Rahul Sengottuvelu

This talk proposes a paradigm shift in building AI agents, advocating for systems that scale with compute rather than rigid, fixed architectures. The core idea is that systems designed to leverage increasing computational power will inherently outperform those that do not, drawing parallels to historical advancements in fields like chess and computer vision where brute-force search eventually surpassed human-designed heuristics. This approach suggests that by embracing the exponential improvements in AI models, developers can build more robust and adaptable agent systems with less manual engineering effort.