# Datadog EKS Automation

**Description:** Enable Datadog Workflow automations to handle CPU & memory spikes for AWS EKS pods by automatically raising Pull Requests in Azure DevOps, determining resource increases, connecting to Jenkins for releases, and either confirming stability via Slack or escalating to PagerDuty. This automates the repetitive manual response currently handled by DevOps engineers for noisy resource utilization alerts.
**Type:** greenfield
**Target State:** The workflow automation raises Pull Requests in Azure DevOps for impacted services to increase pod specs, determines reasonable value increases based on usage, connects to Jenkins to release new app versions, notifies the developer releases Slack channel, runs Jenkins E2E pipeline tests, monitors the situation for a few minutes, and either sends Slack confirmation or notifies PagerDuty for human involvement.
**Sprint Planning:** 2-week sprints × 2 sprints

## Goals
- Automate response to CPU and memory utilization alerts for EKS pods
- Reduce manual intervention for repetitive resource scaling tasks
- Implement end-to-end workflow from alert to resolution or escalation

## End Users
- DevOps engineers
- On-call responders

## Tech Stack
- Terraform
- AWS Secrets Manager
- Azure DevOps Git
- Jenkins
- Datadog Workflows

## Constraints
- Workflow automation connection to Jenkins authentication and job triggering permissions
- Mapping each Datadog service to its Azure DevOps repository
- Workflow automation connection to Azure DevOps with PAT permissions
- Workflow automation connection to Slack with bot token permissions
- Resource increase calculations must include safety margins
- Must handle cases where E2E test suites don't exist for a service

## Out Of Scope
- Creating Jenkins or Jenkins pipelines
- Creating the Slack channel
- Creating the PagerDuty rota
- Handling non-CPU/memory alerts
- Multi-cluster support

## Assumptions
- ⚠️ No hard deadlines
- ⚠️ Generalist/fullstack team roles
- ⚠️ No architectural constraints specified
- ⚠️ No existing documentation to reference
- ⚠️ Monorepo structure
- ⚠️ No known technical debt identified
- ⚠️ No specific risks identified
- ⚠️ No known blockers or external dependencies
- ⚠️ Markdown output format
- ⚠️ 10% capacity lost to unplanned absences
- ⚠️ No engineers onboarding

## Capacity

- **Team size:** 5 engineer(s)
- **Sprint length:** 2 weeks
- **Target sprints:** 2
- **Gross velocity:** 21 pts/sprint
- **Deductions:** Unplanned absence: 10%, Discovery/design: 5%

**Per-sprint velocity:**

- Sprint 107: **18 pts**
- Sprint 108: **16 pts** — May Day

# Features

## F1: Datadog EKS Automation
**Priority:** high
Enable Datadog Workflow automations to handle CPU & memory spikes for AWS EKS pods by automatically raising Pull Requests in Azure DevOps, determining resource increases, connecting to Jenkins for releases, and either confirming stability via Slack or escalating to PagerDuty. This automates the repetitive manual response currently handled by DevOps engineers for noisy resource utilization alerts.

# User Stories

## US-F1-001: Configure External System Integrations

*As a DevOps engineer, I want to configure authentication and permissions for Azure DevOps, Jenkins, and Slack integrations, so that the automation workflow can securely interact with all required external systems.*

**Feature:** F1 | **Points:** 5 | **Priority:** high | **Discipline:** infrastructure

> **Points rationale:** Involves configuring three different external systems with different authentication mechanisms, handling credential management via AWS Secrets Manager, and validating permissions across multiple APIs.

