BusiAgent: AI in Bus & Business Systems

• BusiAgent is a polysemous term denoting multiple AI frameworks applied to structured decision-making in both transit and enterprise domains.
• In transit systems, it optimizes bus operations via advanced reinforcement learning techniques, dynamic scheduling, and robust multi-agent coordination.
• In enterprise contexts, BusiAgent systems integrate neuro-symbolic reasoning, LLM-driven orchestration, and privacy-preserving negotiation to enhance business decision support.

Searching arXiv for papers mentioning “BusiAgent” and closely related variants to ground the article. BusiAgent is a name used in recent arXiv literature for several agentic AI systems rather than a single canonical architecture. The label appears in at least two major lineages. In public-transport research, it denotes bus-control frameworks for holding, dispatch robustness, and electric-bus scheduling under stochastic operations. In enterprise AI, it denotes business-oriented agent systems for strategic decision support, neuro-symbolic process orchestration, CRM task execution, and privacy-preserving negotiation. Across these usages, the common theme is the construction of agents that operate over structured state, invoke specialized tools or optimization routines, and coordinate decisions under explicit operational constraints (Zhang, 28 Aug 2025, Wang et al., 21 Aug 2025, Manzolli et al., 24 Jun 2026).

1. Nomenclature and research scope

A recurring misconception is that BusiAgent names one software stack. In the literature surveyed here, the term is polysemous. It is used for reinforcement-learning systems in bus operations, for LLM-based business decision frameworks, and for broader “business agent” architectures in CRM and negotiation settings. This suggests a research motif rather than a standardized platform.

Usage of the name	Technical core	Representative papers
Bus fleet control	Asynchronous MARL, distributional MARL, single-agent SAC	(Wang et al., 2021, Wang et al., 2021, Zhang, 28 Aug 2025)
Enterprise decision support	Extended CTMDP, generalized entropy, Stackelberg hierarchy, QA loops	(Wang et al., 21 Aug 2025)
Neuro-symbolic business orchestration	LLM agents, enterprise KG, predicate logic, Prolog execution	(Pang et al., 22 Jan 2026)
CRM and negotiation agents	Agentic RL with shared memories; device-native cryptographic bargaining	(Lai et al., 29 Oct 2025, Roy, 1 Jan 2026)
Electric-bus aggregation	MILP scheduler plus supervisory trigger, pricing, and evaluator agents	(Manzolli et al., 24 Jun 2026)

The term therefore spans both transportation and enterprise-computing contexts. In the transportation lineage, “bus” refers literally to transit vehicles. In the enterprise lineage, “busi” abbreviates business. The shared naming can obscure substantial methodological differences: some BusiAgent systems are continuous-control RL policies, some are hierarchical LLM collectives, and some are neuro-symbolic executors with hard logical constraints.

2. Transit-control lineage: asynchronous and robust bus-holding agents

The earliest lineage in the supplied corpus treats route-level bus control as an asynchronous decision problem. In "Reducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning" (Wang et al., 2021), each vehicle $b_i$ is an agent that acts only when it arrives at a stop. The local state is

$$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$

the action is a strength parameter $a_{i,t}\in[0,1]$, and holding time is

$\Delta d_{i,t} = a_{i,t}\,\Delta T.$

The reward trades off route-wide headway regularity and intervention cost,

$$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$

with $\mathrm{CV}^2$ the squared coefficient of variation of headways. The architectural novelty is the critic decomposition

$$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$

where $U^\psi_{i,t}$ is an event critic built over a graph-attention network on neighboring asynchronous events. Empirically, the CAAC variant achieved the largest reduction in waiting time on route R1, with $-166$ s AWT, $42$ s AHT, $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$0 AOD, and $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$1 s ATT, and it retained the best $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$2AWT and $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$3AOD on unseen routes R2–R4 (Wang et al., 2021).

A second development emphasizes robustness under perturbations. "Robust Dynamic Bus Control: A Distributional Multi-agent Reinforcement Learning Approach" (Wang et al., 2021) introduces IQNC-M, which replaces a conventional critic with an Implicit Quantile Network and adds a meta-learning module over an event graph $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$4. The local state adds waiting passengers to the standard headway variables; actions remain continuous in $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$5 with $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$6. The reward again penalizes headway variance and control effort, with $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$7 in the experiments. The meta-learner produces distortion weights $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$8 over quantiles so that the critic better separates uncertainty due to the ego action from uncertainty induced by asynchronous interventions by other buses. In the reported evaluations on four trunk routes with real smart-card demand and AVL travel-time data, the simulator matched boarding times with $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$9 and journey times with $a_{i,t}\in[0,1]$0; under perturbations, IQNC-M achieved the largest reduction in waiting time, for example $a_{i,t}\in[0,1]$1 s versus $a_{i,t}\in[0,1]$2 s for CAAC on R1 at $a_{i,t}\in[0,1]$3, the greatest drop in occupancy dispersion $a_{i,t}\in[0,1]$4 versus $a_{i,t}\in[0,1]$5, and a moderate holding cost of $a_{i,t}\in[0,1]$6 s (Wang et al., 2021).

