AutoPilot Observability is an AI-native observability platform that proactively solves problems, reduces manual effort, and accelerates system reliability using LLM-based agents.
Features
Agentic Workflows
Low-code orchestration transforms your expertise into dynamic workflows that power intelligent agents, simplifying complex processes and improving productivity.
LLM-native Data Plane
A purpose-built data plane for LLMs with deep AI Agent integration for intelligent system monitoring, optimizing the efficiency and effectiveness of monitoring processes.
Seamless Integration
Supports easy connection to existing data sources without system modifications, allowing users to leverage the data plane effortlessly.
Full-Stack Coverage
Provides unified monitoring of logs, traces, and metrics across your entire technology stack for comprehensive visibility and faster troubleshooting.
10X Cost-Effective
Reduces operational costs through intelligent data handling and efficient resource allocation, maximizing value while minimizing expenses.
Open Source
Built on transparency and collaboration, inviting contributions from the community to drive innovation and improvement.
Integrated Anomaly Detection
Employs advanced algorithms for anomaly detection and data correlation, providing accurate and timely insights.
Multi-source Data Correlation
Allows anomaly detection and noise reduction across multiple sources without requiring navigation between pages, ensuring focus on critical insights.
Guided Automation
Automated fault resolution with clear, explainable AI-driven processes to reduce MTTR and enhance reliability.
Intelligent Data Consolidation
Optimizes data by consolidating and refining raw observability data to highlight significant issues, reducing noise and focusing on critical concerns.
AI Agentic workflow orchestration
Enables automation of workflows using AI to streamline operations and improve efficiency.
OpenTelemetry integration
Offers seamless integration with OpenTelemetry for standardized observability across different platforms.
Advanced alert analysis
Delivers intelligent alert processing and categorization for improved response and issue tracking.
Polaris metrics system
Facilitates advanced metrics collection and analysis to enhance performance monitoring.
Root cause confirmation
Automates the identification of the root cause of problems to enable quicker resolution.
AI Agentic Workflows Designed for Observability
A low-code AI agent workflow framework with intuitive drag-and-drop nodes for creating and managing workflows.
Integrated Anomaly Detection Tools
Advanced algorithms for anomaly detection and data correlation.
Guided Automation with Explainability
Automated fault resolution with transparent reasoning behind AI recommendations.