The context problem: It’s what’s really breaking AI agents at scale

The context problem is the primary obstacle preventing AI agents from reaching their full potential in the modern enterprise. Despite AI models improving rapidly and token costs decreasing, the context gap remains an issue that many companies struggle with. According to Sri Desikan, an Elastic executive, AI models require proper context to function effectively. If this context is lacking, companies will waste money on tokens without seeing the desired results.

To bridge this context gap, companies must provide AI agents with the information they need promptly and efficiently. However, simply connecting a company's data to an AI model's context window is insufficient. Desikan explains that doing so is both computationally expensive and time-consuming. Therefore, a better solution is needed.

Desikan suggests pre-conforming corporate data into an AI index that contains metadata about all data stored within the enterprise. In this way, AI agents can quickly access correct and relevant information while minimizing token usage. This approach would significantly improve the performance of AI agents and help companies realize the return on investment they expect from their AI initiatives.

However, there's an additional layer to consider. Pre-computing AI context using an AI index helps ensure that stale or outdated information isn't included, allowing companies to gauge how an LLM will think about a particular task. This feature raises important questions about privacy and agentic autonomy, which Desikan and I delved into further.

While these concerns are valid, the article concludes on a positive note, highlighting how industry approaches to novel problems are often well-thought-out and may not persist much longer than the current quarter.