Agentic Observability: Automated Alert Triage for Adobe E-Commerce

• URL: http://arxiv.org/abs/2602.02585v1
• Date: Sat, 31 Jan 2026 20:20:02 GMT
• Title: Agentic Observability: Automated Alert Triage for Adobe E-Commerce
• Authors: Aprameya Bharadwaj, Kyle Tu,
• Abstract summary: This paper presents an agentic observability framework deployed within Adobe's e-commerce infrastructure.<n>The framework autonomously performs alert triage using a ReAct paradigm.<n>Our results show that agentic AI enables an order-of-magnitude reduction in triage latency and a step-change in resolution accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern enterprise systems exhibit complex interdependencies that make observability and incident response increasingly challenging. Manual alert triage, which typically involves log inspection, API verification, and cross-referencing operational knowledge bases, remains a major bottleneck in reducing mean recovery time (MTTR). This paper presents an agentic observability framework deployed within Adobe's e-commerce infrastructure that autonomously performs alert triage using a ReAct paradigm. Upon alert detection, the agent dynamically identifies the affected service, retrieves and analyzes correlated logs across distributed systems, and plans context-dependent actions such as handbook consultation, runbook execution, or retrieval-augmented analysis of recently deployed code. Empirical results from production deployment indicate a 90% reduction in mean time to insight compared to manual triage, while maintaining comparable diagnostic accuracy. Our results show that agentic AI enables an order-of-magnitude reduction in triage latency and a step-change in resolution accuracy, marking a pivotal shift toward autonomous observability in enterprise operations.

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