As enterprises rush to deploy AI agents, many discover that the model is only part of the equation. The 'Agent Harness'—the layer that schedules, executes, and manages model interactions—often determines real-world performance. This article argues that companies focusing solely on model upgrades miss the bigger bottleneck: data infrastructure. A data-native agent architecture, where the harness is tightly coupled with storage and query engines, can reduce latency, improve context accuracy, and scale more reliably. For engineering leaders, this shifts the conversation from 'which model' to 'how we feed and orchestrate it.' The practical takeaway is that investing in data plumbing and agent orchestration may yield higher returns than chasing the next model release.
Enterprise AI agents underperform not because of model limits but due to weak orchestration and data infrastructure. This analysis explores the 'Agent Harness' concept and why data-native design is the next competitive frontier.