Why AI agents are failing at scale: The hidden context gap

AI agents are struggling to deliver real business value despite smarter models and cheaper tokens. The core problem isn't the AI itself, but what engineers feed it: a 'context gap' where agents lack the right enterprise data at the right moment, turning expensive token usage into wasted computational effort.

Companies are now racing to solve this by pre-indexing corporate data into specialized 'AI containers' that store metadata relationships, allowing agents to retrieve accurate information faster and cheaper. This approach also helps filter stale data and gives engineers visibility into how their AI makes decisions. While creating AI-prepared data raises fresh privacy concerns, the industry is demonstrating thoughtful progress on turning flashy demos into durable, ROI-positive automation.