**Acceptance Criteria:**
- **Given** valid PAT tokens and credentials are available
  **When** configuring Azure DevOps, Jenkins, and Slack connections
  **Then** all three systems authenticate successfully and permissions are verified
- **Given** invalid or expired credentials are provided
  **When** attempting to configure system connections
  **Then** clear error messages are returned and configuration fails gracefully
- **Given** partial permissions are granted on any system
  **When** testing the connection configuration
  **Then** specific missing permissions are identified and reported

**Definition of Done:**
- [x] Acceptance Criteria Met
- [x] Documentation
- [x] Proper Testing
- [x] Code Merged to Main
- [x] Released via SDLC
- [x] Stakeholder Sign-off
- [x] Knowledge Sharing

## US-F1-002: Create Service Repository Mapping System

*As a DevOps engineer, I want to establish and maintain mapping between Datadog services and their corresponding Azure DevOps repositories, so that the automation can correctly identify which repository to target for each service's resource updates.*

**Feature:** F1 | **Points:** 3 | **Priority:** high | **Discipline:** backend

> **Points rationale:** Requires designing a mapping data structure, implementing lookup logic, and handling edge cases for unmapped services. Relatively straightforward but critical for workflow routing.

**Acceptance Criteria:**
- **Given** a Datadog service with a known repository mapping
  **When** the automation receives an alert for that service
  **Then** the correct Azure DevOps repository is identified and accessible
- **Given** a Datadog service without a repository mapping
  **When** an alert is triggered for that service
  **Then** the workflow logs the unmapped service and escalates to PagerDuty
- **Given** repository mappings need to be updated
  **When** a DevOps engineer modifies the mapping configuration
  **Then** changes are validated and applied without requiring workflow restart

**Definition of Done:**
- [x] Acceptance Criteria Met
- [x] Documentation
- [x] Proper Testing
- [x] Code Merged to Main
- [x] Released via SDLC
- [x] Stakeholder Sign-off
- [ ] Knowledge Sharing

## US-F1-003: Implement Resource Calculation and PR Generation

*As a DevOps engineer, I want to automatically calculate appropriate resource increases and generate pull requests with updated configurations, so that resource scaling decisions are consistent and include proper safety margins without manual calculation.*

**Feature:** F1 | **Points:** 5 | **Priority:** high | **Discipline:** backend

> **Points rationale:** Involves complex business logic for resource calculation algorithms, Terraform file parsing and modification, and Azure DevOps API integration for PR creation. Multiple technical components with interdependencies.

**Acceptance Criteria:**
- **Given** a CPU spike alert with current and threshold values
  **When** calculating new resource requirements
  **Then** the new CPU limit includes a safety margin and follows scaling best practices
- **Given** calculated resource changes and target repository
  **When** generating the pull request
  **Then** PR contains updated Terraform configurations with proper formatting and descriptive commit messages
- **Given** extremely high resource utilization that would exceed reasonable limits
  **When** calculating resource increases
  **Then** the calculation caps at maximum safe values and flags for manual review

**Definition of Done:**
- [x] Acceptance Criteria Met
- [x] Documentation
- [x] Proper Testing
- [x] Code Merged to Main
- [x] Released via SDLC
- [x] Stakeholder Sign-off
- [x] Knowledge Sharing

## US-F1-004: Orchestrate Jenkins Deployment and Monitoring

*As a DevOps engineer, I want to trigger Jenkins jobs for merged PRs and monitor deployment success or failure, so that resource changes are automatically deployed and their success is tracked without manual intervention.*

**Feature:** F1 | **Points:** 3 | **Priority:** high | **Discipline:** backend

> **Points rationale:** Straightforward Jenkins API integration for job triggering and status monitoring, but requires handling various job states and timeout scenarios.

**Acceptance Criteria:**
- **Given** a merged pull request with resource changes
  **When** triggering the corresponding Jenkins deployment job
  **Then** the job starts successfully and deployment progress is monitored
- **Given** a Jenkins job that completes successfully
  **When** monitoring the deployment
  **Then** the workflow proceeds to stability verification and Slack notification
- **Given** a Jenkins job that fails or times out
  **When** monitoring the deployment
  **Then** the failure is logged and the incident is escalated to PagerDuty with relevant details

**Definition of Done:**
- [x] Acceptance Criteria Met
- [x] Documentation
- [x] Proper Testing
- [x] Code Merged to Main
- [x] Released via SDLC
- [x] Stakeholder Sign-off
- [ ] Knowledge Sharing

## US-F1-005: Implement Notification and Escalation Logic

*As a on-call responder, I want to receive appropriate notifications for successful resolutions or escalated incidents requiring manual intervention, so that I'm informed of automation outcomes and can focus attention on cases that truly need human intervention.*

**Feature:** F1 | **Points:** 2 | **Priority:** high | **Discipline:** backend

> **Points rationale:** Involves integrating with Slack and PagerDuty APIs for notifications, with straightforward conditional logic for success vs failure scenarios.

