AI's Context Problem: Experts Highlight Challenges in Delivering Real-Time Results and Securing Agentic Systems
Large language models (LLMs) are facing significant challenges in delivering real-time results and securing agentic systems, according to recent reports and expert opinions. While LLMs excel at reasoning, they often struggle with context, hindering their ability to provide truly assistive experiences, particularly in dynamic environments like real-time ordering systems. Simultaneously, concerns are growing around the security of agentic systems, prompting calls for robust governance and boundary controls.
The "brownie recipe problem," as Instacart CTO Anirban Kundu described it, exemplifies the context challenge. According to VentureBeat, it's not enough for an LLM to simply understand a request to make brownies. To be truly helpful, the model must factor in user preferences, market availability (organic vs. regular eggs), and geographical constraints to ensure deliverability and prevent food spoilage. Instacart aims to juggle latency with the right mix of context to provide experiences in under one second.
This lack of context extends beyond ordering systems. Raju Malhotra of Certinia, in VentureBeat, argued that the failure of many AI pilot programs to deliver promised results stems from a lack of context, not a lack of intelligence in the models themselves. He attributed this to "Franken-stacks" of disconnected point solutions, brittle APIs, and latency-ridden integrations that trap context within disparate technologies.
Adding to the complexity, MIT Technology Review highlighted the exponential rate at which certain AI capabilities are developing, as tracked by the AI research nonprofit METR. While advancements are rapid, the need for secure implementation remains paramount.
The increasing sophistication of AI agents has raised concerns about potential risks. MIT Technology Review reported on the first AI-orchestrated espionage campaign and the failure of prompt-level control. In response, experts are advocating for treating agents like powerful, semi-autonomous users and enforcing rules at the boundaries where they interact with identity, tools, data, and outputs. Protegrity, in MIT Technology Review, outlined an eight-step plan for CEOs to implement and report against, focusing on governing agentic systems at the boundary through three pillars of control.
Furthermore, the energy demands of AI are also coming into focus. MIT Technology Review noted the unprecedented investment in massive data centers to support AI's computational appetite. Next-generation nuclear power plants are being considered as a potential source of electricity for these facilities, offering potentially cheaper construction and safer operation compared to older models. This was a key topic discussed in a recent subscriber-exclusive Roundtables discussion on hyperscale AI data centers and next-gen nuclear.
The challenges surrounding AI development, from contextual understanding to security and energy consumption, highlight the need for a multi-faceted approach. As AI continues to evolve, addressing these issues will be crucial for realizing its full potential while mitigating potential risks.
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