Turning Alert Noise into Actionable Insight

by

Author: Denis Avetisyan

A new approach to observability leverages intelligent agents to dramatically accelerate issue resolution in complex e-commerce systems.

This paper details an agentic observability framework for automated alert triage and root cause analysis in Adobe e-commerce infrastructure, achieving a 90% reduction in Mean Time to Insight.

Modern enterprise systems, despite increasing complexity, often rely on manual processes for incident response, creating a significant bottleneck in reducing recovery time. This paper introduces ‘Agentic Observability: Automated Alert Triage for Adobe E-Commerce’, a novel framework leveraging an agentic approach to autonomously triage alerts within a large-scale e-commerce infrastructure. Empirical results demonstrate a 90% reduction in mean time to insight compared to manual triage, while maintaining comparable diagnostic accuracy. Could this paradigm shift toward autonomous observability fundamentally reshape how enterprises approach operational resilience and system stability?

The Inevitable Cascade: Modern Observability’s Breaking Point

As digital infrastructure expands in scale and intricacy, traditional methods of incident response are increasingly overwhelmed. The sheer volume of alerts generated by modern, distributed systems often leads to ‘alert fatigue’, where critical signals are lost amidst a sea of noise. Consequently, response teams struggle to quickly identify the root cause of issues, leading to prolonged resolution times and degraded service performance. This challenge isn’t simply about more data, but the inability of human operators to effectively process and correlate information quickly enough to prevent minor incidents from escalating into major outages. The limitations of manual triage, coupled with the speed at which modern applications operate, create a significant bottleneck that demands a shift towards automated, intelligent observability solutions.

The escalating complexity of modern systems is rapidly overwhelming traditional methods of incident response, as manual triage and root cause analysis become increasingly unsustainable practices. Previously viable approaches now struggle to keep pace with the sheer volume of data and the speed at which issues arise, leading to analyst fatigue and delayed resolutions. Consequently, organizations are actively seeking automated and intelligent solutions – leveraging machine learning and artificial intelligence – to filter noise, correlate events, and pinpoint the underlying causes of incidents. These systems aim to not only identify problems faster but also to proactively prevent them, shifting the focus from reactive troubleshooting to preventative maintenance and ensuring consistently high service availability.

Modern observability transcends simply gathering telemetry data; its true power lies in transforming that raw information into actionable intelligence. Systems now generate vast quantities of logs, metrics, and traces, but these are meaningless without robust analytical capabilities to correlate events, identify patterns, and pinpoint root causes. This requires sophisticated tools and techniques – such as machine learning and anomaly detection – that can automatically synthesize insights from complex datasets. Ultimately, effective observability isn’t about more data, but about deriving meaning from it, enabling proactive responses and automated remediation to maintain optimal system performance and swiftly address emerging issues before they escalate into full-blown incidents.

Maintaining peak service performance increasingly hinges on minimizing both Mean Time To Insight (MTTI) and Mean Time To Recovery (MTTR). Prolonged incident response times are no longer acceptable in fast-paced digital environments; current reliance on manual investigation frequently results in an MTTI of 18 to 30 minutes – a substantial delay when seconds can equate to lost revenue or damaged reputation. This lag stems from the sheer volume of alerts overwhelming on-call personnel, forcing them to sift through noise instead of focusing on genuine issues. Consequently, organizations are actively seeking automated solutions that can rapidly correlate data, pinpoint root causes, and initiate remediation, effectively compressing these critical timeframes and bolstering overall system resilience.

The Automated Sentinel: An Agentic Framework Emerges

The Agentic Observability Framework addresses the challenges of high alert volume and complex dependencies within the Adobe E-commerce Ecosystem by automating the initial stages of incident response. This automation encompasses both alert triage – the process of prioritizing and categorizing alerts – and root cause analysis, which identifies the underlying reason for an issue. By reducing the need for manual intervention in these processes, the framework aims to decrease mean time to resolution (MTTR) and improve overall system reliability. The system is designed to function across various Adobe Commerce components, including order management, product catalogs, and customer profiles, offering a unified approach to observability and incident management.