**Acceptance Criteria:**
- **Given** a successful deployment with stable resource metrics
  **When** the automation completes the full workflow
  **Then** a summary notification is sent to Slack with resolution details
- **Given** a deployment failure or continued resource issues after scaling
  **When** the automation detects the failure condition
  **Then** an incident is created in PagerDuty with context about attempted resolution
- **Given** a service without E2E tests or other validation mechanisms
  **When** deployment completes but stability cannot be verified
  **Then** a Slack notification requests manual verification with deployment details

**Definition of Done:**
- [x] Acceptance Criteria Met
- [x] Documentation
- [x] Proper Testing
- [x] Code Merged to Main
- [x] Released via SDLC
- [x] Stakeholder Sign-off
- [ ] Knowledge Sharing

# Tasks

### T-US-F1-001-01 [Infrastructure]: Create Terraform configuration for AWS Secrets Manager integration
Implement Terraform modules to provision AWS Secrets Manager secrets for storing PAT tokens and credentials for Azure DevOps, Jenkins, and Slack. Create variables.tf, main.tf, and outputs.tf files with proper IAM policies for secret access. Include secret rotation configuration and KMS encryption.

**Test plan:** Unit test: terraform plan validates without errors, all required variables are defined. Integration test: terraform apply creates secrets with correct IAM policies, secrets are accessible by intended services. Test secret rotation configuration works correctly.

**AI prompt:** You are a DevOps engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Create Terraform configuration files for AWS Secrets Manager to store authentication credentials for Azure DevOps, Jenkins, and Slack integrations. Include proper IAM policies for secret access, KMS encryption, and secret rotation. The secrets will be used by Datadog Workflows to authenticate with external systems for automated resource scaling.

### T-US-F1-001-02 [Code]: Implement credential validation service for external system connections
Create a validation service that tests connectivity and permissions for Azure DevOps (repository access, PR creation), Jenkins (job triggering, status monitoring), and Slack (message posting). Implement retry logic, error handling, and detailed permission checking. Store validation results and provide clear error messages for missing permissions.

**Test plan:** Unit test: validation functions return correct status for valid/invalid credentials, error messages are descriptive. Integration test: service correctly identifies missing permissions, retry logic works for transient failures. Test graceful handling of expired tokens and network timeouts.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Implement a credential validation service that tests authentication and permissions for Azure DevOps, Jenkins, and Slack APIs. Include specific permission checks (repo access, PR creation, job triggering), retry logic for transient failures, and detailed error reporting. The service will be called during workflow configuration to ensure all external systems are properly accessible.

### T-US-F1-001-03 [Infrastructure]: Configure Datadog Workflow authentication with external systems
Set up Datadog Workflow connections to Azure DevOps, Jenkins, and Slack using credentials from AWS Secrets Manager. Configure workflow variables, connection testing, and error handling. Implement secure credential retrieval and caching mechanisms within Datadog Workflows.

**Test plan:** Integration test: Datadog Workflow successfully authenticates with all three external systems, credentials are retrieved from AWS Secrets Manager correctly. Test connection failure scenarios and error handling. Verify credential caching works and doesn't expose sensitive data in logs.

**AI prompt:** You are a DevOps engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Configure Datadog Workflow connections to integrate with Azure DevOps, Jenkins, and Slack using credentials stored in AWS Secrets Manager. Set up secure credential retrieval, connection testing, and error handling within the workflow. The connections will be used for automated PR creation, deployment triggering, and notifications.

### T-US-F1-001-04 [Code]: Implement connection monitoring and health checks
Create monitoring system to periodically verify connectivity and permissions for all external integrations. Implement health check endpoints, alerting for connection failures, and automatic credential refresh when possible. Include dashboard for connection status visibility.

