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Agents think in milliseconds, legacy infrastructure doesn't. LinkedIn, Walmart, and Zendesk shared how they closed the gap at VB Transform 2026
Enterprise agent adoption is being throttled by legacy infrastructure gaps rather than model intelligence, according to LinkedIn, Walmart, and Zendesk.
What: To bridge the gap, these firms are moving toward deterministic code for orchestration, strict internal governance of redundant agents, and portable, model-agnostic execution harnesses.
Why it matters: This underscores that the primary challenge for enterprise AI is no longer the LLM, but the reliability and integration of the surrounding engineering systems.
Takeaway: Shift your focus from experimenting with new model weights to building a portable, deterministic evaluation and execution harness for your agents.
Decoder
- Agent harness: The scaffolding code that manages the lifecycle of an AI agent, including input sanitization, tool invocation, and error handling.
Original article
Enterprise agents are constrained more by legacy infrastructure than model quality. LinkedIn pre-provisioned containers and shifted 80% of orchestration to deterministic code, Walmart governed duplicate internal agents, and Zendesk strengthened data pipelines for its 20B conversations. The lesson is to invest in evals, own the agent harness, and keep workloads portable across models.
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