 ## Why AI Agents Get 'Dumber' on Long Tasks — and How We Fixed It An article on Habr hit the top within a day: 'Cursor Broke Everything, But It's Not Cursor's Fault.' The author describes how Cursor on Claude Opus 4.6 deleted the PocketOS production database — because when filling the context, the AI agent performs summarization, loses critically important details, and makes catastrophic decisions. What's the root of the problem? Any LLM operates with a limited context window. When an agent performs a long task — analyzing code, conducting correspondence, building reports — the context gets clogged, and the model is forced to compress information. The result: missed bugs, incorrect conclusions, 'hallucinations.' Google recently introduced TurboQuant — compressing the KV cache to 3 bits per value (5 times less memory). A cool technology, but it solves the problem of inference speed and memory, not the quality of context retention. How ASI Biont Approaches This Differently We don't just compress context — we structure it. Our AI agents work on the principle of modular memory: 1. Context separation — each subtask gets its own isolated window, rather than being dumped into a common pile 2. Verification anchors — before a critical action, the agent rechecks key facts from 'long-term' memory 3. Verification chains — instead of one summarization, the agent builds several independent conclusions and cross-checks them Result: our agents analyze 100+ pages of documentation or 50,000 lines of code without quality degradation. No deleted databases. No 'broken' production servers. Try It Yourself — 1500 Tokens to Start Launch an AI agent that doesn't get 'dumber' on long tasks. Follow the link, register, and get 1500 tokens for your first run. → https://asibiont.com/