Taken together, these papers establish the bus-control meaning of BusiAgent as a family of event-driven control agents for bus bunching mitigation. A plausible implication is that the core modeling problem is not merely multi-vehicle interaction, but asynchronous credit assignment under stochastic traffic and demand.

3. Single-agent reformulation in realistic bus operations

"Single Agent Robust Deep Reinforcement Learning for Bus Fleet Control" (Zhang, 28 Aug 2025) reformulates the bus-holding problem from MARL to single-agent SAC, and this framework is explicitly described as BusiAgent or Embedding-SAC. The central claim is that a single agent can absorb inter-agent dependencies if the state is augmented with categorical identifiers. Each stop-level decision for bus $a_{i,t}\in[0,1]$7 after boarding and alighting at stop $a_{i,t}\in[0,1]$8 uses a state $a_{i,t}\in[0,1]$9 comprising four categorical identifiers—$\Delta d_{i,t} = a_{i,t}\,\Delta T.$0, $\Delta d_{i,t} = a_{i,t}\,\Delta T.$1, $\Delta d_{i,t} = a_{i,t}\,\Delta T.$2, and $\Delta d_{i,t} = a_{i,t}\,\Delta T.$3—plus three numerical features: forward headway $\Delta d_{i,t} = a_{i,t}\,\Delta T.$4, backward headway $\Delta d_{i,t} = a_{i,t}\,\Delta T.$5, and current segment speed $\Delta d_{i,t} = a_{i,t}\,\Delta T.$6. Instead of one-hot coding, each categorical variable is embedded through a learnable matrix $\Delta d_{i,t} = a_{i,t}\,\Delta T.$7 with

$\Delta d_{i,t} = a_{i,t}\,\Delta T.$8

The final state concatenates all embeddings and the three numerical features: $\Delta d_{i,t} = a_{i,t}\,\Delta T.$9

The action space is a single continuous scalar

$$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$0

representing additional holding time beyond natural dwell, with $$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$1 s in the implementation. The policy and critics share four embedding layers and then use a 4-layer MLP with hidden sizes $$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$2 and ReLU activations. The policy head outputs Gaussian parameters $$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$3 and $$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$4, followed by a $$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$5-squash to enforce $$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$6; each twin-Q head outputs a scalar $$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$7. Target critics are updated by Polyak averaging with coefficient $$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$8, using Adam with learning rate $$r_{i,t} = -\,(1-w)\,\mathrm{CV}^2 \;-\; w\,a_{i,t},$$9 and batch size $\mathrm{CV}^2$0 (Zhang, 28 Aug 2025).

The reward is a schedule-aware ridge-shaped function centered on the nominal headway of $\mathrm{CV}^2$1 s: $\mathrm{CV}^2$2

$\mathrm{CV}^2$3

The first terms reward proximity to the $\mathrm{CV}^2$4 s schedule, the $\mathrm{CV}^2$5 term incentivizes symmetry, and the indicator term penalizes extreme deviations exceeding $\mathrm{CV}^2$6 s. The simulation environment is a bidirectional corridor with $\mathrm{CV}^2$7 stops, departures every $\mathrm{CV}^2$8 s, thirteen hourly OD matrices, Poisson arrivals with $\mathrm{CV}^2$9, and stochastic segment speed sampled from $$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$0 with $$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$1 (Zhang, 28 Aug 2025).

The reported steady-state 10-episode rolling means were $$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$2 for Embedding-SAC, $$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$3 for MADDPG-PS, $$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$4 for MADDPG-NPS, and $$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$5 for the uncontrolled baseline. Embedding-SAC converged in approximately $$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$6 episodes, while MADDPG-PS/NPS required approximately $$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$7–$$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$8 episodes and were unstable. Removing embeddings and reverting to raw one-hot inputs caused approximately $$G_{i,t} = Q^\phi_{i,t}\bigl(s_{i,t},a_{i,t}\bigr)+U^\psi_{i,t},$$9 slower convergence and a $U^\psi_{i,t}$0 drop in final reward. Within the bus-control literature, this is the clearest instance in which BusiAgent denotes a specific architectural proposal rather than a generic bus-control agent (Zhang, 28 Aug 2025).

