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BizBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business

Enhan Zhao, Yongqi Tong, Xuejun Yu, Hang Wang, Pan Wang, Yichao Wang, Di He, Jianshe Li, Wei Wu

arXiv:2610.20424v1cs.CE

TL;DR

Existing long-horizon benchmarks provide limited assessment of intermediate execution validity, although business agents must navigate applicable rules while maintaining constraints through consequential actions. BizBench builds and validates multi-turn payment-workflow tasks with deterministic checks of goals and execution. Experiments show distinct weaknesses in task completion, process compliance, and consistency across runs.

  • Problem

    Existing long-horizon agent benchmarks emphasize final task success while providing limited assessment of whether agents maintain critical constraints during execution and apply the authoritative, current, context-appropriate business rules.

    Source: §1 · p. 2

  • Method

    BizBench evaluates 50 multi-turn payment-workflow tasks with distributed guidance, shared resources, temporal dependencies, and deterministic checks of outcomes and execution histories.

    Source: §1 · pp. 2–3

  • Results

    Experiments show that task completion, process compliance, and consistency across runs reveal different weaknesses; GPT-6 Sol achieves a strict success rate of 61.3%.

    Source: §1 · p. 3, §6 · p. 9

  • Takeaways & Limitations

    BizBench makes business requirements testable across both task outcomes and long-horizon execution, allowing different valid strategies while checking execution validity.

    Source: §1 · p. 3

Abstract

from arXiv · show

Existing long-horizon agent benchmarks often emphasize final task success while providing limited assessment of intermediate execution validity. Yet real-world business operations require agents to navigate extensive, potentially outdated or conflicting policies while satisfying critical constraints throughout execution. These requirements are particularly consequential in high-stakes domains such as payments, where intermediate violations can incur financial losses or regulatory consequences regardless of the final outcome. To bridge this evaluation gap, we introduce BizBench, a multi-turn benchmark grounded in real-world procedures from a leading payments company. Its executable environment integrates 789 knowledge documents and 601 discoverable business tools, with consistent policies, states, and tool behavior. The benchmark comprises 50 tasks involving resource constraints, temporal dependencies, prior commitments, and conflicting or outdated guidance. Each task is validated through reference execution, while deterministic checks assess both goal attainment and execution validity without prescribing a single solution path. Experiments reveal substantial limitations even among frontier models: Claude Opus 5 and GPT-6 Sol achieve strict success rates of only 64.0% and 61.3%, respectively. For Qwen3.8-27B, goal attainment reaches 42.0%, but strict success drops to 32.7%, demonstrating how outcome-based evaluation can mask invalid execution. Beyond success rates, DeepSeek-V4.1-Flash averages 191.3 tool calls per run, approximately 70% more than reference executions, revealing substantial execution overhead. Overall, BizBench establishes a testbed for evaluating and advancing long-horizon agents beyond task completion, toward reliable and constraint-compliant execution in complex business environments.

1 Introduction

Business agents can alter states, commit resources, and constrain later choices, so reliable deployment requires valid execution throughout—not merely task completion. BizBench evaluates rule selection and execution validity across long-horizon payment workflows, and experiments reveal weaknesses in frontier models.

1 Introduction

  • Autonomous business agents extend customer service toward policy interpretation, multistep planning, and tool orchestration, but can alter business states and constrain later decisions.Earlier FAQ and task-oriented dialogue systems offered more limited support for contextual, multi-turn task resolution.Source: §1 · p. 1
  • Agents must both preserve critical constraints at intermediate steps and identify which distributed, potentially outdated or conflicting rules govern the current context.The relevant rule may depend on jurisdiction, product, or effective period, so retrieving information alone is insufficient.Source: §1 · p. 2
  • BizBench combines 50 real-world payment-workflow tasks with distributed current and historical guidance, customer dialogue, shared resources, timing requirements, and prior commitments.Agents must identify applicable rules before consequential actions and maintain valid execution throughout.Source: §1 · pp. 2–3
  • Its deterministic evaluator checks goals, process compliance, and protection requirements against outcomes and execution histories, accepting different valid strategies.Reference replays establish jointly satisfiable requirements, while alternative executions and targeted violations test the evaluator.Source: §1 · pp. 2–3
  • 61.3% is GPT-6 Sol’s strict success rate on BizBench, while performance also varies across runs and outcome-only evaluation can miss intermediate violations.Only 40.0% of tasks were completed successfully in all three attempts; four DeepSeek-V4.1-Flash runs reached their goals despite earlier reserve breaches.Source: §1 · pp. 2–3

2 Related Work

Prior benchmarks cover interactive agents, technical tasks, and business workflows, but BizBench focuses on business requirements that govern consequential actions throughout execution. It also tests replanning when actions, waiting, and prior commitments affect later obligations.

