Command Center
Learn about SRE.ai's Command Center
Overview
The Command Center provides an overview of relevant environments and a directory of pending tasks.
Chat is central to the Command Center.
When prompted, the chat box outlines its process and the environments it uses to resolve inquiries, so you can take control over where and how to manage ongoing tasks.
Core capabilities
Intelligent Incident Detection
Proactive identification of issues before customer impact
Cross-system correlation detects patterns that individual monitoring tools miss
Predictive failure analysis using historical incident data and system behavior patterns
Automated incident creation with proper severity classification and initial context gathering
Pre-Deployment Risk Analysis
AI-powered assessment of proposed changes
Static code analysis integrated with security scanning and dependency vulnerability checks
Impact analysis showing potential effects on downstream systems and customer-facing features
Automated regression testing orchestration with intelligent test selection based on change scope
Collaborative Response Coordination
Streamlined incident resolution across teams
War room automation creates dedicated Slack channels with relevant team members and documentation
Context aggregation pulls together recent deployments, system changes, and related incidents
Real-time collaboration tools with shared timeline and status updates for all stakeholders
Safe Deployment Orchestration
Coordinated releases across complex multi-system environments
Blue-green deployment automation with intelligent traffic routing and rollback triggers
Database migration coordination with schema version control and rollback strategies
Feature flag management enabling progressive rollouts with automatic anomaly detection
Automated Root Cause Analysis
AI-powered investigation and learning from incidents
Timeline reconstruction showing the sequence of events leading to the incident
Impact analysis quantifying customer, revenue, and system effects
Improvement recommendation generation with specific action items for prevention
Post-Deployment Validation
Comprehensive monitoring and validation of release success
Automated functional testing with business logic validation across integrated systems
Performance baseline comparison with automatic alerting for degradation
Customer impact monitoring through support ticket analysis and user behavior tracking
Unified Dashboard
Single-pane view of system health across all environments and platforms
Production, staging, development, and sandbox environment status in real-time
Cross-system dependency mapping showing service relationships and data flows
Performance metrics aggregation from application monitoring, infrastructure, and business KPIs
Intelligent Status Synchronization
Automatic updates eliminate manual coordination overhead
GitHub PR status automatically updates corresponding Jira tickets with deployment progress
Slack channel notifications include intelligent summaries of changes and potential impacts
Salesforce sandbox status reflects production deployment schedules and environment health
Intelligent Alerting
Context-aware notifications that reduce noise and improve signal clarity
AI-powered alert correlation eliminates duplicate notifications across monitoring tools
Business impact scoring prioritizes alerts based on customer and revenue implications
Intelligent routing delivers alerts to relevant team members with appropriate context and urgency
Smart Work Prioritization
AI-driven task and incident prioritization across teams
Business impact scoring considers customer contracts, revenue implications, and SLA requirements
Technical dependency analysis ensures prerequisite work is completed before dependent tasks
Team capacity and expertise matching optimizes work distribution and reduces bottlenecks
The change workflow
Starting a change
When you create a change in SRE.ai, the platform analyzes the components you're modifying and tracks them against your connected environments. You'll see test coverage information and an analysis of which metadata types are involved.
Committing changes
Once you're ready to persist your work, commit your changes from within SRE.ai. The platform generates a commit to the appropriate branch based on your pipeline configuration.
Creating pull requests
SRE.ai can create pull requests directly from the change interface. When you create a PR:
The PR is generated in your GitHub repository with your changes
A link to the PR appears in the SRE.ai change details
The PR targets the correct branch based on your pipeline stage
You can view and manage the PR either in GitHub or through SRE.ai's interface.
Quality gates
Before changes can advance to the next stage, they must pass the quality gates configured for that stage.
Common quality gates include:
Pull request approval
A PR must exist and be approved before deployment proceeds. If no PR exists for the target branch, the quality gate fails.
Code coverage
Test coverage must meet a configured threshold.
Code analysis
Static analysis checks must pass.
Quality gate status appears in the change details panel.
If a gate fails, SRE.ai shows what's blocking deployment and the required actions.
Deploying to the next environment
After your changes pass quality gates, you can deploy to the next environment in your pipeline.
Manual deployment
By default, moving changes through the pipeline requires explicit action.
After committing changes and merging your PR, return to SRE.ai and click Deploy to next environment to push the changes to the next stage.
This separation gives you control over timing. Your PR can be merged and ready while you wait for a deployment window or final sign-off.
Automated deployment
If you want deployments to trigger automatically when a PR is merged, you can configure an automation.
Set the trigger to start on PR merge, and the deployment to the next environment kicks off automatically.
This is useful for earlier pipeline stages (development → integration) where you want continuous flow.
For production deployments, most teams prefer the manual approach or add additional approval steps.
TIP: You can configure distinct behaviors for each stage. Automate deployments through your lower environments, but require manual promotion to production.
Syncing branches and orgs
When you first connect a repository, your branch and your Salesforce org may not be in sync.
Metadata might exist in the org but not be reflected in the branch, or vice versa.
Initial sync strategies
Option 1: Push org metadata to a new branch
The simplest approach is to create a fresh branch and push all metadata from your org into it.
This establishes the branch as the source of truth going forward.
Option 2: Pull repository contents into the org
If your branch already contains the canonical version of your metadata, you can configure an automation to pull everything from the repository into your org.
If components exist in the repo but not in the org (or vice versa), SRE.ai flags the discrepancies so you can resolve them.
Option 3: Identify and reconcile differences
For more complex situations, SRE.ai can help you understand the differences between your branch and your org.
During onboarding, the team works with you to identify gaps and either automate the sync or handle it manually.
Ongoing drift
Over time, orgs and branches can drift apart, especially if changes are made directly in production or if sandbox refreshes introduce discrepancies. SRE.ai tracks changes across your environments, making it easier to identify out-of-sync items and reconcile differences before they cause deployment issues.
Viewing changes without source tracking
Even if source tracking isn't enabled on a Salesforce environment, you can still see what's changed.
SRE.ai's View My Changes feature shows all changes made in an org, not just recent ones, but also historical changes.
To access this:
Navigate to the org in question (even without a connected repo)
Open View My Changes
Filter by component type, date range, or other criteria
This is useful for auditing environments, understanding what's deployed where, and identifying changes that need to be captured in source control.
IMPORTANT: Source tracking speeds up and improves the precision of change detection. SRE.ai can still surface changes without source tracking, but enabling source tracking is recommended for the best experience.
Prompts and changes
Click below to view a collection of optimized prompts and learn how to track and manage changes.
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