# Agentic Observability: Automated Alert Triage for Adobe E-Commerce

arXiv: https://arxiv.org/abs/2602.02585 · HTML: https://mlsanity.com/paper/2602.02585/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 1 of 1)

- [CORTEX: Collaborative LLM Agents for High-Stakes Alert Triage](https://mlsanity.com/paper/2510.00311/) (2025)

## Built on by (most cited first, 2 of 2)

- [Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems](https://mlsanity.com/paper/2607.08010/) (2026)
- [SRE-Marathon: A Continuous, Change-Driven Benchmark for Autonomous Site Reliability Agents](https://mlsanity.com/paper/2609.33023/) (2026)

## Abstract (arXiv)

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.