2 citations · 3 across the 6 of their papers we have counts for
7 papers
ResonAct: Streaming Metrics for Runtime Diagnosis and Self-Healing in Multi-Agent Systems
Tarun Chintada, Neelamadhav Gantayat, Ishaan Romil +3
Multi-agent systems (MAS) are increasingly used to automate enterprise workflows involving multiple specialized agents, external tools, and long-running task execution. Failures ma…
Agentic DAG-Orchestrated Planner Framework for Multi-Modal, Multi-Hop Question Answering in Hybrid Data Lakes
Kirushikesh D B, Manish Kesarwani, Nishtha Madaan +6
Enterprises increasingly need natural language (NL) question answering over hybrid data lakes that combine structured tables and unstructured documents. Current deployed solutions,…
Can AI autonomously build, operate, and use the entire data stack?
Arvind Agarwal, Lisa Amini, Sameep Mehta +2
Enterprise data management is a monumental task. It spans data architecture and systems, integration, quality, governance, and continuous improvement. While AI assistants can help…
Classifier-Augmented Generation for Structured Workflow Prediction
Thomas Gschwind, Shramona Chakraborty, Nitin Gupta +1
ETL (Extract, Transform, Load) tools such as IBM DataStage allow users to visually assemble complex data workflows, but configuring stages and their properties remains time consumi…
Automated Creation and Enrichment Framework for Improved Invocation of Enterprise APIs as Tools
Prerna Agarwal, Himanshu Gupta, Soujanya Soni +3
Recent advancements in Large Language Models (LLMs) has lead to the development of agents capable of complex reasoning and interaction with external tools. In enterprise contexts,…
A Framework for Testing and Adapting REST APIs as LLM Tools
Jayachandu Bandlamudi, Ritwik Chaudhuri, Neelamadhav Gantayat +5
Large Language Models (LLMs) are increasingly used to build autonomous agents that perform complex tasks with external tools, often exposed through APIs in enterprise systems. Dire…
xLP: Explainable Link Prediction for Master Data Management
Balaji Ganesan, Matheen Ahmed Pasha, Srinivasa Parkala +6
Explaining neural model predictions to users requires creativity. Especially in enterprise applications, where there are costs associated with users' time, and their trust in the m…