Technical Summary: Agentic Observability for Automated Alert Triage in Adobe E-Commerce

Problem Statement

Modern enterprise systems, particularly large-scale e-commerce platforms, exhibit complex interdependencies that make observability and incident response increasingly difficult. Despite advances in observability tooling, incident triage remains a largely manual process. Engineers must manually query logs, inspect dashboards, consult runbooks, and reason across heterogeneous data sources under intense time pressure. This human-centered workflow creates a significant bottleneck, contributing substantially to the Mean Time to Recovery (MTTR) and limiting situational awareness as critical context is fragmented across tools and data silos.

Existing automated approaches often operate reactively, performing structured root cause analysis (RCA) only after incidents are registered, or they rely on predefined troubleshooting workflows that lack integration with live telemetry. Furthermore, many conceptual frameworks for Agentic AIOps remain theoretical and unvalidated in production environments, failing to address the semantic heterogeneity and domain-specific nature of real-world error patterns.

Methodology

The paper presents an agentic observability framework deployed within Adobe's e-commerce infrastructure. The system utilizes a ReAct (Reasoning and Acting) paradigm orchestrated via LangGraph, employing three specialized GPT-4o agents to automate alert triage:

1. Splunk Agent: Upon receiving an alert, this agent extracts metadata (affected service, alert type, identifiers) and queries the Splunk API. It filters for error-level events and traces identifiers through downstream services to correlate distributed events, outputting a structured dataset of anomalies.
2. Tools Agent: Acting as the planner and reasoning engine, this agent interprets the alert metadata and log summaries. It identifies information gaps and defines sub-goals (e.g., verifying API responses, validating deployments). Using Retrieval-Augmented Generation (RAG), it integrates data from runbooks, internal wikis, and deployment metadata to formulate a stepwise action plan. It can autonomously execute diagnostic tasks and, where applicable, remediation steps (e.g., restarting jobs, clearing caches) with human oversight for high-risk operations.
3. Reflection Agent: This agent performs a meta-evaluation of the synthesized findings against three criteria: completeness, causality, and actionability. To prevent indefinite reasoning loops, the system imposes a maximum of five reflection cycles. If uncertainty persists beyond this limit, the system terminates and returns the most confident hypothesis with an explicit uncertainty tag.

The framework enables interleaved parallelism; for instance, while the Tools Agent reasons, the Splunk Agent can asynchronously fetch logs from downstream services, reducing total triage time.

Key Contributions

The paper advances the field of AIOps through three primary contributions:

• Proactive, Real-Time Alert Triage: Unlike existing RCA frameworks that act post-failure, this system performs diagnostic reasoning at the moment alerts are raised, eliminating post-failure latency and enabling earlier mitigation.
• Cross-Domain, Context-Aware Reasoning: The framework integrates heterogeneous enterprise sources—including Splunk logs, distributed traces, deployment metadata, and documentation—enabling reasoning over unstructured telemetry across services without relying solely on predefined workflows.
• Production-Validated Impact: The framework is deployed in a production environment (Adobe's e-commerce observability stack), moving beyond conceptual prototypes to demonstrate measurable operational impact.

Results

Empirical evaluation was conducted over a 12-week period across 250 alert events in diverse service categories (checkout, subscription management, catalog ingestion). The system was compared against two baselines: Manual Engineer Triage (average 18.4 minutes) and Manual Support Triage (average 33.2 minutes).

Key Performance Metrics:

• Mean Time to Insight (MTTI): The agentic system reduced MTTI to 2.3 minutes, representing a ten-fold reduction compared to manual engineer triage (18.4 minutes) and a significant improvement over support triage (33.2 minutes).
• Error Localization Accuracy (ELA): The system achieved 88.4% accuracy, comparable to expert engineers (82.4%) and significantly higher than general support analysts (55.6%).
• Engineer Effort Reduction (EER): The system automated 65% of manual triage steps, significantly reducing cognitive load.
• Alert Responsiveness (AR): The system generated initial diagnostic reports within five minutes for 90.4% of alerts.

Case Study:
In a specific case study involving "Content Validation Errors," the agent reduced the time to actionable diagnosis from 13.3 minutes (manual) to 1.8 minutes. It achieved an EER of 75% by automating log inspection, variant identification, and script execution, leaving only the final content modification and republishing for human engineers.

Significance and Claims

The paper claims that this work marks a pivotal shift toward autonomous observability in enterprise operations. By narrowing the gap between detection and insight, the framework demonstrates that agentic AI can achieve an order-of-magnitude reduction in triage latency while maintaining human-level diagnostic accuracy.

The authors emphasize that the system successfully operationalizes reasoning, retrieval, and action in a production setting, transforming reactive monitoring pipelines into proactive, self-correcting workflows. However, the paper maintains a modest tone regarding limitations, acknowledging that reasoning quality depends on log data fidelity, that API rate limits can cause temporary delays, and that onboarding new alerts still requires manual integration of runbooks and tools. The work serves as a validated proof-of-concept that domain-specific, context-grounded agentic frameworks can effectively augment human operators in high-stakes enterprise environments.