Help Without Being Asked: A Deployed Proactive Agent System for On-Call Support with Continuous Self-Improvement

📰 ArXiv cs.AI

Learn how to deploy a proactive agent system for on-call support with continuous self-improvement, reducing workload on human support analysts

advanced Published 14 Apr 2026

Action Steps

Deploy a proactive agent system using large language models to interact with customers
Implement continuous self-improvement mechanisms to refine the agent's performance
Configure the agent to handle on-call dialogues and resolve issues autonomously
Test and evaluate the agent's effectiveness in reducing workload on human support analysts
Apply machine learning algorithms to improve the agent's issue resolution capabilities

Who Needs to Know This

Support teams and developers can benefit from this system, as it automates issue resolution and improves response times

Key Insight

💡 Proactive agent systems can significantly reduce the workload on human support analysts by automating issue resolution and improving response times

Key Takeaways

Learn how to deploy a proactive agent system for on-call support with continuous self-improvement, reducing workload on human support analysts

Full Article

Title: Help Without Being Asked: A Deployed Proactive Agent System for On-Call Support with Continuous Self-Improvement

Abstract:
arXiv:2604.09579v1 Announce Type: new Abstract: In large-scale cloud service platforms, thousands of customer tickets are generated daily and are typically handled through on-call dialogues. This high volume of on-call interactions imposes a substantial workload on human support analysts. Recent studies have explored reactive agents that leverage large language models as a first line of support to interact with customers directly and resolve issues. However, when issues remain unresolved and are

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