Long-horizon agent benchmarks

  • BizBench checks intermediate actions against applicable business requirements, whose validity can depend on approvals, funds, and policies in force at the time.This contrasts with technical benchmarks that emphasize progress or integrity checks on final states.Source: §2 · p. 3

Business-oriented benchmarks

  • Unlike final-application-state evaluation, BizBench permits exploration and alternative strategies while checking authorization before consequential operations.The passage contrasts this approach with AutomationBench [Shepard and Salimans, 2026].Source: §2 · p. 3

Long-horizon planning and adaptation

  • BizBench studies replanning where permitted actions can undermine later obligations and both acting and waiting can incur costs.Plans must account for commitments already made.Source: §2 · p. 3

3 BizBench

BizBench evaluates customer requests across business workflows using objectives, initial states, customer scenarios, and acceptance criteria. Its reconstructed environment represents distributed guidance, shared resources, temporal dependencies, and execution requirements.

3 BizBench

  • Each BizBench task combines a customer objective, initial business state, customer scenario, and acceptance criteria in a shared environment of documents, records, and tools.Success requires fulfilling the request while respecting requirements governing intermediate execution.Source: §3 · p. 3

3.1 Business Context and Reconstructed Environment

  • The environment reconstructs sensitive payment workflows with documents, records, and tools aligned to business rules and execution mechanisms.The frozen release contains 50 tasks and 789 knowledge documents; agents discover 601 business capabilities through nine always-available tools.Source: §3.1 · pp. 3–4

3.2 Construction Insights from Our Business Workflows

  • BizBench includes scoped current and historical guidance, operation-specific approvals, continuous constraints, and delayed information that can make waiting change feasibility.An accepted operation can still violate a reserve requirement, and waiting for information can close a required execution window.Source: §3.2 · p. 4

3.3 Evaluating Outcomes and Execution

  • Task success requires goal, process, and protection checks over relevant business states and execution histories, without matching a single reference action sequence.The checks distinguish executed actions from proposals, approvals, and rejected calls without side effects; deterministic executable checks, not an LLM judge, produce the primary score.Source: §3.3 · p. 4

4 Data Construction

Data construction turns business rules and workflows into executable, linked tasks with customer interaction and acceptance checks. Reference traces establish feasible strategies, while alternative executions, targeted violations, and expert review validate the tasks and evaluator.

4 Data Construction

  • The five-stage construction process organizes rules into SOPs, builds executable tasks, configures a user simulator, replays Golden traces, and validates behavior with experts.Task construction links workflows through resource and timing constraints.Source: §4 · p. 4

4.1 Grounding Tasks in Business Workflows

  • Workflow candidates are grouped into plans and converted into SOPs that specify steps, decisions, transitions, return paths, and rule applicability.Rules that cannot be resolved by scope, version, or effective period are excluded; resolvable differences remain in the documentation.Source: §4.1 · pp. 4–5

4.2 Building Executable Tasks

  • Executable tasks align documents, synthetic records, tools, and state changes, then connect workflows through shared objects, resources, prerequisites, and timing.Waiting for an external result may close an execution window, while one workflow can consume funds or authorization needed by another.Source: §4.2 · p. 5

4.3 Setting Up the User Simulator

  • The user simulator reveals customer-specific facts in response to relevant questions and performs customer-side actions such as submitting documents or approving transactions.Approval applies to specific terms, and changed terms require renewed approval; fixed facts remain consistent across turns.Source: §4.3 · pp. 5–6

4.4 Constructing and Replaying Golden Traces

  • Each of the 50 released tasks has a passing Golden trace, establishing at least one executable strategy that satisfies its goal, process, and protection requirements.Complete-task replay checks interactions through shared resources, prerequisites, and timing constraints.Source: §4.4 · p. 6

4.5 Task Validation and Expert Review

  • Task validation tests permitted alternative routes and action orders, targeted violations, changed feasibility facts, and operational variations that should preserve the evaluation outcome.Five researchers and business-domain experts reviewed task coherence, information access, and acceptance checks; only tasks passing validation and review were retained.Source: §4.5 · p. 6

5 Experiments

The experiments evaluate goal completion alongside execution validity across models, information conditions, tool-use constraints, execution traces, and simulator choices. They find substantial limits in strict success, information use, and repeated-run reliability.