The Agentic Observability Framework employs a ReAct – Reasoning and Acting – paradigm to facilitate automated alert triage and root cause analysis. This approach allows agents to not simply react to alerts, but to actively reason about them by generating thought processes, then executing actions based on those thoughts. The cycle of reasoning and acting is iterative; observations from actions inform subsequent reasoning steps. This enables the framework to move beyond pre-defined workflows and dynamically address complex issues within the Adobe E-commerce Ecosystem by determining the appropriate course of action based on available data and the current state of the system.

The Agentic Observability Framework leverages LangGraph as its orchestration layer, enabling coordinated operation of multiple GPT-4o agents. Specifically, the framework utilizes a Tools Agent, responsible for executing actions and retrieving data from external systems, and a Reflection Agent, designed to analyze the Tools Agent’s actions and refine subsequent steps. LangGraph manages the communication and data flow between these agents, ensuring a cohesive and iterative process for alert triage and root cause analysis. This agent-based architecture allows for complex problem-solving by distributing tasks and leveraging the specialized capabilities of each agent within the framework.

The Agentic Observability Framework relies on a Splunk Agent to ingest critical log data through the Splunk API. This agent functions as the primary data retrieval mechanism, querying Splunk instances for relevant logs based on incoming alerts and the reasoning processes of the framework’s GPT-4o agents. The retrieved logs, encompassing application, server, and network activity, are then provided to agents like the Tools Agent and Reflection Agent, enabling them to perform root cause analysis and automate alert triage. The Splunk API integration allows for efficient and scalable access to log data, which is essential for the framework’s ability to correlate events and identify the underlying causes of issues within the Adobe E-commerce Ecosystem.

The Autopsy in Detail: Automated Root Cause Analysis Unveiled

The Tools Agent utilizes Retrieval-Augmented Generation (RAG) to enhance its analytical abilities by dynamically accessing and incorporating information from existing runbooks and documentation stores. This process involves retrieving relevant passages based on the current incident context, and then using those passages as additional context for the Large Language Model (LLM) during reasoning. By grounding its responses in verified, pre-existing knowledge, RAG mitigates the risk of hallucination and improves the accuracy and reliability of the agent’s conclusions. The retrieved information is not simply presented to the user, but is directly integrated into the LLM’s prompt, effectively expanding its knowledge base and enabling more informed decision-making during root cause analysis.

The agentic framework for automated Root Cause Analysis (RCA) integrates with existing RCA tools such as RCACopilot, IRCopilot, StepFly, and FLASH to provide a structured approach to incident investigation. These systems offer pre-built diagnostic trees, knowledge bases of common issues, and automated data collection capabilities. By leveraging the established workflows and datasets within these platforms, the agent can systematically analyze incident data, correlate events, and formulate potential root causes. This integration minimizes the need for manual data gathering and analysis, accelerating the RCA process and ensuring consistency in diagnostic procedures. The tools provide a foundational structure for the agent to operate within, enabling it to reason about incidents in a defined and predictable manner.

The Reflection Agent operates as a critical post-analysis component, conducting a meta-evaluation of the Root Cause Analysis (RCA) generated by other agents. This evaluation centers on three key criteria: completeness, verifying that all relevant data points have been considered; causality, confirming a demonstrable link between identified root causes and the observed incident; and actionability, ensuring that proposed resolutions are practical and directly address the determined root causes. The agent systematically reviews the RCA output, flagging any deficiencies in these areas and prompting further investigation or refinement as needed, ultimately increasing the reliability and effectiveness of the incident resolution process.

Automation of root cause analysis, facilitated by this framework, demonstrably decreases incident resolution times, specifically yielding a 90% reduction in Mean Time to Insight (MTTI). This metric, representing the duration from incident detection to understanding the underlying cause, is improved through the streamlined, agentic process. The reduction is achieved by eliminating manual data gathering, correlation, and analysis traditionally required for RCA, enabling faster identification of critical factors and accelerating the path to remediation. This improvement in MTTI directly translates to minimized downtime and reduced operational costs.

