The Context Bottleneck: Why AI Agents Struggle to Scale Despite Better Models
As language models improve and token costs decline, enterprises discover that feeding agents the right information at the right time remains the critical barrier to moving beyond proof-of-concept deployments.
Language models continue to advance, token pricing keeps falling, and AI agents show promise. Yet a fundamental challenge persists: the inability to efficiently provide agents with the context they need, preventing many organizations from achieving meaningful returns on their AI spending.
Vendors are moving quickly to address this gap. The Silicon Ledger recently spoke with Sri Desikan, an Elastic executive responsible for search initiatives, about the nature of the context problem and Elastic's approach to solving it.
Understanding the Context Gap
The challenge is straightforward to articulate. According to Desikan, "frontier models are getting better and better and better," yet without supplying them with appropriate context, "all that money you're spending on tokens is going to waste." Delivering agents the data they require, precisely when required, and without delay, represents the path from flashy prototypes to reliable, production-grade automation.
Enterprise organizations are experiencing the consequences. While AI models can query MCP servers for information, simply linking a company's data repository to a connection point falls short. Dumping "all this data into [a model's] context window," Desikan contends, creates two problems: it consumes substantial computational resources (translating to higher token expenditure) and it introduces latency.
The Solution: Pre-Indexed Context
The answer involves structuring enterprise information into an AI-ready index that understands data location and interconnections. Desikan characterized this approach as establishing a "container which stores metadata about everything in your enterprise." With this structure in place, AI agents retrieve pertinent information more rapidly and with substantially reduced token consumption—in certain cases, dramatically lower.
An additional benefit emerges from pre-computing context in this manner. By following Elastic's methodology, organizations can "make sure that stale information" remains excluded, enabling companies to "know how the LLM is going to think."
Emerging Concerns
Using AI systems to prepare data for consumption by other AI agents introduces new questions around privacy and agent autonomy. While these issues warrant serious consideration, the broader picture suggests that industry solutions to these novel challenges are maturing rapidly. Problems that seem intractable today may well be resolved within quarters.