AgentOps for Governed Enterprise Service Delivery
DOI:
https://doi.org/10.5281/3e939t86Keywords:
Governed Enterprise AI; Intelligent Automation; IT Service Management; Human Resources Service Delivery; Safety; Compliance; Risk Management; Data Management; Large Language Models; Multi-agent Systems; ChatGPT; Negotiation; Orchestration; Task Taxonomy; ITSM; HRSD.Abstract
Large language models (LLMs) are driving a rapid evolution in intelligent automation across industry sectors. Organizations are increasingly deploying LLMs to streamline operations by automating and augmenting human tasks and decision-making, with applications spanning the enterprise—particularly in service areas like IT service management (ITSM) and human resources service delivery (HRSD), where workflows tend to follow predictable, repetitive patterns. Yet automation becomes significantly more challenging in governed enterprise environments, where data privacy, security, and risk management are paramount; safety, compliance, and effective risk management must therefore be built into any advanced enterprise automation solution, not added as an afterthought.
Multi-agent LLM systems offer an effective framework for automating complex tasks while preserving this governance. Using a dedicated ITSM and HRSD task taxonomy, we show that multi-agent systems can automate a substantial share of incidents and service requests. Clearly defined agent roles and responsibilities help ensure data provenance, privacy, and security throughout the process: a central orchestrator manages end-to-end automation, while specialized agents handle dedicated functions such as safety and compliance monitoring for tasks involving sensitive data or materials.
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