The Inevitable Outcome: Impact and the Path Forward

The Agentic Observability Framework demonstrably minimizes service interruptions and elevates customer experience through substantial reductions in both Mean Time To Identify (MTTI) and Mean Time To Resolve (MTTR). Rigorous testing revealed an impressively swift MTTI of just two minutes, indicating the system’s capacity for near-instantaneous problem detection. This rapid identification, coupled with streamlined resolution processes, translates directly into fewer disruptions and a more reliable service for end-users. By swiftly pinpointing and addressing issues, the framework not only limits the impact of incidents but also fosters increased trust and satisfaction among those who rely on the service.

The implementation of an automated observability framework demonstrably shifts engineering focus from reactive problem-solving to proactive system enhancement. By autonomously handling initial alert triage and diagnosis, the framework alleviates a substantial burden on engineering teams, achieving a 65% reduction in effort across all alerts. This reclaimed time empowers engineers to concentrate on strategic initiatives – optimizing system performance, developing innovative features, and strengthening overall system resilience – rather than being constantly consumed by incident response. The result is not simply faster issue resolution, but a fundamental change in how engineering resources are allocated, fostering a culture of continuous improvement and preventative maintenance.

The Agentic Observability Framework signifies a notable advancement in the pursuit of self-healing systems and autonomous incident management. By automating initial diagnosis and reducing reliance on manual intervention, the framework moves beyond simply alerting engineers to actively addressing issues. This capability isn’t merely about faster response times – achieving a diagnostic report within five minutes for 90% of alerts – but about shifting the paradigm from reactive troubleshooting to proactive remediation. The system’s demonstrated ability to maintain diagnostic accuracy comparable to human experts, coupled with substantial reductions in engineer effort, suggests a future where systems can independently identify, diagnose, and resolve a growing number of incidents, ultimately minimizing downtime and enhancing overall system resilience. This represents a crucial step toward more robust and self-sufficient infrastructure, freeing up valuable engineering resources for innovation and strategic improvements.

The Agentic Observability Framework demonstrates a remarkable capacity for automated diagnostics, achieving an Error Localization Accuracy (ELA) of 88.4% – a performance level comparable to that of experienced engineers. This capability is coupled with exceptional Alert Responsiveness, generating an initial diagnostic report for 90% of alerts within a mere five minutes. Notably, the framework delivers substantial efficiency gains; engineer effort related to Content Validation Error alerts is reduced by 75%, suggesting a significant potential for streamlining operations and freeing up valuable resources without compromising diagnostic precision.

The pursuit of automated alert triage, as detailed in the agentic observability framework, reveals a predictable pattern. Systems designed for static perfection invariably succumb to the entropy inherent in complex e-commerce infrastructures. As Carl Friedrich Gauss observed, “If I have to wait for a miracle, I would rather rely on my own efforts.” This framework doesn’t prevent failures – it acknowledges them as inevitable – but dramatically reduces Mean Time to Insight by proactively analyzing and contextualizing alerts. The system doesn’t seek to eliminate chaos, but to navigate it with increased efficiency, mirroring a pragmatic acceptance of systemic decay rather than a naive belief in flawless architecture.

What Lies Ahead?

The automation of alert triage, as demonstrated, is not a destination, but a postponement. It is architecture, how one delays the inevitable return to chaos. A ninety percent reduction in Mean Time to Insight merely shifts the burden – the system doesn’t solve problems, it concentrates them, demanding ever more sophisticated layers of abstraction to mask the underlying entropy. The true measure of such systems will not be speed of response, but the elegance of their failures.

Current approaches, focused on pattern recognition and correlation, treat symptoms, not causes. The next evolution will require systems capable of genuine causal reasoning – a move beyond observing that something failed, to understanding why it failed, and, crucially, how that failure propagates. There are no best practices-only survivors. The pursuit of “root cause” is a comforting fiction; systems are not trees with single roots, but rhizomes, endlessly branching and interconnected.

Ultimately, the field must confront the inherent limitations of prediction. Order is just cache between two outages. The most resilient architectures will not attempt to eliminate uncertainty, but to embrace it, to build systems that degrade gracefully, and to learn from every iteration of failure. The future is not about preventing incidents, but about shortening the distance between incident and adaptation.

Original article: https://arxiv.org/pdf/2602.02585.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-04 16:03