SK Telecom treats AI agents as a managed workforce

What changed: SK Telecom published its first AI Governance Report and said it is considering AI agents as a new form of labor, including potentially linking agent performance to organizational key performance indicators. The carrier assigns agents employee ID numbers, records each agent’s role and supervisor, and monitors operating status, token use, and work history.

The company also says agents with greater autonomy or business impact must have a senior human supervisor, such as a team leader or executive. Agents can be suspended or retired when performance reviews show they are no longer needed.

Why it matters: This is a practical operating model for companies moving beyond pilots: inventory every agent, assign clear ownership, log its activity, and establish retirement criteria. Buyers should ask vendors whether their systems support equivalent audit trails and approval controls rather than relying on informal human oversight.

Try/watch: Create an internal register for every production agent with an owner, permitted tools, escalation path, activity logs, and a documented shutdown procedure.

Dell expands its AI Factory for on-premises agent workloads

What changed: Dell announced an expanded AI Factory with NVIDIA at NVIDIA GTC Berlin, including new rack-scale systems, an air-cooled two-socket server, a workstation with NVIDIA’s GB10 platform, and validated software and deployment templates for agentic workloads. Dell said more than 6,500 enterprises are using the platform as customers move AI projects from experimentation toward production.

The offering is designed to run agent workloads across deskside systems, data centers, and on-premises environments, giving enterprises an alternative to sending every task to a public cloud.

Why it matters: For regulated or latency-sensitive operators, the decision is shifting from “Can we build an agent?” to “Where should it run, and how do we control its data and tool access?” The new reference systems may shorten infrastructure work, but buyers still need workload-level cost and security comparisons against hosted services.

Try/watch: Pilot one bounded workflow on the proposed hardware and measure total cost per completed task, latency, energy use, model quality, and the number of human interventions before committing to a larger deployment.

NVIDIA signals a recurring software charge for agent infrastructure

What changed: NVIDIA CEO Jensen Huang discussed a possible annual software license of about $2,000 per GPU for software used to run AI agents. NVIDIA has not said which products would be covered or when the fee would begin, so this is a pricing signal rather than a confirmed commercial launch.

The proposal would expand NVIDIA’s business beyond selling chips and systems into recurring software revenue. It comes as enterprises seek security and management layers that constrain what agents can access while they execute tasks.

Why it matters: Infrastructure budgets for agentic AI may include a substantial software charge tied to GPU capacity, not just model API usage or hardware depreciation. Founders and IT buyers should avoid assuming that running more agents will have a linear cost: licensing, isolation, monitoring, and governance may become material components.

Try/watch: Request written pricing and entitlement details before sizing a deployment, and model the economics under both steady utilization and peak GPU capacity; monitor whether competitors offer comparable agent-runtime controls without per-GPU licensing.