**Test plan:** Unit test: health check functions correctly identify connection issues, alerting triggers at appropriate thresholds. Integration test: monitoring detects actual connection failures, automatic credential refresh works for supported systems. Test dashboard displays accurate connection status.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Create a monitoring system for external system connections (Azure DevOps, Jenkins, Slack) with periodic health checks, alerting for failures, and automatic credential refresh capabilities. Include a status dashboard for visibility. The system should proactively detect authentication issues before they impact the automation workflow.

### T-US-F1-001-05 [Documentation]: Document authentication and permissions configuration
Document the complete authentication setup process including AWS Secrets Manager configuration, required permissions for each external system (Azure DevOps repository access and PR creation, Jenkins job triggering and monitoring, Slack channel posting), credential rotation procedures, troubleshooting common authentication issues, and security best practices. Include setup instructions and permission verification steps.

**AI prompt:** You are a technical writer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Create comprehensive documentation for authentication and permissions configuration covering AWS Secrets Manager setup, required permissions for Azure DevOps/Jenkins/Slack integrations, credential rotation procedures, and troubleshooting guides. Include step-by-step setup instructions and security best practices for DevOps engineers configuring the system.

### T-US-F1-002-01 [Code]: Create service-to-repository mapping configuration system
Implement a configuration management system for mapping Datadog services to Azure DevOps repositories. Create JSON/YAML configuration files with service names, repository URLs, branch strategies, and file paths for Terraform configurations. Include validation for mapping entries and configuration reload capabilities.

**Test plan:** Unit test: configuration parser validates mapping entries, handles malformed JSON/YAML gracefully. Integration test: mapping system correctly identifies repositories for known services, configuration reload works without service restart. Test validation catches invalid repository URLs and missing required fields.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Create a configuration management system that maps Datadog service names to their corresponding Azure DevOps repositories. Include configuration file format (JSON/YAML), validation logic, and hot-reload capabilities. The mapping will be used by automation workflows to determine which repository to update when scaling resources for specific services.

### T-US-F1-002-02 [Code]: Implement repository lookup and validation service
Create a service that resolves Datadog service names to repository information and validates repository accessibility. Include caching for performance, fallback mechanisms for unmapped services, and integration with PagerDuty for escalation. Implement repository metadata retrieval (branch info, file structure validation).

**Test plan:** Unit test: lookup service returns correct repository info for mapped services, handles unmapped services appropriately. Integration test: repository accessibility validation works, caching improves performance, PagerDuty escalation triggers for unmapped services. Test fallback mechanisms and error handling.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Implement a repository lookup service that resolves Datadog service names to Azure DevOps repository information, validates repository access, and escalates unmapped services to PagerDuty. Include caching for performance and repository metadata validation. The service will be called when processing Datadog alerts to determine the target repository for resource updates.

### T-US-F1-002-03 [Documentation]: Document service-to-repository mapping configuration
Document the mapping configuration system including configuration file format and schema, process for adding new service mappings, repository validation requirements, troubleshooting unmapped services, and escalation procedures. Include examples of mapping configurations and best practices for maintaining the mapping database.

**AI prompt:** You are a technical writer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Document the service-to-repository mapping system including configuration file format, process for adding/updating mappings, validation requirements, and troubleshooting procedures. Include practical examples and best practices for DevOps engineers managing service mappings and handling unmapped services.

### T-US-F1-003-01 [Code]: Implement resource calculation engine for CPU and memory scaling
Create algorithms to calculate appropriate resource increases based on current usage, thresholds, and historical patterns. Implement safety margin calculations, scaling best practices (e.g., incremental increases, maximum limits), and support for both CPU and memory resources. Include configuration for scaling policies and maximum safe limits.

**Test plan:** Unit test: calculation engine produces reasonable resource increases with safety margins, respects maximum limits, handles edge cases like extremely high utilization. Integration test: calculations work with real Datadog metrics, scaling policies are applied correctly. Test boundary conditions and error handling for invalid inputs.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Implement a resource calculation engine that determines appropriate CPU and memory increases based on Datadog alert data, current usage patterns, and scaling best practices. Include safety margins, maximum limits, and configurable scaling policies. The engine will process Datadog monitor alerts and calculate new resource requirements for EKS pods.

