AI agents operate at millisecond speeds, but the infrastructure supporting them remains stuck in legacy systems built for slower workloads. That fundamental mismatch emerged as the real bottleneck when three major companies scaled agents from pilots to production.

At VB Transform 2026, infrastructure leaders from LinkedIn, Walmart, and Zendesk described identical problems that had nothing to do with model capabilities. Animesh Singh, senior director of AI platform and infrastructure at LinkedIn, Desiree Gosby, SVP of corporate technology services at Walmart, and Sami Ghoche, VP of applied AI at Zendesk, each encountered production failures rooted in aging systems.

The issue cuts deeper than performance tuning. Legacy databases, message queues, API layers, and monitoring tools designed for human-paced interactions cannot handle agent decision loops executing thousands of times per second. When agents moved from controlled pilots to real-world workloads, these systems collapsed under the throughput demands.

LinkedIn faced latency spikes when agents queried databases built for batch processing. Walmart encountered bottlenecks in its transaction handling infrastructure. Zendesk discovered that traditional logging and observability systems could not track agent execution traces fast enough to diagnose problems.

The solution required rearchitecting infrastructure stacks. Companies needed to upgrade databases to handle higher query volumes, implement streaming architectures for real-time data flows, and deploy observability tools designed for agent telemetry. Some replaced synchronous API calls with asynchronous patterns. Others partitioned workloads to distribute agent requests across multiple systems.

This pattern repeats across the industry. Companies investing in models without modernizing infrastructure hit the same walls. The mismatch between agent speed and system responsiveness becomes the constraint, not model quality or reasoning capability.

For enterprises planning agent deployments, the lesson is clear. Infrastructure assessment must precede model selection