4. Enterprise decision support and neuro-symbolic process orchestration

In enterprise AI, BusiAgent denotes a very different class of systems. "From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM Framework for Business Excellence" (Wang et al., 21 Aug 2025) defines BusiAgent as a six-module framework spanning fine-grained operational reasoning and boardroom-level strategy. Its modules are Role-Based Agent Modeling, Collaboration Optimizer, Hierarchical Decision Engine, Tool Integration System, Prompt Optimization (Contextual Thompson Sampling), and Quality Assurance System. Each role is modeled as an extended CTMDP

$U^\psi_{i,t}$1

with optimal value function

$U^\psi_{i,t}$2

Horizontal collaboration is optimized via a Rényi-type generalized entropy

$U^\psi_{i,t}$3

while vertical coordination is enforced through a multi-level Stackelberg game with equilibrium policy

$U^\psi_{i,t}$4

Prompt selection is formulated as contextual Thompson sampling with Gaussian-process priors and expected regret bound

$U^\psi_{i,t}$5

On the AI Company Generation dataset of $U^\psi_{i,t}$6 tasks—$U^\psi_{i,t}$7 Problem Analysis, $U^\psi_{i,t}$8 Task Assignment, and $U^\psi_{i,t}$9 Solution Development—the framework reported $-166$0 improvement over the best baseline in Problem Analysis, $-166$1 in Task Assignment, user satisfaction of $-166$2 versus $-166$3 for GPT-4o and $-166$4 for GPT-3.5, and robustness on critical tasks of $-166$5 versus $-166$6 for the control policy, with Welch’s $-166$7, $-166$8 (Wang et al., 21 Aug 2025).

A second enterprise interpretation appears in "Autonomous Business System via Neuro-symbolic AI" (Pang et al., 22 Jan 2026), which presents AUTOBUS and describes it as an autonomous “Business Agent” or BusiAgent. Here the architecture couples Human Overseers, an Enterprise KG, LLM-Based AI Agents, a Predicate-Logic Engine, and Auxiliary Tools. An initiative is formalized as tasks $-166$9 with predicates

$42$0

and a canonical task schema rule

$42$1

Enterprise KG triples are translated into Prolog facts and schema-level constraints into foundational rules; the LLM agent synthesizes task-specific rules and action predicates from natural-language instructions, KG schema, and tool catalogs; the logic engine then performs backward and forward chaining, checks preconditions, invokes external tools, and verifies postconditions. Human overseers define semantics and policies, curate tools, and supervise ambiguous or high-impact decisions. This suggests a complementary enterprise interpretation of BusiAgent: not a collective of autonomous decision makers optimizing a stochastic game, but a logic-grounded executor that enforces deterministic, auditable business semantics (Pang et al., 22 Jan 2026).

5. CRM, memory-augmented task execution, and privacy-preserving negotiation

"CRMWeaver: Building Powerful Business Agent via Agentic RL and Shared Memories" (Lai et al., 29 Oct 2025) presents a business-agent architecture that is not titled BusiAgent but is explicitly framed as a practical recipe for building one. CRMWeaver uses a 4B-parameter instruct-tuned LLM, Qwen3-4B-Instruct, with a tool interface consisting of execute, date_calculation, and answer, plus a long-term memory of distilled “guidelines” indexed by BGE-small-en-v1.5 embeddings. The inference pipeline first retrieves a top-1 memory $42$2 and prepends its guideline when $42$3, with $42$4. Training combines synthetic complex data from random walks on a record graph, simple synthetic examples, and task-specific CRMArena trajectories. The RL formulation uses observation histories of prior thoughts, tool calls, and tool responses; actions are tool invocations or final answers; transitions are deterministic; and final reward is

$42$5

The PPO-style objective uses clipping bounds $42$6, $42$7, and rollout size $42$8. On CRMArena-Pro, CRMWeaver achieved B2B averages of $42$9 and B2C averages of $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$00, with database scores of $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$01 in B2B and $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$02 in B2C; removing shared memory reduced averages from $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$03 in B2B and $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$04 in B2C (Lai et al., 29 Oct 2025).

