Challenge: Existing Large Language Models fail to execute multistep operational workflows requiring precise procedural adherence.
Approach: They propose an end-to-end multi-agent framework automating Standard Operating Procedures in e-commerce.
Outcome: The proposed framework achieves 85-97% accuracy and a 94.2% execution consistency in e-commerce . it is based on a human-AI framework that transforms ambiguous documentation into automation-ready specifications .

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Challenge: Unstructured and ambiguous Standard Operating Procedures suffer from ambiguity, missing information, and inconsistency, all of which hinder automation.
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A Practical Approach for Building Production-Grade Conversational Agents with Workflow Graphs (2025.acl-industry)

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Challenge: Large Language Models (LLMs) have led to significant improvements in various service domains, including search, recommendation, and chatbot applications.
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ReAct Meets Industrial IoT: Language Agents for Data Access (2025.emnlp-industry)

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Challenge: a framework for domain-specific language agents is being developed for industrial automation . a novel approach to adapting these systems to domain-based applications poses new challenges .
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WorkTeam: Constructing Workflows from Natural Language with Multi-Agents (2025.naacl-industry)

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Challenge: Existing workflow construction methods require specialized knowledge and task-switching skills.
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Towards large language model-based personal agents in the enterprise: Current trends and open problems (2023.findings-emnlp)

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Challenge: Existing large language models (LLMs) are brittle to input changes and can produce inconsistent results for the same inputs.
Approach: They propose to use large language models to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal.
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Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant (2025.findings-emnlp)

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Challenge: Auto-SLURP is a benchmark dataset for evaluating multi-agent frameworks powered by large language models.
Approach: Auto-SLURP is a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks . authors propose it extends original SLURP dataset by relabeling data and integrating simulated servers and external services.
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TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)

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Challenge: Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools.
Approach: They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems.
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AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)

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Challenge: Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
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A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domains (2026.acl-long)

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Challenge: Existing benchmarks focus on product search tasks, but ignore potential risks.
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Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research (2025.acl-demo)

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Challenge: Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks.
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