SQBench: A Benchmark for Evaluating Task Delivery by Language-Model Agents in Production-Oriented Workflows
Introduced SQBench, a benchmark evaluating language models' task delivery in production workflows, with 220 tasks, combining functional completion and risk assessment, achieving a max of 60.5%.
Key Findings
Methodology
SQBench employs a three-layer task hierarchy—L1 basic capabilities, L2 composite skills, L3 industry-specific scenarios—integrated with automated and manual scoring. Tasks require models to process input assets, utilize available tools, and produce verifiable deliverables. Evaluation involves measuring functional completion (Completion) and applying a 10D risk matrix to quantify risks (Risk Penalty). Strict Pass mandates full completion with zero risk penalties. Testing 27 configurations across 220 tasks, the highest Weighted Pass@1 reached 60.5%, revealing performance gaps especially in high-compliance L3 tasks.
Key Results
- The top configuration achieved 60.5% Weighted Pass@1, but the average strict pass rate on L3 was only 18.5%, indicating models struggle under domain constraints. Out of 2,348 functionally complete results, 113 (4.8%) failed Strict Pass mainly due to risks like unverifiable citations, improper resource use, or format violations. Performance drops from functional completion to risk-adjusted scores by an average of 7.5 points, highlighting the importance of risk assessment in real-world tasks.
Significance
This work advances AI evaluation by emphasizing reliable task delivery in production-like settings, addressing the gap between answer correctness and artifact verifiability. Incorporating risk assessment into benchmarking aligns evaluation with real-world needs, promoting development of models that are not only accurate but also trustworthy and compliant. It provides a structured framework for assessing model robustness, especially crucial for deploying AI in sensitive sectors such as healthcare, finance, and industrial automation. The introduction of a risk matrix offers a nuanced view of model reliability, fostering safer AI deployment.
Technical Contribution
The paper introduces a novel evaluation framework combining automated and human scoring, with a versioned task instance structure that captures input, tools, and deliverables. The core innovation is the 10D risk matrix, which quantifies diverse risks like factual inaccuracies, format violations, safety breaches, and resource misuse, summing penalties to adjust performance scores. The multi-layer task taxonomy enables detailed capability profiling across foundational, compositional, and domain-constrained tasks, providing comprehensive insights into model strengths and weaknesses. This approach enhances existing benchmarks by integrating artifact-level risk evaluation, setting a new standard for production-oriented assessment.
Novelty
This is the first systematic effort to evaluate language models based on their ability to produce verifiable, risk-aware deliverables within constrained workflows. Unlike traditional benchmarks focusing solely on correctness or reasoning, SQBench emphasizes artifact integrity, compliance, and risk management. Its layered task design and the 10D risk matrix provide a detailed, multi-dimensional assessment of model reliability, bridging the gap between academic evaluation and real-world deployment challenges.
Limitations
- The evaluation relies on single-run results, which may not capture model stability over multiple attempts. Some risk indicators depend on subjective human judgment, introducing potential bias. Tasks are primarily designed for office, research, and industry scenarios, limiting generalization to other domains. Additionally, the framework's complexity may pose implementation challenges for broader adoption.
Future Work
Future research will incorporate multiple runs to assess model consistency, expand industry and task diversity, refine risk metrics, and develop adaptive evaluation protocols. Integrating reinforcement learning and self-supervised techniques could improve models' robustness under high-risk constraints. The framework will also explore real-time risk monitoring and explainability features to enhance trustworthiness, aiming for broader applicability across AI deployment scenarios.
AI Executive Summary
In the rapidly evolving field of artificial intelligence, evaluating large language models (LLMs) has traditionally centered on knowledge recall, reasoning, and tool use. However, these metrics often overlook a critical aspect: the model’s ability to deliver verifiable, high-quality artifacts suitable for real-world deployment. Recognizing this gap, Summer Sun introduces SQBench, a comprehensive benchmark designed to assess models’ capacity for reliable task delivery within constrained, production-like workflows.
SQBench constructs a structured evaluation environment encompassing 220 standardized tasks across three hierarchical layers—L1 foundational capabilities, L2 composite skills, and L3 industry-specific scenarios. Each task simulates real-world work patterns, requiring models to process input assets, employ tools, and generate artifacts that meet explicit formatting, content, and compliance criteria. The evaluation framework combines automated verification methods with human judgment, capturing both functional completion and risks through a 10D risk matrix. This matrix quantifies diverse risks such as factual inaccuracies, format violations, safety breaches, and resource misuse, assigning penalties that adjust the raw completion scores.
The experimental results, based on 27 model configurations, reveal that while some models achieve high scores in basic capabilities, their performance significantly deteriorates under complex, domain-constrained tasks. The highest weighted pass rate reached 60.5%, but the average strict pass rate on high-stakes L3 tasks was merely 18.5%. Notably, 4.8% of functionally complete results failed strict evaluation due to risks like unverifiable citations or inappropriate resource use, emphasizing that functional correctness alone is insufficient for reliable deployment.
This work marks a pivotal step toward more trustworthy AI systems, emphasizing artifact integrity, compliance, and risk management. By integrating detailed risk assessments into the evaluation process, SQBench provides a nuanced understanding of model robustness, guiding future improvements. The framework’s flexibility allows adaptation to various industries and application scenarios, fostering safer and more dependable AI integration. Future directions include multi-run stability assessments, broader industry coverage, and enhanced risk detection mechanisms, aiming to bridge the gap between academic benchmarks and real-world AI deployment challenges.
Deep Dive
Abstract
Existing evaluations of large language models cover knowledge, reasoning, coding, and tool use, but they rarely treat a verifiable deliverable produced within a constrained workflow as the unit of evaluation. We introduce SQBench, a benchmark for evaluating production-oriented task delivery by language-model agents. SQBench v1.0 contains 220 standardized tasks organized into L1 atomic capabilities, L2 composite skills, and L3 business scenarios. Each task requires an agent to process input assets, use available tools, and produce an explicitly specified deliverable. The evaluation first computes functional Completion and then derives Risk Penalty and Performance from independently evidenced triggers in a 10D Risk Matrix. A Strict Pass requires Completion = 1 and Risk Penalty = 0. We evaluate 27 model configurations under a common protocol, with one run per configuration-task pair. The highest prespecified Weighted Pass@1 is 60.5%. Mean Strict Pass@1 on L3 is 18.5%, and every configuration performs worse on L3 than on both L1 and L2, indicating that delivery under domain constraints is a shared weakness within the current task set. Of 2,348 results with Completion = 1, 113 (4.8%) fail the Strict Pass criterion because of risks such as unverifiable citations, inappropriate resource use, or format violations. These results show that functional completion alone does not fully characterize delivery quality and that risk determinations should be reported separately.