Chat
Learn about SRE.ai's Chat interface
Overview
SRE.ai's Chat interface is the primary way to move work through your Salesforce DevOps pipeline.
Chat works in two modes, and you'll use both:
Free-form input: Describe what you need in natural language. SRE.ai routes to the right capability based on intent, no special commands or syntax required.
Guided prompts: At each step, Chat surfaces suggested next actions as clickable options. These tell you what's available from where you are, so you don't need to know what to type to move forward.
Most sessions start free-form: you describe what you're working on and then follow guided prompts through the steps to completion.
Core capabilities
Change management via Chat
From Chat, you can:
Start a change: Describe what you want built or modified. SRE.ai creates a Change artifact and tracks it through the full lifecycle.
Commit changes: Persist your Salesforce metadata to the appropriate branch based on your pipeline configuration.
Create pull requests: Generate a PR in your GitHub repository targeting the correct branch for your pipeline stage.
Deploy to the next environment: Advance changes through your pipeline stages with quality gate enforcement before each deployment.
Design and build with agents: Invoke the Design, Build, or Deploy agent directly through Chat for AI-assisted development.
Deployment intelligence
Before a deployment executes, Chat surfaces the information you need to make a confident decision:
Quality gate status: See whether your change meets the coverage thresholds, static analysis requirements, and PR approvals configured for the target stage.
Deployment target confirmation: Chat identifies the target Salesforce org based on your pipeline stage and asks you to confirm before proceeding.
Test level selection: Choose the test level for the deployment, subject to the governance rules configured in your pipeline (see Selecting a test level).
Environment visibility
Chat has awareness of your connected Salesforce orgs and pipeline stages, enabling you to ask questions about:
The current state of your environments
What changes are pending or deployed in a given org
Deployment history for a change or environment
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.
Selecting a test level
Test level selection is a gated feature. If you don't see the dropdown, contact your account team to enable it for your workspace.
When you initiate a deployment through Chat, a test level dropdown appears on the deployment confirmation screen. It defaults to the test level configured in your pipeline stage.
Test level governance:
You can only select a test level equal to or higher than the level your pipeline stage is configured for. Selecting a lower level is not permitted, pipeline test settings are a governance control and cannot be bypassed through Chat.
Available test levels, from lowest to highest:
Run Specified Tests / Run Relevant Tests
Run Local Tests
Run All Tests
If you select a higher test level than your pipeline default (e.g., Run All Tests), a warning will appear noting that this can take longer and will block the pipeline for the team during that window.
Non-Apex deployments:
If your change contains no Apex components (e.g., only LWC, flows, custom objects, or other non-Apex metadata), SRE.ai automatically applies an appropriate test level and does not require test execution.
The dropdown will reflect this adjustment.
Deployment failure recovery
If a deployment fails, the chat will automatically surface a "Fix deployment errors" suggestion.
Rather than leaving you without a next step, the agent reviews the error output and proposes a resolution.
This suggestion only appears when the most recent deployment has failed.
If you don't see it, check the deployment status in the change details panel to confirm whether a failure occurred.
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.
Last updated