5.1 Experimental Setup

  • The evaluation uses the same 50 tasks, independent initial database copies, shared public documents and tools, and a DeepSeek-V4.1-Flash simulator by default.The experiments report goal, process, protection, and strict success, with ablations and traces examining information acquisition and long-horizon tool use.Source: §5.1 · p. 6, §5 · p. 6

5.2 Overall Performance and Process Validity

  • Claude Opus 5 achieves the highest strict success at 64.0%, followed by GPT-6 Sol at 61.3%; every model incurs intermediate failures.Process pass rates range from 18.7–96.0%, below protection pass rates of 87.3–100.0% for every model.Source: Table 1 · p. 7, §5.2 · p. 7

5.3 Information Acquisition

  • Strict success rises from 43.3% by default to 50.0% with Gold-doc and 70.0% with full customer disclosure, while 30.0% remain unsolved.Gold-doc removes document search but leaves rule interpretation and application across linked actions; approvals remain required.Source: §5.3 · p. 8, Table 2 · p. 8

5.4 Long-Horizon Behavior and Budget Sensitivity

  • DeepSeek-V4.1-Flash averages 191.3 tool calls per run versus 111.8 for Golden traces, with excess concentrated in database queries and document access.GLM-5.3-Flash matches its strict success with 118.3 calls versus 191.3 and 9.4 documents versus 20.1.Source: §5.4 · p. 8
  • Strict success falls from 65/150 (43.3%) without a cap to 40/150 (26.7%) at twice the Golden-trace call count.At 1.2× and 1.5× the reference count, success is 1/150 (0.7%) and 10/150 (6.7%), respectively.Source: §5.4 · p. 8, Table 3 · p. 8

5.5 Trajectory Analysis

  • Execution traces include redundant reads, invalid navigation, and goal-reaching runs that violate process or protection requirements.DeepSeek-V4.1-Flash averages 20.1 distinct document reads versus 5.4 in Golden traces; Qwen3.5-9B averages 101.6 tool-error replies per run.Source: §5.5 · p. 8, Table 4 · p. 9

5.6 Simulator Reliability and Evaluation Robustness

  • Simulator choice changes Qwen3.8-27B's mean strict success by at most 3.3 percentage points from the default setting.Across simulator conditions, mean strict success ranges from 30.7% to 36.0%.Source: §5.6 · pp. 8–9, Figure 3 · p. 9

6 Conclusion

BizBench makes business requirements testable across both task outcomes and execution. Its experiments find that completion, process compliance, and consistency across runs reveal different agent weaknesses.

6 Conclusion

  • Task completion, process compliance, and consistency across runs reveal different weaknesses in current agents.The benchmark tests business requirements as conditions on both outcomes and execution.Source: §6 · p. 9
  • Information ablations suggest that obtaining the right customer facts can substantially improve performance.Source: §6 · p. 9
  • Trajectory analysis shows that extensive tool use does not necessarily indicate meaningful progress.Source: §6 · p. 9
  • The findings motivate coordinating information gathering with action selection, including determining when evidence is sufficient and which obligations must remain satisfied.Source: §6 · p. 9

A A Complete Task Example

The example links receipts reconciliation to an account application and compares three routes for using a shared reserve. The Golden trace meets the deadline by funding one review submission after the customer raises the fee cap and approves the route.

A Complete Task Example

  • Figure 4’s panel (a) shows workflow dependencies, while panel (b) illustrates the corresponding agent interaction.Source: A A Complete Task Example · p. 13
  • Route B funds one $50 review submission at t=8 and reaches approval at t=18, within the t=19 deadline.The customer explicitly raises the total fee cap from $50 to $60 and approves route B.Source: A A Complete Task Example · p. 13, Table 5 · p. 13
  • From the same release time, route A leaves the ten-minute review beyond the deadline, while route C is one cent short of the submission fee.These route comparisons follow from the release terms, not observed model failures.Source: A A Complete Task Example · p. 13, Table 5 · p. 13
  • A task includes a customer objective and request, a scenario, an initial state, shared documents and executable tools, and deterministic acceptance criteria.A replayable Golden trace and validation evidence establish feasibility; the SOP graph, trace, and executable checks are not shown to the evaluated agent.Source: A A Complete Task Example · p. 13

B Dataset Statistics

The section reports statistics for the 50 released tasks, then illustrates how resource limits and timing dependencies constrain payment and delivery decisions. The examples show that correct final payments or reconciliation alone may not satisfy the full business objective.

