Description for AutoPilot Observability
AutoPilot Observability is an AI-native observability platform that automates system analysis, enhances operational efficiency, and troubleshoots issues across the entire tech stack by integrating OpenTelemetry and eBPF with large language models (LLM).
Features of Autopilot:
- Agentic Workflows for Observability: Low-code orchestration transforms user expertise into intelligent agents powered by AI.
- Seamless Data Source Integration: Connects to existing data sources via a data plane without requiring system changes.
- LLM-native Data Plane: Specifically designed for LLMs with deep AI Agent integration for smart system monitoring.
- Full-Stack Coverage: Monitors logs, traces, and metrics throughout the complete technology environment.
- Polaris Causal Metrics: Employs eBPF to identify root causes by addressing trace data gaps.
Use Cases for AutoPilot Observability
- Real-Time System Monitoring: Utilize AI-powered data pipelines to enable continuous observability.
- Automated Troubleshooting: Detect and resolve infrastructure issues without the need for manual intervention.
- DevOps Workflow Automation: Utilize drag-and-drop agentic workflows to streamline observability processes.
- Data Analysis and Querying: Execute intricate data operations with integrated query tools.
- Anomaly Detection: Utilize the built-in algorithms to identify performance issues and anomalous behavior.
FAQs for AutoPilot Observability
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