"Device-Native Autonomous Agents for Privacy-Preserving Negotiations" (Roy, 1 Jan 2026) then extends the business-agent idea into cryptographic, on-device bargaining. The paper’s “Implications for BusiAgent” section treats BusiAgent as an off-the-shelf, device-native, privacy-preserving autonomous negotiator. The architecture is organized as an eight-step workflow—Goal Initiation, Guardrails, Context Expansion, Intent Understanding, Adaptive Planning, Autonomous Execution, Real-Time Monitoring, and Outcome Evaluation—into which six components are embedded: Selective State Transfer, Explainable Memory, World Model Distillation, Multi-Agent Negotiation Protocol, Model-Aware Offloading, and Simulation-Critic Safety. The world model is distilled from a 7 B-parameter teacher into a 500 M-parameter student of approximately 1.2 GB. Privacy is enforced by Groth16 zk-SNARKs proving that each offer $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$05 satisfies

$$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$06

without revealing $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$07. Reported results include an average success rate of $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$08, a $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$09 latency improvement over cloud baselines, proof generation on a high-end device of approximately $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$10 ms with verification under $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$11 ms, and user trust rising from $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$12 to $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$13 $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$14 when decision trails are available (Roy, 1 Jan 2026).

Across CRMWeaver and device-native negotiation, BusiAgent functions as a business-agent design space characterized by tool use, memory, and explicit control over reliability or privacy. A plausible implication is that the enterprise meaning of the term is increasingly associated with operationally grounded agents rather than purely conversational assistants.

6. Electric-bus aggregation, pricing behavior, and governance

A further transport-specific usage appears in Manzolli et al., "When Agents Meet Electric Bus Fleet Operations: Pricing Behavior, Trade-offs, and Policy Implications in an Aggregator Framework" (Manzolli et al., 24 Jun 2026). Here the supplied summary refers to a BusiAgent framework consisting of a two-layer separation between an Optimization Core and an Agentic Layer. The Optimization Core is a mixed-integer linear model for the PTO scheduler, with variables $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$15, $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$16, $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$17, $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$18, and $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$19, minimizing PTO energy cost

$$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$20

subject to service-charge exclusivity, state-of-charge dynamics, battery bounds, charger occupancy, depot aggregation, and depot exchange limits. The Agentic Layer comprises three supervisory LLM-driven agents: a Trigger Agent, a Pricing Agent, and an Evaluator Agent. The Trigger Agent computes

$$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$21

and raises re-optimization when $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$22 or a hard-risk flag is active. The Pricing Agent selects bounded multipliers $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$23 and $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$24 for tariffs under either profit-based or operational-based coordination. The Evaluator Agent scores schedules using

$$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$25

The case study uses $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$26 buses of $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$27 kWh capacity, $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$28 chargers at $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$29 kW, $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$30 half-hour intervals, $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$31 heterogeneous route blocks, and spot prices from $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$32 to $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$33 EUR/kWh with average $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$34. Day-ahead strategies were S1 “dumb” charge, S2 smart charge no V2G, S3 profit-based, and S4 operational-based. Reported day-ahead results were: S1 cost $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$35 EUR with no V2G; S2 cost $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$36 EUR with no V2G; S3 cost $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$37 EUR, revenue $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$38 EUR, buy $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$39 kWh, sell $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$40 kWh, average buy $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$41 EUR/kWh, sell $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$42; and S4 cost $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$43 EUR, revenue $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$44 EUR, buy $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$45 kWh, sell $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$46 kWh, average buy $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$47, sell $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$48 (Manzolli et al., 24 Jun 2026).

The real-time experiments introduce service delays of $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$49 min, route-energy deviations of $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$50, price shocks of $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$51, and combined disturbances. Under P+50, profit-based mode yielded PTO cost $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$52 EUR and aggregator revenue $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$53 EUR, while operational-based mode yielded PTO cost $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$54 EUR and aggregator revenue $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$55 EUR, a gap of $$s_{i,t} = \bigl(\text{onboard\_pax}_{i,t},\; h_i(t),\; h_{i+1}(t)\bigr),$$56 EUR/day. Profit-based mode also eliminated V2G export in all combined scenarios, whereas operational-based mode preserved export except under extreme late-day stress. This is the most explicit policy-oriented treatment of BusiAgent in the corpus: the same agentic capability that simplifies charging coordination can transfer cost exposure to the PTO when pricing is profit-oriented. The paper therefore emphasizes tariff-margin bounds, value-sharing rules, transparency of coordination mode and prompt schema, and reporting of realized tariff vectors, trigger decisions, accepted schedules, and value allocations (Manzolli et al., 24 Jun 2026).