Not all AI tasks are equal. Networks should prioritize workloads based on their required outcomes and urgency. The joint objective should be to assure AI agent Service Level Expectations (SLEs): the performance and reliability required to complete an intended task. A security response and a background research task have different deadlines. Network treatment should reflect those requirements rather than give every AI flow the same priority. 

As adoption and aggregate traffic grow, providers should automate traffic management within defined service policies. That means detecting contention, adjusting queueing and scheduling where congestion occurs, and verifying that time-sensitive tasks improve without starving other services. Where appropriate, those adjustments can extend to access-network resource allocation and traffic engineering. Coordination across the home and provider network is essential when a workflow spans both. 

Cost per successful task provides a complementary measure. Savings must be demonstrated: a network retry does not necessarily trigger another billable inference, and agent and application platforms make most model-selection and token-saving decisions. With application instrumentation, premise intelligence can help identify connectivity-related rework, inform local-versus-cloud execution, and schedule flexible tasks around congestion. The goal is to translate better connectivity and execution decisions into more completed tasks, less avoidable rework, and a lower cost of useful AI output. 

For broadband providers, this creates a path to more informed capacity planning and differentiated services. For enterprises, it creates a way to connect execution reliability with avoidable AI costs. For network infrastructure providers, it extends the relevance of transport assurance to workloads originating beyond the office.