Legacy infrastructure, not AI models, is the real bottleneck slowing down the performance of AI agents. At VB Transform 2026, leaders from LinkedIn, Walmart, and Zendesk shared insights on how their companies tackled this challenge as they moved AI agents from pilot phases to full production. The core issue? Traditional enterprise systems are built for human workflows, which operate on a much slower timescale than AI agents. LinkedIn had to redesign their container provisioning strategy and control flows to reduce lag from seconds to milliseconds. Walmart faced an internal surge of non-engineer “citizen developers” creating overlapping AI agents, prompting them to establish governance and streamline production without slowing innovation. Zendesk wrestled with massive historical customer data, realizing that success required robust data pipelines rather than merely feeding large datasets into language models. All three stressed the importance of owning core infrastructure while selectively integrating frontier AI advancements. Their advice: invest early in evaluation systems, empower employees with AI tools paired with close monitoring, and build infrastructure to remain flexible and model-agnostic for future changes.
Back