### T-US-F1-003-02 [Code]: Create Terraform configuration file generator
Implement service to generate updated Terraform configuration files with new resource limits. Parse existing Terraform files, update CPU/memory values, maintain proper formatting and comments. Support different Terraform resource types (deployments, pods, containers) and handle complex nested configurations.

**Test plan:** Unit test: generator correctly parses and updates Terraform files, maintains formatting and comments, handles various resource types. Integration test: generated files are valid Terraform syntax, updates are applied to correct resource blocks. Test error handling for malformed Terraform files and unsupported resource types.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Create a Terraform configuration file generator that parses existing Terraform files and updates CPU/memory resource limits while preserving formatting and structure. Support various Kubernetes resource types and handle complex nested configurations. The generator will create updated Terraform files for pull request creation.

### T-US-F1-003-03 [Code]: Implement Azure DevOps pull request creation service
Create service to generate and submit pull requests to Azure DevOps repositories with updated Terraform configurations. Include PR template generation, descriptive commit messages with scaling rationale, branch management, and reviewer assignment. Implement conflict detection and resolution strategies.

**Test plan:** Unit test: PR creation service generates proper commit messages and PR descriptions, handles branch creation correctly. Integration test: PRs are successfully created in Azure DevOps with correct file changes, reviewers are assigned appropriately. Test conflict detection and error handling for repository access issues.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Implement a pull request creation service for Azure DevOps that creates branches, commits updated Terraform files, and submits PRs with descriptive messages explaining the resource scaling rationale. Include reviewer assignment and conflict handling. The service will be called after resource calculations to propose infrastructure changes.

### T-US-F1-003-04 [Code]: Implement safety checks and manual review flagging
Create validation system to identify resource changes that require manual review (excessive increases, critical services, budget thresholds). Implement flagging mechanisms, approval workflows, and integration with existing change management processes. Include configurable thresholds and escalation rules.

**Test plan:** Unit test: safety checks correctly identify scenarios requiring manual review, thresholds are configurable and respected. Integration test: flagged changes trigger appropriate approval workflows, escalation rules work correctly. Test edge cases like extremely high resource requests and critical service identification.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Implement safety checks that identify resource scaling changes requiring manual review based on configurable thresholds, service criticality, and scaling magnitude. Include flagging mechanisms and integration with approval workflows. The system should prevent automatic application of potentially risky resource changes.

### T-US-F1-003-05 [Documentation]: Document resource calculation and PR generation process
Document the resource calculation algorithms including scaling policies and safety margins, Terraform file generation process, pull request creation workflow, safety check thresholds and manual review criteria, and troubleshooting common issues with resource calculations. Include examples of generated PRs and configuration options for scaling policies.

**AI prompt:** You are a technical writer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Document the resource calculation and pull request generation process including scaling algorithms, safety margins, Terraform file updates, PR creation workflow, and manual review criteria. Include practical examples and configuration options for DevOps engineers managing automated resource scaling.

### T-US-F1-004-01 [Code]: Implement Jenkins job triggering service
Create service to automatically trigger Jenkins deployment jobs when pull requests are merged. Implement webhook handling for Azure DevOps merge events, Jenkins API integration for job triggering, parameter passing for deployment context, and job queue management. Include retry logic for failed job starts.

**Test plan:** Unit test: service correctly identifies merged PRs related to resource scaling, Jenkins API calls succeed with proper parameters. Integration test: Jenkins jobs are triggered automatically on PR merge, job parameters are passed correctly. Test retry logic for Jenkins API failures and webhook reliability.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Implement a Jenkins job triggering service that responds to Azure DevOps PR merge events and automatically starts corresponding deployment jobs. Include webhook handling, Jenkins API integration, parameter passing, and retry logic. The service will initiate deployments for approved resource scaling changes.

