In today’s multi-cloud environment, businesses often deploy applications on platforms like Azure, AWS, and GCP to meet diverse workload needs. Managing observability across such environments is critical for maintaining performance and reliability.
The Observability AI Application, powered by Agentic AI using SambaNova Cloud Models, simplifies this challenge by unifying log monitoring, analysis, and insights for three core applications:
- Meddy (hosted on Azure)
- LegalExpert (hosted on AWS)
- Flash (hosted on GCP)
- And able to expand/re-use for no.of.applications that need to be monitored.
Logs from these applications are centrally aggregated into AWS OpenSearch, enabling seamless observability and AI-driven insights across all platforms.
How It Works
- User Query Input:
Users interact with the Streamlit UI to input natural language queries about the observability of the three applications, each powered by SambaNova Cloud models. The system listens actively to these queries. - Autonomous DSL Query Construction:
Agentic AI (powered by Meta-Llama-3.1-405B-Instruct) autonomously interprets the user’s query and translates it into a precise Domain-Specific Language (DSL) This transformation is dynamically tailored based on the user’s intent, including specific application insights, severity, performance, or error trends, without manual intervention. - Dynamic Log Retrieval from AWS OpenSearch:
The DSL query fetches relevant logs from the centralized AWS OpenSearch by unique Index, regardless of the application’s hosting environment. - AI-Powered Analysis and Insights:
Meta-Llama-3.2-1B-Instruct is the Agentic AI responsible for analysing the logs and providing a high-level summary, identifying anomalies, extracting insights, and offering recommendations based on user’s query. - Visual Insights:
The results are displayed on the Streamlit UI, providing real-time, actionable insights in a clear and user-friendly format.
X – Factors
- Support for Multi-Cloud Applications:
- Meddy (Azure), LegalExpert (AWS), and Flash (GCP) are seamlessly integrated into a unified observability system.
- LLM-Powered Agentic AI Workflow: Leverages multi-models with Meta-Llama-3.1-405B for Autonomous Decision Making and Meta-Llama-3.2-1B for Proactive Insights Generation of summarization, anomaly detection, and optimization recommendations that were not explicitly requested by the user.
- End-to-End Automation: From translating the query to fetching data and generating insights, the system handles all tasks autonomously, reducing the need for human intervention and optimizing operational efficiency.
- Adding and integrating new apps / log files is as simple as integrate it into Open Search platform and by default our apps starts to Observe it.
Reusability
This application is highly reusable and can be adapted to monitor multiple applications across various environments, leveraging its AI-driven capabilities to detect bottlenecks effectively.
Reuse
- Monitor any number of applications by extending log ingestion pipelines to additional sources.
- Ensure centralized log aggregation and AI analysis for seamless scalability.
- Provide actionable insights and anomaly detection for diverse workloads.
Future Enhancement
- Allow for another app’s log to upload into our app, it will quickly analyze tell us what’s going on to move things a little faster.
- Recommendations and Corrections: Enhance the application to not only detect bottlenecks but also suggest fixes, optimizing performance with AI-driven recommendations.
- Continuous Monitoring and Automation:
- Automate log monitoring to run continuously, ensuring real-time detection of issues.
- Integrate alerting systems to notify administrators about critical bottlenecks.
- Provide automated fixes or suggested resolutions for recurring issues.
- Each app sets up an agent, and the master orchestrator decides which agent should process that operation/workflow.
Team and Development Timeline
Team Composition:
- Python Developer: Focused on backend integration and log processing.
- Cloud Engineer: Managed AWS deployment and multi-cloud connectivity.
- AI Specialist: Integrate AI models for log analysis and insights.
Timeline:
- Multi-Cloud Setup: 3 days
- AI Integration: 2 days
- UI Development with Streamlit: 2 days
Architecture Overview
Conclusion
The Observability AI Application is a game-changer for businesses managing multi-cloud environments by leveraging multi-models. With centralized log aggregation, Streamlit-powered visualizations, and AI-driven insights, it simplifies monitoring and accelerates troubleshooting across platforms.
Deployed on AWS, it ensures reliability, scalability, and an intuitive user experience, empowering teams to focus on delivering value rather than managing complexity.
Empower your observability strategy with this innovative, agent-driven solution.