Learning When to Automate: Queue Control in Human-AI Service Systems

Autonomous customer-service agents are shifting from conversational interfaces toward operational execution roles: they retrieve firm records, apply service policies, and execute backend writes such as refunds, cancellations, exchanges, order modifications, and reservation changes. This shift creates a service-control problem: firms must keep routine service fast and low-friction while preventing operational errors on requests where customer instructions, policy constraints, firm records, and backend writes interact. We propose a difficulty-routed service-control architecture that asks when service agents should reconsider before acting. A lightweight router keeps routine sessions on a low-cost baseline path and routes operationally coupled sessions to an escalated workflow. The escalated path uses conflict-aware communication and write-triggered reconsideration to concentrate deliberation and safeguards before consequential backend writes, rather than applying additional control uniformly across all service sessions. We evaluate the architecture on human-verified retail and airline tasks from $τ^{2}$-bench. In retail, the method improves reliability consistently on service requests with operational conflict. Routing evidence shows that stronger control is directed toward conflicted requests rather than broadly applied to routine ones. Dialogue and tool-use profiles suggest that gains do not come from indiscriminate interaction expansion or broader tool chains; instead, added turns and tool calls support evidence gathering, write separation, and pre-write reconsideration. Case-level evidence shows that the escalated workflow preserves fallback plans, binds retrieved records to the correct action, sequences writes, and decomposes multi-entity requests. Airline results extend the same service-control logic to reservation operations.

We propose a per-task leverage ratio for human-agent collaboration: human work displaced by an agent, divided by the human time required to specify the task, resolve mid-run interrupts, and review the result. The denominator decomposes into three channels through which a conserved per-task information requirement must flow, each with its own time-cost scalar. We show that information density itself is directional and bounded by separate ceilings on human-to-agent and agent-to-human flow, and that the asymptotic behavior of leverage decomposes into two scaling axes (capability and memory) with a non-zero floor on the planning term set by irreducible task novelty bounded by human throughput. We extend this per-task analysis to a windowed leverage measure that accommodates recurring tasks, spawned subtasks, and amortized system-design investment. The per-task ceiling does not bind the windowed measure, though both remain bounded: $L_{\text{task}}$ by per-task novelty, $L_{\text{window}}$ by the stock of accumulated planning investment that pays out within the window. The framework operationalizes aspects of earlier qualitative work on supervisory control (Sheridan, 1992), common ground (Clark & Brennan, 1991), and mixed-initiative interaction (Horvitz, 1999) within a single normative ratio, and produces a list of testable empirical questions that we leave as open problems.

We present HAAS Studio, a simulation and decision-support tool for policy-aware adaptive task allocation between humans and AI systems. HAAS Studio turns the HAAS framework into an interactive environment for asking a practical deployment question: before introducing AI into a workflow, how can a team compare allocation strategies, inspect governance tradeoffs, and derive a defensible task-level operating model? The tool combines a five-dimensional cognitive representation of subtasks, a five-mode collaboration spectrum, adaptive allocation with multi-armed bandits (UCB1, Discounted UCB, LinUCB, and Thompson Sampling), oracle counterfactual regret analysis, contract-based governance with four independent guards, and a multi-criteria decision-support layer that separates efficient strategies from deployable options. It also models human-AI coevolution across six layers, monitors deskilling risk through sliding-window exposure metrics and benchmark runners, and supports persistent worker modeling through Live Twin and Planning modules. Three domain packs are included: software engineering, manufacturing, and healthcare. Each provides a task catalog, worker profiles, and KPI vocabulary, while the architecture allows new domains to be added without modifying the simulation core. The release includes 16 company profiles and six governance benchmark suites. This paper focuses on the tool, including its modeling assumptions, layered architecture, interaction workflow, built-in evidence assets, task-oriented recipes, case-study protocols, and a compact reproducible demonstration snapshot. A decision-guidance layer translates benchmark outputs into deployment decisions through structured patterns, heuristics, and a decision matrix.