Modern monitoring systems excel at detecting anomalies, but they still leave engineers with the most time-consuming task: understanding what happened and determining whether an alert requires action.

In our production environment, every alert triggered a manual investigation workflow. Engineers had to open multiple dashboards, compare correlated metrics, analyze dimensional breakdowns, search through operational documentation, and manually determine business impact before escalating findings. Although anomaly detection was automated, incident triage remained largely manual.

To address this challenge, we built an Agentic Monitoring platform that transforms alerts into actionable investigations.

The system combines multiple specialized AI agents that collaborate to analyze alerts, retrieve operational context, perform metric correlation analysis, identify impacted business dimensions, and generate structured investigation summaries. Rather than replacing engineers, the agents act as investigative assistants that significantly reduce the effort required to validate incidents.

In this session, I will share the architecture behind our production agents:

1. Diagnosis Agent: The agent retrieves operational documentation and SOPs and identifies relevant troubleshooting procedures to covert them into structured investigation plans

2. Alert Enrichment Agent: Analyzes correlated metric behavior and detects dimension-level anomalies. It then identifies impacted business segments. Generates investigation summaries and remediation guidance

We will discuss how these agents are orchestrated, how monitoring data is transformed into agent-friendly context, and how we addressed common challenges such as hallucination prevention, grounding responses with operational data, latency optimization, and trustworthiness in production environments.

The session will include architecture diagrams, implementation patterns, operational lessons learned, and quantitative impact metrics. Since deployment, the platform has reduced alert validation time from more than 20 to 30 minutes to less than 5 minutes per incident while significantly reducing dashboard context switching and manual correlation analysis. The result is faster incident response, improved engineer productivity, and more consistent operational decision making.

Attendees will benefit with practical patterns for designing agentic systems that augment operational workflows and can be applied to monitoring, observability, incident management, and site reliability engineering