's work patterns and style. This can be a critical weakness in areas where long texts or contextual continuity are essential, such as customer support and legal analysis.

There are also issues with performance degradation when large language models (LLMs) handle long-term conversations. According to a paper published this month on the open-access academic platform arXiv by the Full Loop research team, AI agents tend to become less accurate as conversations get longer, due to increased retrieval noise and memory errors. In the LOCOMO test, which measures the long-term memory of AI agents, accuracy dropped sharply from 0.455 to as low as 0.05 in some cases.

The industry is proposing 'memory engineering' as a solution to these memory issues. This involves creating external memory systems that store data outside the AI model and allow only the necessary parts to be retrieved as needed. OpenAI co-founder Andrej Karpathy recently released open-source tools that generalize the idea of 'AI autonomous experiments,' presenting an external memory solution for agents.

Cloud infrastructure company Cloudflare also announced a managed service on April 17 that enables information to be extracted and used when needed. The service organizes memory lists by function: individual agent memory for persistent memory layers, custom agent harness memory that is retained after restarts, and profile memory that shares rules and internal knowledge, among others.

Global data and AI company Databricks has suggested memory expansion as another solution. By providing agents with external memory, it acts as a permanent information store that only interacts with the agent at inference time. Databricks explained that this could address the problem of insufficient memory capacity.

Some analysts say effective memory engineering requires robust context management capabilities. Market research firm Gartner advised, "Context serves as the brain for AI," and emphasized the need to redesign data analysis and interpretation structures so that AI agents can reliably provide context.

This content was produced with the assistance of AI translation services.