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LLM Agents Grounded in Business Processes for Reliable API Invocation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method to enhance the reliability of Large Language Model (LLM) agents interacting with enterprise APIs. This approach integrates Open Data Protocol (OData) metadata and Business Process Model and Notation (BPMN) into a typed property graph. The system uses a hybrid retriever that combines dense embeddings with process-aware navigation to identify subsequent endpoints, significantly improving the success rate of multi-step API calls, especially for queries with low lexical overlap. Evaluations show a substantial increase in task completion rates for LLM agents using this method compared to baseline approaches, demonstrating its effectiveness in reducing errors and ensuring adherence to process and schema constraints. AI

IMPACT Enhances LLM agent reliability in enterprise API interactions, potentially improving automation and efficiency.

RANK_REASON Research paper detailing a new method for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM Agents Grounded in Business Processes for Reliable API Invocation

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Research paper detailing a new method for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Saiful Khan, Afrah Farea ·

    Grounding LLM Agents in Business Processes: Process-Aware Knowledge Graphs for Reliable Multi-Step Service Invocation

    arXiv:2610.37242v1 Announce Type: new Abstract: Large language model (LLM) agents that call enterprise APIs must select the correct endpoints, follow process order, use valid payload keys, and choose coded values from predefined lists. However, current retrieval-augmented service…

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