Observability Agent

Workflow

Inspiration

Modern cloud infrastructure generates massive amounts of observability data (logs, metrics, traces). Teams struggle to correlate this data and quickly identify root causes of system failures. We envisioned an AI-powered agent that could autonomously analyze Elasticsearch data, detect anomalies, and suggest remediation steps.

What it does

Observability Agent is an intelligent system that:

Connects to Elasticsearch clusters to ingest observability data
Uses LLM-based reasoning to analyze patterns and anomalies
Generates real-time alerts with contextual insights
Provides remediation suggestions based on historical data
Offers a user-friendly dashboard to visualize findings

How we built it

Frontend: React-based interactive dashboard for real-time visualization Backend: Python FastAPI with Elasticsearch connectors for data analysis AI Engine: Integrated LLM agent with tool calling capabilities for autonomous decision-making Infrastructure: Cloud Run deployment for scalable, serverless execution

Challenges we faced

Efficiently querying large Elasticsearch datasets while maintaining real-time responsiveness
Designing accurate anomaly detection algorithms
Balancing agent autonomy with safety guardrails
Seamlessly integrating LLM reasoning with structured data queries