Overview
Scoutflo’s seamlessly integrates with a wide range of cloud-native tools, observability platforms, and CI/CD systems, enabling comprehensive debugging and management of Kubernetes-based environments. By connecting to these data sources, Scoutflo’s AI Engine leverages the Model Context Protocol (MCP) tools to fetch real-time and historical data, such as logs, metrics, and infrastructure configurations, to drive context-aware root cause analysis and remediation. Integrations span cloud monitoring, application performance monitoring (APM), analytics, CI/CD pipelines, alerting systems, and project management tools, ensuring Scoutflo adapts to your existing stack.
This section provides an overview of currently supported and upcoming integrations, along with step-by-step guides for connecting Scoutflo to these platforms. Each integration enhances the AI Engine’s ability to query relevant data, correlate alerts, and deliver actionable insights, streamlining incident resolution and system management.
Integration Status
The following table outlines Scoutflo’s current and planned integrations, detailing the categories, specific tools, their status, and the assets fetched to support debugging and monitoring workflows.
Kubernetes Platforms
Amazon EKS
Active
Amazon EKS integration allows Scoutflo to fetch cluster names, regions, node pools, and Kubernetes assets (e.g., deployments, services, ingresses). The AI Engine inspects cluster state to diagnose issues like pod scheduling failures or resource constraints, correlating with metrics from Prometheus or logs from CloudWatch for comprehensive analysis. This integration is foundational for Kubernetes debugging. Critical for cluster-level troubleshooting, such as resolving a CrashLoopBackOff pod by analyzing logs (pods_log) and events (events_list) to suggest increasing memory limits.
Google GKE
Coming Soon
Cluster names, regions, node pools and configurations, Kubernetes assets
Application Performance
Sentry
Active
Sentry integration provides access to projects, issues, events, releases, and alert rules. The AI Engine uses these to analyze application errors, such as stack traces from a crashing service, and correlates with deployment events from ArgoCD to pinpoint issues like a buggy release. Features like sentry-begin-autofix enable automated fixes for simple issues, reducing manual effort. Essential for diagnosing application-level errors, such as identifying a null pointer exception in a recent release, with suggestions like rolling back.
Grafana
Active
Grafana integration allows Scoutflo to fetch dashboards with panels and variables, datasources, and alert rules. The AI Engine inspects panel queries to diagnose visualization issues or misconfigured alerts, such as a dashboard missing critical metrics due to an incorrect datasource. This integration enhances monitoring by enabling the AI to correlate Grafana alerts with Kubernetes events or Prometheus metrics for comprehensive debugging. Critical for diagnosing monitoring issues, such as fixing a broken panel causing alert gaps, or adjusting alert rules firing too frequently
Prometheus
Active
Prometheus integration empowers Scoutflo to query metrics, time series data, and alerting/recording rules. The AI Engine analyzes metrics to diagnose issues like high CPU usage or error rate spikes, correlates with alerts, and suggests remediations. This integration is vital for real-time monitoring and historical trend analysis in Kubernetes environments. Enables rapid triage of performance issues (e.g., high memory usage alerts) and root cause analysis of recurring problems by analyzing metric trends over time.
Datadog
Coming Soon
Services, dashboards with widgets and queries, infrastructure hosts, Kubernetes components
New Relic
Coming Soon
Services, dashboards with widgets and queries, infrastructure hosts, Kubernetes components
Loki
Coming Soon
Log index names
Signoz
Coming Soon
Services, dashboards with widgets and queries
Cloud Monitoring
AWS CloudWatch
Active
AWS CloudWatch integration enables Scoutflo to access metric namespaces (e.g., CPU, memory, network), dashboards with widgets and their underlying queries, and log groups for application and infrastructure logs. The AI Engine uses these assets to diagnose performance issues, such as high latency in an EKS cluster, by querying metrics and correlating with logs. For example, MCP tools can fetch CloudWatch logs to identify errors in a failing pod or analyze metric trends to detect resource bottlenecks, providing real-time and historical insights for root cause analysis. Essential for troubleshooting infrastructure issues in AWS environments, such as identifying a spike in EC2 instance CPU usage triggering an alert, with remediation suggestions like scaling resources.
Azure Monitor
Coming Soon
Infrastructure components, dashboards with widgets and queries, log groups/indexes
Google Cloud Monitoring
Coming Soon
Infrastructure components, dashboards with widgets and queries, log groups/indexes
Analytics
PostHog
Coming Soon
Dashboards with widgets and queries
CI/CD Tools
ArgoCD
Active
ArgoCD integration enables Scoutflo to manage and debug applications by fetching configurations, events, and managed resources. The AI Engine diagnoses deployment issues, such as an OutOfSync state, and triggers actions like argocd_sync_application to align applications with their Git-defined state, ensuring consistent deployments. Vital for resolving deployment-related issues, such as a failed rollout due to configuration drift, with suggestions to sync or update applications.
GitHub
Active
GitHub integration allows Scoutflo to access repositories, main branch details, users, and commits, enabling the AI Engine to correlate code changes with issues detected in Sentry or ArgoCD. For example, the AI can trace a buggy commit causing errors in a recent deployment by analyzing commit history alongside sentry-get-release-details, supporting precise root cause analysis. Essential for linking code changes to application issues, such as identifying a commit introducing a bug, with suggestions to revert or deploy a fix via ArgoCD.
Jenkins
Coming Soon
Job names with parameters
Alerting
Slack
Active
Slack integration delivers real-time alert notifications to Scoutflo, enabling the AI Engine to trigger debugging workflows based on incoming alerts from Prometheus, Sentry, or Grafana. The AI correlates Slack notifications with data to prioritize incidents and provide remediation suggestions, streamlining incident response. Facilitates rapid incident awareness, such as triggering an investigation for a high-severity Prometheus alert, ensuring timely debugging actions.
Microsoft Teams
Coming Soon
Alert notifications
Zenduty
Coming Soon
Alert notifications
Project Tools
Jira
Coming Soon
Projects, users
Confluence
Coming Soon
Pages
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