### T-US-F1-004-02 [Code]: Create deployment monitoring and status tracking system
Implement system to monitor Jenkins job progress, track deployment status, and detect success/failure conditions. Include real-time status updates, timeout handling, log aggregation, and integration with deployment verification. Support multiple concurrent deployments and status correlation with original alerts.

**Test plan:** Unit test: monitoring system correctly tracks job status changes, timeout detection works properly. Integration test: system monitors actual Jenkins deployments, status updates are accurate and timely. Test handling of multiple concurrent deployments and correlation with original scaling requests.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Create a deployment monitoring system that tracks Jenkins job progress, detects success/failure conditions, and handles timeouts. Include real-time status updates and support for multiple concurrent deployments. The system will monitor resource scaling deployments and determine next steps based on deployment outcomes.

### T-US-F1-004-03 [Documentation]: Document Jenkins integration and deployment monitoring
Document the Jenkins integration setup including webhook configuration for Azure DevOps, Jenkins job triggering process, deployment monitoring capabilities, timeout and retry configurations, troubleshooting deployment failures, and log analysis procedures. Include setup instructions for Jenkins job templates and monitoring dashboards.

**AI prompt:** You are a technical writer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Document the Jenkins integration including webhook setup, job triggering process, deployment monitoring, timeout handling, and troubleshooting procedures. Include setup instructions and configuration examples for DevOps engineers managing automated deployments triggered by resource scaling changes.

### T-US-F1-005-01 [Code]: Implement Slack notification service for workflow outcomes
Create notification service to send Slack messages for successful resolutions and manual verification requests. Implement message templating, channel routing based on service/team, rich formatting with deployment details, and notification deduplication. Include integration with Slack threading for related notifications.

**Test plan:** Unit test: notification service generates properly formatted Slack messages, channel routing works correctly. Integration test: messages are delivered to appropriate Slack channels, rich formatting displays correctly. Test message deduplication and threading functionality.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Implement a Slack notification service that sends formatted messages for successful resource scaling resolutions and requests for manual verification. Include message templating, channel routing, rich formatting with deployment context, and deduplication. The service will keep teams informed of automation outcomes and manual intervention needs.

### T-US-F1-005-02 [Code]: Create PagerDuty incident management integration
Implement PagerDuty integration for escalating deployment failures and continued resource issues. Create incident templates with context about attempted resolutions, implement severity classification, and include relevant deployment logs and metrics. Support incident updates and resolution tracking.

**Test plan:** Unit test: PagerDuty integration creates incidents with proper severity and context, incident templates are populated correctly. Integration test: incidents are created in PagerDuty with complete context, incident updates work properly. Test severity classification and escalation rules.

**AI prompt:** You are a backend engineer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Create PagerDuty integration for escalating deployment failures and persistent resource issues after scaling attempts. Include incident creation with context about attempted resolutions, severity classification, and relevant logs/metrics. The integration will ensure critical issues receive appropriate attention when automation cannot resolve them.

### T-US-F1-005-03 [Documentation]: Document notification and escalation procedures
Document the notification system including Slack message formats and channel routing, PagerDuty incident creation and escalation criteria, manual verification procedures for deployments without E2E tests, troubleshooting notification delivery issues, and configuration options for notification preferences. Include examples of notification messages and escalation scenarios.

**AI prompt:** You are a technical writer working on Datadog EKS Automation (Terraform, AWS Secrets Manager, Azure DevOps Git, Jenkins, Datadog Workflows). Document the notification and escalation system including Slack notifications, PagerDuty incident creation, manual verification procedures, and configuration options. Include message examples and escalation criteria for on-call responders and DevOps teams managing automated resource scaling outcomes.

# Sprint Plan

## Sprint 107
**Goal:** Establish foundational infrastructure and authentication for Datadog EKS automation, including service mapping and integration setup. Focus on de-risking technical unknowns and preparing core backend components.
**Capacity:** 16 pts (velocity: 21)

- US-F1-001
- US-F1-002
- US-F1-003
- US-F1-004

## Sprint 2
**Goal:** Sprint 2 stories
**Capacity:** 2 pts (velocity: 21)

- US-F1-005
