The bottleneck for AI agents isn’t the model anymore. It’s the context layer.

The New Stack · 4 hours ago

AI agent reliability problems stem from insufficient infrastructure around context management, tool retrieval, and execution guardrails rather than model capability limitations. Teams building production agents must implement compiled context layers that structure organizational knowledge (Karpathy's wiki reached 100 articles and 400,000 words), hypothetical-invocation matching for tool selection instead of semantic similarity, and execution isolation layers that validate every tool call before it reaches downstream systems. The differentiator between reliable and unreliable agent systems is investment in context plumbing, observability, continuous evaluation against production data, and configuration management—not upgrading to smarter models.