AIOps Platform Design: A 3-Layer Architecture for Observe-Engage-Act

Overall Design of an AIOps Platform: A 3-Layer Architecture Embodying Observe-Engage-Act

Modern IT systems are experiencing an exponential increase in data volume and complexity due to the widespread adoption of microservices and cloud-native technologies. Consequently, the traditional operational style of humans monitoring dashboards to detect anomalies and responding manually is reaching a "Cognitive Gap."

AIOps acts as the intelligent engine to break through this limit and elevate operations from "Reactive" to "Predictive/Autonomous." This article details the "3-Layer Architecture" for achieving autonomous operations, as proposed in my book, "AIOps Practice Guide."

1. The Foundational "Observe - Engage - Act" Framework

The core of AIOps is not merely the introduction of tools, but the construction of a logical cycle that transforms data into "action."

• Observe:

  Going beyond simple metric monitoring, this phase collects and integrates diverse telemetry data, including metrics (quantitative data), logs (event records), traces (distributed paths), events, and even topology that shows system dependencies. Consolidating these into a "Single Pane of Glass" is a prerequisite for subsequent advanced analysis.

• Engage:

  This is the process of applying AI and machine learning to the collected big data to extract the "meaning of information." It automates anomaly detection through time-series forecasting, correlation analysis between multiple alerts, and Root Cause Analysis (RCA). By doing so, it narrows down thousands of alert noises into a few "incidents" that require attention.

• Act:

  Based on the analysis results, concrete actions are executed. This includes automated ticketing in ITSM (Jira/ServiceNow), ChatOps via Slack, and even "Auto-remediation" using Ansible or Terraform. Depending on the level of trust, one can choose between requiring human approval or fully automating the process.

2. 3-Layer Structure of an AIOps Platform

To implement this logical cycle as a practical system, we define the platform using the following three physical layers.

① Data Layer (Ingestion & Storage)

This is the entry point for all data and the most critical layer that determines the accuracy of AIOps.

• OpenTelemetry: Responsible for the standardization and collection of data. To prevent "Garbage In, Garbage Out," cleansing and context enrichment via standard protocols are essential.

• Prometheus / Log Store: Retains metrics and logs in a scalable manner. From a DBRE perspective, it is particularly important to store database metrics such as query performance and lock contention in conjunction with the application layer.

② Analytics Layer (The Brain)

This is the heart of the platform that provides "intelligence" to the collected data.

• Stream Processing (Kafka/Flink): Processes incoming data in real-time to achieve high-immediacy anomaly detection.
• Model Training & Inference Engine: Infers whether the current situation is "abnormal" or "normal" based on machine learning models trained with historical data. The calculation of "dynamic baselines" that consider seasonality and trends is also performed here.

③ Action Layer (Integration)

This is the exit point that converts analysis results into business value (improved recovery speed).

• ITSM & ChatOps Integration: Delivers causes identified by AI and proposed repairs directly to the engineer's existing workflow (Slack or Jira).
• Automation Framework: For known issues (e.g., cache overflow, disk pressure), it automatically triggers predefined Runbooks (Ansible, etc.) to complete repairs before human intervention is required.

3. Three Requirements to Consider During Design

To build an effective AIOps foundation and maintain it over the long term, the following non-functional requirements must be incorporated into the design.

1. Scalability:

   To process TB/PB levels of telemetry data per day without latency, a loosely coupled design that allows horizontal scaling at every layer is required.

2. Real-time:

   To minimize the time from incident detection to resolution (reduction of MTTR), it is necessary to adopt an event-driven architecture rather than relying too heavily on batch processing.

3. Security & Reliability:

   To prevent unintended runaway processes caused by automation, Role-Based Access Control (RBAC), execution log auditing, and mechanisms for "Automated Rollback" must be implemented in the Action Layer.

Conclusion: Designing a Mechanism Where Data Turns into "Action"

The true value of AIOps does not lie in the cleverness of a single AI model. It lies in whether the pipeline, where data is automatically collected, analyzed, and channeled into concrete actions, is embedded within the organization's culture and technology stack. By defining the cycle from Observe to Act in code, you can evolve operations from a "defensive" posture into a "mechanism that accelerates business."

Transforming IT Operations into a Business Accelerator

In my book, "AIOps Practice Guide: The Evolution to Autonomous IT Operations," I systematize more detailed design guidelines for the architecture explained in this article, MLOps pipelines to keep AI models fresh, and practical approaches to overcoming the wall of data quality. Please grasp the hints for building the next-generation operational foundation in this book.