AI agents are being slowed down not by the models themselves, but by legacy infrastructure. Leaders from LinkedIn, Walmart, and Zendesk shared this conclusion at VB Transform 2026. Their experiences revealed that enterprise infrastructure, built for human workflows, struggles with the speed of AI agents.At LinkedIn, Kubernetes provisioning was too slow, requiring a shift to pre-provisioned containers. A second issue involved LLMs evaluating other LLMs, leading to hallucinations. LinkedIn addressed this by scripting most of the workflow and using LLMs only for reasoning.Walmart faced a bottleneck from overwhelming internal demand for agents, leading to duplication. Their solution involved building governance to manage and deploy agents efficiently. Zendesk encountered challenges with massive customer conversation data, necessitating investment in robust data pipelines.All three companies emphasized owning their AI infrastructure where possible, relying on external providers only for specialized frontier work. LinkedIn developed an AI gateway and a model-independent memory subsystem. Walmart created an internal gateway to maintain vendor agnosticism across different workflow types.Their advice includes investing in evaluation systems early, owning the agent harness from the start, and building infrastructure for model and context independence. This approach ensures flexibility and allows companies to adapt to future AI advancements. Ultimately, the focus should be on adapting infrastructure to accommodate AI agent capabilities effectively.