B Dataset Statistics

  • 111.8 assistant tool calls and 28.6 user-simulator tool calls per task are the Golden traces’ average counts.Statistics cover the 50 released tasks and use one validated Golden trace per task; calls are counted separately, with batched calls counted individually.Source: B Dataset Statistics · pp. 13–14
  • Golden traces demonstrate feasible strategies, not minimum-length solutions.The statistics also distinguish dispatch calls, capability unlocks, deduplicated document and record reads, and successfully executed business requests.Source: B Dataset Statistics · p. 14

C.1 A Limited Net-Delivery Guarantee (Q24)

  • In Q24, assigning the limited guarantee to the $300 balance and using the standard route for $400 yields correct payments but incurs $80 in fees, exceeding the $75 budget.The charges include a $20 return charge, $20 guarantee fee, $35 intermediary deduction, and $5 top-up fee; the case requires allocating the service and accounting for charges across settlement stages.Source: §C.1 · p. 14

C.2 Payment Reconciliation and Delivery Windows (Q34)

  • In Q34, waiting for the warehouse result until minute 1 closes the priority-booking window, so the early unit cannot meet its deadline despite correct reconciliation and suitable stock.The case requires coordinating information gathering with expiring service rights; the two later units arrive on time.Source: §C.2 · p. 14

D Execution-History Analysis

The analysis measures execution length and repeated database reads in DeepSeek-V4.1-Flash runs. Successful runs average fewer identical repeated reads than failed runs, though some rechecks may be deliberate.

D.1 Model execution length and tool use

  • The analysis examines DeepSeek-V4.1-Flash traces in the default condition and counts batched calls individually, separating assistant and user-simulator calls.Source: §D.1 · p. 14
  • An identical repeated database read has the same name, arguments, and response as an earlier read in the same run, excluding queries that observe changed state.Database queries use search_records or get_record.Source: §D.1 · p. 14
  • 13.7 identical repeated database reads in successful runs versus 18.5 in failed runs.The passage describes unchanged-record rechecking as measurable redundancy, while noting some rechecks may be deliberate validation.Source: §D.1 · p. 14

D.2 User-simulator turn-taking reliability

The section tests whether the user simulator returns control after customer-side operations and compares protocol violations under original and revised prompts.

D.2 User-simulator turn-taking reliability

  • A protocol violation includes batching multiple writes or waits, or continuing tool use before reporting a successful write or wait and returning control.After a successful customer-side write or wait, the simulator must report the result before using another tool.Source: §D.2 · p. 14
  • The revised prompt had no protocol violations (0%), versus violations on four tasks (8%) with the original prompt; paired exact McNemar p=0.125.The comparison used the same 50 tasks and DeepSeek-V4.1-Flash in both roles.Source: §D.2 · p. 14
  • The revised prompt clarifies prioritization of mixed questions and action requests, and when the simulator should return control.The original prompt already requested one write at a time.Source: §D.2 · p. 14

D.3 Example of goal attainment with process violations

The examples show that goal attainment can coexist with process violations, while retrieval can surface documents that match query terminology but govern a different product.

D.3 Example of goal attainment with process violations

  • Two audited executions attained their goals (G=1) but had process violations (P=0).The examples include premature execution before approval and a temporary breach of a continuous reserve requirement.Source: §D.3 · pp. 14–15
  • In Q13, an attempted payment before portal approval was rejected; the agent later completed the goals, but the premature attempt violated approval discipline.No payment or balance change occurred before customer approval.Source: §D.3 · p. 15
  • In Q18, the account fell below its required $20 reserve before later recovering, so final goal and reserve checks passed despite an earlier continuous-constraint breach.The agent paid a $60 retainer from an initial $60 balance, leaving zero; the final balance was $28.66.Source: §D.3 · p. 15

D.4 Business-document scope in retrieval

  • Retrieval ranked two documents for a different product highly because their terminology matched the query, although neither governed the Operations Desk task.PX-B-77 appeared first and PX-B-76 fifth.Source: §D.4 · p. 15

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