@inproceedings{dhanyamraju-etal-2026-autonomous,
title = "Autonomous Event Driven Multi-Agent Orchestration for Enterprise {AI} at Scale",
author = "Dhanyamraju, Harsh Rao and
Raghav, Leonidas and
Lee, Aaron",
editor = "Kamalloo, Ehsan and
Gontier, Nicolas and
Rozanov, Nikolai and
Hashemzadeh, Maryam and
Arora, Akhil",
booktitle = "Proceedings of the Second Workshop for {RE}search on Agent Language Models ({REALM} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.realm-1.27/",
pages = "398--434",
ISBN = "979-8-89176-485-9",
abstract = "Enterprise agent language models must coordinate specialist agents over continuous business-event streams, yet most multi-agent evaluations assume discrete request-response workflows. We present a controlled simulation based on a sanitized enterprise research snapshot: 208 scenarios, 393 events, and 1,051 expected agent calls across Persona ({\ensuremath{<}}10 agents), Department (20{--}80), and Enterprise (200) registries. We compare DAG Plan Execute with ReAct and evaluate a minimal Task Manager for priority inference, related-event merging, and preemption. Both systems perform well on the Persona cohort but score lower on the Enterprise cohort, especially for Simple scenarios; a larger, more confusable discovery space may contribute. The tested DAG Plan Execute implementation generally maintains higher agent-call precision, whereas ReAct shows stronger recall and Expected Answer Completeness in several Enterprise conditions. The Task Manager reduces high-priority queue latency by 14{--}75{\%} and improves related-event Expected Answer Completeness by over 20 percentage points at Enterprise scale. These controlled simulation results characterize the tested systems but do not establish production readiness."
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<abstract>Enterprise agent language models must coordinate specialist agents over continuous business-event streams, yet most multi-agent evaluations assume discrete request-response workflows. We present a controlled simulation based on a sanitized enterprise research snapshot: 208 scenarios, 393 events, and 1,051 expected agent calls across Persona (\ensuremath<10 agents), Department (20–80), and Enterprise (200) registries. We compare DAG Plan Execute with ReAct and evaluate a minimal Task Manager for priority inference, related-event merging, and preemption. Both systems perform well on the Persona cohort but score lower on the Enterprise cohort, especially for Simple scenarios; a larger, more confusable discovery space may contribute. The tested DAG Plan Execute implementation generally maintains higher agent-call precision, whereas ReAct shows stronger recall and Expected Answer Completeness in several Enterprise conditions. The Task Manager reduces high-priority queue latency by 14–75% and improves related-event Expected Answer Completeness by over 20 percentage points at Enterprise scale. These controlled simulation results characterize the tested systems but do not establish production readiness.</abstract>
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%0 Conference Proceedings
%T Autonomous Event Driven Multi-Agent Orchestration for Enterprise AI at Scale
%A Dhanyamraju, Harsh Rao
%A Raghav, Leonidas
%A Lee, Aaron
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Rozanov, Nikolai
%Y Hashemzadeh, Maryam
%Y Arora, Akhil
%S Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-485-9
%F dhanyamraju-etal-2026-autonomous
%X Enterprise agent language models must coordinate specialist agents over continuous business-event streams, yet most multi-agent evaluations assume discrete request-response workflows. We present a controlled simulation based on a sanitized enterprise research snapshot: 208 scenarios, 393 events, and 1,051 expected agent calls across Persona (\ensuremath<10 agents), Department (20–80), and Enterprise (200) registries. We compare DAG Plan Execute with ReAct and evaluate a minimal Task Manager for priority inference, related-event merging, and preemption. Both systems perform well on the Persona cohort but score lower on the Enterprise cohort, especially for Simple scenarios; a larger, more confusable discovery space may contribute. The tested DAG Plan Execute implementation generally maintains higher agent-call precision, whereas ReAct shows stronger recall and Expected Answer Completeness in several Enterprise conditions. The Task Manager reduces high-priority queue latency by 14–75% and improves related-event Expected Answer Completeness by over 20 percentage points at Enterprise scale. These controlled simulation results characterize the tested systems but do not establish production readiness.
%U https://aclanthology.org/2026.realm-1.27/
%P 398-434
Markdown (Informal)
[Autonomous Event Driven Multi-Agent Orchestration for Enterprise AI at Scale](https://aclanthology.org/2026.realm-1.27/) (Dhanyamraju et al., REALM 2026)
ACL