Operations: AIOps & RCA

Goal: Reduce MTTR (Mean Time To Resolution) from hours to minutes. We shift from “staring at dashboards” to “reviewing AI diagnoses.”

The Workflow

graph TD
    App[Application] -->|Logs| Observability["Azure Monitor / Datadog"]
    Observability -->|"Alert (High CPU)"| Alerter[Alert Manager]
    Alerter -->|Trigger| RCA[RCA Agent]
    RCA -->|Query| Logs[Log Store]
    RCA -->|Query| Code[GitHub Repo]
    RCA -->|Analysis| Draft[Incident Report]
    Draft -->|Suggest| Fix[Remediation Script]
    
    style RCA fill:#b3e5fc
    style Draft fill:#fff9c4
    style Fix fill:#c8e6c9

Tools Used

• Observability: Azure Monitor, Datadog.
• LLM Integration: LangChain (connecting logs to LLM), Langfuse (tracing AI apps).
• Log Analytics: KQL (Kusto Query Language) generated by AI.

Step-by-Step Implementation

1. Log Collection: Ensure structural logging (JSON) so AI can parse it easily.
2. Anomaly Detection: Use built-in AI in Azure Monitor/Datadog to find “unknown unknowns” (e.g., weird latency pattern).
3. RCA Agent: Build a simple agent that triggers on P1 alerts.
4. Automated Remediation: Allow the agent to restart pods or clear caches (with safeguards).

Example Scenario: Database Latency Spike

Alert: “SQL Query duration > 2s on high load.”

1. Root Cause Analysis (RCA)

Process: The RCA Agent receives the alert payload.

Agent Action:

1. Queries the DB logs for the timestamp.
2. Identifies the slow query: SELECT * FROM Orders WHERE ...
3. Checks git log for recent changes to that query.
4. Analyzes the execution plan.

Agent Output (Slack Message):

Incident: #1234 - High Latency Cause: Missing Index on OrderDate column. Evidence: Query execution time jumped from 20ms to 2000ms after Deployment #55. Proposed Fix: CREATE INDEX idx_order_date ON Orders(OrderDate);

2. Automated Remediation

Human: “Approved.” (Clicks button in Slack).

Agent: Executes the script against the DB (or creates a Hotfix PR).

Implementation Guidelines

• Sanitize Logs: Before sending logs to an LLM, ensure PII masking is active. You don’t want to send customer emails to OpenAI.
• Context Window: You can’t send all logs. Send the error stack trace and the 50 lines preceding it.
• Feedback Loop: If the AI’s diagnosis is wrong, tell it. “No, it wasn’t the index, it was the disk I/O.”

Key Pitfalls

Noise

If you alert on everything, the AI will hallucinate causes for non-issues. Only trigger RCA on clear, actionable signals.

Key Takeaways

1. Sleep Better: AIOps handles the “3 AM” alerts that validly just need a restart.
2. Knowledge Capture: The “Incident Report” generated by AI becomes part of your knowledge base for future training.
3. Proactive vs Reactive: AI can predict “Disk will fill up in 2 days” based on trends, allowing you to fix it before it breaks.