arXiv (All)
open access
AgenTest: Mining Business Rules to Test LLM Agents for Business Logic Bugs
Large language model (LLM) agents increasingly execute business operations through tools and interact directly with application data and services. Beyond completing user requests, these agents must obey domain-specific business rules that constrain when actions are permitted and what data or resourc
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- AgenTest: Mining Business Rules to Test LLM Agents for Business Logic Bugs#1406552
- LogicHunter: Testing LLM Agent Frameworks with an Agentic Oracle#346575
- Towards Agentic Agent-Based Models: Feasibility, Performance, and Statistical Model Checking#1360659
- SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills#361415
- Arbiter: Detecting Interference in LLM Agent System Prompts#1247347
Record · ID 1412091
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive