AI-Enabled Platform Observability Accelerate Platform Development
Platform observability is crucial in accelerating platform development by facilitating faster issue resolution, improving collaboration and communication, enabling continuous improvement, proactively optimizing performance, streamlining testing, supporting data-driven decision-making, and ensuring reliable infrastructure and deployment processes.
Agivant AI First Digital Engineering Solutions integrates multidimensional observability capabilities within the engineering life cycle by leveraging our strategic partnerships with Datadog and Splunk products.
Agivant POV Around Ovesrvability
Agivant focus on implementing the Al First approach to maximize the ROI by automating all possible use cases to optimize engineer bandwidth and investments
Address Business Use Cases
- Realtime monitoring of machines, network, services usage patterns
- Enable more accurate capacity planning
- Proactive alerts on service health/downtimes/
- Performance degradation
- Service maturity index to prioritize their modernization efforts
- Incident correlation to accelerate the resolutions of a Service dependency tree
- Help in business planning, cost management, load transfer, and right-sizing of infrastructure
- Proactive detection of security/compliance concerns
- Increase productivity of SRE, Operation Engineers
- Develop a platform for the engineering team to improve their releases and plan future ones effectively.
- dentify services that can be retired, needs to be modernized
- Recommend scenarios for automation/self-healing
This system seamlessly manages servers, virtual machines, containers, storage systems, and network devices by tracking resource utilization, identifying bottlenecks, and ensuring optimal resource allocation.
APM tools collect data on response times, throughput, errors, and other relevant metrics. This data helps identify performance bottlenecks, optimize code, and improve the overall user experience
Logs provide valuable information about system events, errors, warnings, and user activities. Effective log management enables organizations to troubleshoot issues, identify security threats, and gain insights into system behavior.
Collecting and visualizing metrics related to the platform’s performance, resource utilization, and user behavior. Metrics include CPU and memory usage, network traffic, and database performance.
Traces requests as they flow through multiple components and microservices, providing insights into latency, bottlenecks, and dependencies between system components. Distributed tracing helps in identifying performance issues and optimizing system performance.
Define thresholds or conditions for triggering alerts based on specific metrics. That ensures responsible teams can immediately address issues and minimize downtime or disruptions.
Identify underutilized resources, predict future resource needs, and make informed decisions about scaling infrastructure to meet demand.
Monitoring access logs, user activities, and system behavior to detect potential security threats or compliance violations.
Building Blocks Of Smart Monitoring Solution
The following diagram demonstrates vital components of the solution delivered to build the observability framework.
AI (Artificial Intelligence) can significantly enhance platform observability by automating and augmenting various aspects of the monitoring and analysis process. Here’s how AI improves platform observability:
AI models can detect deviations from expected behavior, such as unusual spikes in traffic, unusual resource utilization, or abnormal response times. AI-based anomaly detection helps identify potential issues or threats that may go unnoticed with traditional monitoring approaches.
When an issue arises, AI can assist in identifying the root cause more efficiently. AI algorithms can correlate events, detect patterns, and suggest potential causes of the problem.
By leveraging machine learning techniques, AI can forecast resource needs, anticipate potential bottlenecks, and provide insights into future performance.
Learn standard behavior patterns and adjust alerting thresholds dynamically. This process lowers false positives and negatives, ensuring that teams are promptly informed of any significant events or anomalies requiring attention.
Identifies a specific type of performance issue; it can trigger automated actions like scaling resources, restarting services, or adjusting configurations. By automating remediation steps, AI reduces the time spent on manual interventions and speeds up recovery.
Identify patterns, trends, and correlations that may not be apparent to human observers. This allows teams to gain deeper insights into the platform’s performance, identify optimization opportunities, and make data-driven decisions.
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