The Hidden Fragility of AI Agents: Why Vector Retrieval and Long Context Windows Aren’t Enough

The Hidden Fragility of AI AgentsThe race to build autonomous AI agents that can reliably plan, reason, and act in the real world is heating up. Tech companies are investing billions in developing systems that can go beyond answering questions to becoming trusted digital assistants that handle complex tasks with minimal human supervision.Two technological approaches have dominated this pursuit: vector-based retrieval (like RAG systems) for accessing knowledge, and expanding context windows in large language models to process more information at once. Both approaches seem intuitive — give AI systems more information and more capacity to process it, and surely they’ll become more capable agents.But recent research reveals concerning limitations that challenge these assumptions.