A Large-Language-Model-Oriented Dual-Channel Agent Framework for Intelligent Generation of Simulation Scenarios

Agent-Ops: A Multi-Agent Orchestration Framework for End-to-End SOP Automation in E-Commerce Operations

While Large Language Models excel at reasoning and language understanding, they struggle with multi-step operational workflows requiring precise procedural adherence, which is fundamental for industrial automation. Existing SOP-guided agents assume well-defined procedures and structured APIs, failing to address enterprise realities like incomplete SOPs, dynamic web interfaces, and unpredictable document formats. We present Agent-Ops, an end-to-end multi-agent framework automating Standard Operating Procedures in e-commerce. Agent-Ops contributes: (1) SOP Groomer , a human-AI framework transforming ambiguous documentation into automation-ready specifications, (2) WebAgent , achieving 91.3% task completion and 86.5% execution consistency through demonstration-based learning, and (3) a Document Verification Agent performing multi-lingual validation across tax in-voices, certificates, and supply chain documents with 94.2% accuracy. Deployed across seven SOP categories in three geographic regions, Agent-Ops achieves 85-97% end-to-end accuracy while reducing case resolution time by 83% reduction. Use across 100 Account Managers validates that LLM-based agents achieve enterprise-grade reliability when augmented with robust web automation, comprehensive document understanding, and systematic SOP refinement.

A Knowledge-Enhanced LLM Framework for Power Semantic Understanding and Multi-Agent Decision Support

Power-system decision support increasingly requires the joint interpretation of heterogeneous information sources, including real-time measurements, topology states, alarm logs, operating rules, dispatch instructions, and historical restoration cases. Existing LLM-based approaches remain limited in such settings because their reasoning is often weakly grounded in power-domain knowledge and difficult to verify under physical and procedural constraints. To address this issue, we propose a knowledge-enhanced LLM framework for power semantic understanding and multi-agent decision learning. The framework first organizes heterogeneous grid information into a dynamic semantic graph, then performs graph-grounded semantic understanding, translates the inferred semantics into an executable task graph for hierarchical multi-agent collaboration, and finally applies safety-constrained verification with evidence tracing before action execution. Experiments on Grid2Op/L2RPN WCCI 2022, SMART-DS, RTS-GMLC, and PowerGym show that the proposed framework consistently improves semantic grounding accuracy, restoration quality, and feasible-decision rate over rule-based, text-centric RAG, and graph-retrieval multi-agent baselines, while exhibiting stronger robustness under partial observation. These results indicate that explicit semantic grounding and safety-aware collaborative reasoning are both necessary for reliable LLM-assisted power-system decision support.

Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback

Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve. Existing approaches address agent construction and one-time evaluation but provide no structured mechanism for continuous post-deployment behavioral correction without modifying the agent's source code. Most of the approaches offered in the market, require intense collection of logs and traces, and re-examining the agent design by the engineering team, a process which is heavy, long and negates the economical value of agentic transformation. We introduce Agent Gym, a modular, domain-agnostic framework that wraps any existing LLM-based agent in a continuous evaluation-and-evolution loop. The framework provides six composable capabilities --- Act, Evaluate, Investigate, Correct, Learn, and Observe --- organized across three architectural zones: a constitution layer that codifies domain knowledge in configuration artifacts, a runtime inference pipeline that chains acting, investigation, and adaptive correction, and a learning loop that enables subject matter experts to discover and validate new correction rules through natural language interaction. The key technical contributions include a hybrid deterministic-LLM correction engine with 21 condition operators and three-tier actions, a three-layer investigation architecture for ground-truth-free compliance validation, and a programmatic safety loop that guarantees rule correctness before human approval. We further introduce the Spec-to-Note Gap, an autoencoder-inspired view of agentic system transparency. An open-source reference implementation for invoice processing demonstrates that the framework is fully operational and ready for adoption.

A Multi-Agent Benchmarking Framework for Evaluating the Performance of Large Language Models in Logic Programming

A Multiagent Large Language Model–Based System for Early-Stage Building Layout Planning

A multiagent large language model (LLM)–based system for early-stage building layout planning, which enables flexible design requirement inputs and robust spatial reasoning and demonstrated significant improvements in both geometric quality and semantic alignment over a baseline LLM-only system.

TestAgent: A Multi-Agent LLM Framework for Repository-Level Unit Test Generation

TestAgent, a multi-agent tool implemented as a VS Code extension that automates the generation of high-quality unit tests for Java projects using repository-level Code Knowledge Graphs, demonstrates its practical utility for regression testing and bug discovery.