Skip to content

🏗️ Pipeline Blueprint

📘 Pipeline Blueprint

📘 What Is a Pipeline Blueprint?

A Pipeline Blueprint in the ConnectSoft AI Software Factory defines the CI/CD process, automation hooks, test stages, and promotion flows for a generated microservice, library, module, or full product.

It is the source of truth for automation, representing a declarative and traceable structure that agents use to generate secure, observable, and environment-aware delivery pipelines.

It includes structure for both build-time and runtime automation, serving humans and agents alike.

🎯 Why It Exists

Without standard blueprints, pipelines often suffer from:

  • Manual duplication and inconsistency across services
  • Missing rollback paths or environment-specific logic
  • Insecure secrets handling and testing gaps
  • Lack of traceability and observability integration

The Pipeline Blueprint addresses this by defining pipelines as first-class, agent-generated artifacts, built with the same rigor as software.

🔁 What It Enables

Capability Description
🧠 Agent-Orchestrated Delivery Generated and validated by DevOps and Engineering Agents
🔐 Security-by-Design Includes gates, secrets, permission scopes
📈 Observability Hooks Trace and span emissions during CI and deployment
🧪 Fully Automated Test Plans Integration of test blueprints and automation layers
🔁 Regeneration & Diffing Pipelines can be rebuilt, versioned, and validated on change
🧭 GitOps-Compatible Aligns with GitOps flows and declarative state

📁 Location & Naming Convention

pipelines/{service-name}/pipeline-blueprint.md
pipelines/{service-name}/pipeline-blueprint.yaml
pipelines/{service-name}/azure-pipelines.yaml
pipelines/{service-name}/deployment-docs.md

🔗 Used By Agents

Agent Role
DevOps Architect Agent Defines blueprint strategy, constraints, and structure
DevOps Engineer Agent Implements concrete pipeline fragments
Deployment Orchestrator Sequences deployment events and rollback policies
Test Automation Agent Adds testing fragments into the pipeline
Security Agent Enforces policy, secrets scan, and permission scopes

📎 Summary

Property Value
📄 Format Markdown + YAML + JSON
🧠 Generated by DevOps, Deployment, Security, Test Agents
🎯 Purpose Automate CI/CD with observability, security, and QA
🔁 Lifecycle Regenerable, diffable, GitOps compatible
📈 Tags traceId, serviceId, blueprintType=pipeline

🤖 Pipeline Agent Chain

👷 Agent Collaboration Overview

The Pipeline Blueprint is generated through coordinated effort across multiple ConnectSoft Factory agents. Each agent contributes a specific dimension to the CI/CD process.

🔗 Key Participating Agents

Agent Name Responsibilities
🧠 DevOps Architect Agent Blueprint design, environment models, delivery flow patterns
🔧 DevOps Engineer Agent Concrete template expansion, Azure Pipelines YAML generation, secret linking
🚀 Deployment Orchestrator Agent Coordinates release steps, rollback plans, artifact promotion logic
🧪 Test Automation Agent Injects automated testing stages from Test Blueprints
🔐 Security Agent Injects secret scan steps, RBAC gates, policy approval blocks
🧩 Observability Agent Adds OTEL span generation, telemetry collectors, and build metrics export

🧠 Orchestration Flow

flowchart TD
  A[📘 Product Blueprint] --> B[🧠 DevOps Architect Agent]
  B --> C[🔧 DevOps Engineer Agent]
  C --> D[🚀 Deployment Orchestrator Agent]
  D --> E[📘 Pipeline Blueprint]
  E --> F[🧪 Test Automation Agent]
  E --> G[🔐 Security Agent]
  E --> H[🧩 Observability Agent]
Hold "Alt" / "Option" to enable pan & zoom

Each agent either contributes a section of the YAML pipeline or modifies tags and metadata within the semantic model.

🧠 Memory Graph and Versioning

  • Pipeline blueprints are stored in the long-term memory graph, tagged with:

  • blueprintType=pipeline

  • generatedBy=DevOpsCluster
  • serviceId, traceId, environment
  • All fragments are versioned and trace-linked to upstream inputs (e.g., product, microservice, test plans).

📎 Agent Chain Highlights

Dimension Agent Outputs
Plan DevOps Architect Agent Blueprint header, strategy, branching patterns
Build/Test DevOps Engineer + Test Agent YAML, artifact tracking, testing stages
Security Security Agent Secret validation, policy enforcement
Release/Deploy Deployment Orchestrator Agent Environment-specific steps, approval gates
Observability Observability Agent Tracing, metrics, OTEL spans

🧱 CI/CD Foundation Patterns

🏗️ Modular Pipeline Structure

Every Pipeline Blueprint in the Factory follows a modular structure built from reusable, agent-generated blocks. These blocks are designed to be:

  • Composable (can be reused across services)
  • Environment-aware (dev, QA, staging, production)
  • Platform-specific (Azure Pipelines, GitHub Actions, GitLab CI, etc.)

📐 Core Foundation Layers

Layer Purpose
Build Layer Compile code, restore packages, validate config
Test Layer Execute unit, integration, and contract tests
Security Layer Validate secrets, check packages, scan for vulnerabilities
Artifact Layer Publish build outputs and containers to internal registries
Release Layer Deploy to specified environment or target cluster
Observability Layer Emit metrics, traces, and logs for every key operation

🧩 Example Stage Blocks

stages:
  - stage: Build
    jobs:
      - job: RestoreAndCompile
        steps:
          - task: DotNetCoreCLI@2
            inputs:
              command: 'restore'

  - stage: Test
    dependsOn: Build
    jobs:
      - job: RunTests
        steps:
          - script: dotnet test --logger:trx

  - stage: Publish
    dependsOn: Test
    jobs:
      - job: PushArtifacts
        steps:
          - task: PublishBuildArtifacts@1

These YAML fragments are expanded using pipeline blueprint structure generated by DevOps Engineer Agent.

🔁 Regenerable Foundations

  • Every foundational layer is mapped to reusable fragments stored in the blueprint library.
  • Pipelines are rebuildable on change detection from other blueprints (e.g., test, infra, product).
  • Custom override markers allow safe partial regeneration (e.g., user-defined environments, extra jobs).

🧠 Clean CI/CD Architecture

Each blueprint enforces a layered structure that mirrors clean code principles:

Build -> Test -> Secure -> Artifact -> Deploy -> Observe

This sequence is consistent and version-controlled across the Factory.

🧩 Blueprint-Driven Job Definitions

🎛️ Jobs as Semantic Units

In the ConnectSoft AI Software Factory, jobs in a pipeline are not just YAML steps — they are semantic units generated from upstream blueprints and encapsulate a discrete goal, such as:

  • Building a specific project
  • Running a type of test (unit/integration/contract)
  • Deploying to an environment
  • Validating an infrastructure change
  • Executing a security scan or observability probe

Each job is generated from job blueprints, authored by DevOps Engineer Agent, Test Agent, or Security Agent, based on the context of the service or module.

🔖 Blueprint Origins of Jobs

Job Type Source Blueprint Agent Contributor
Restore + Build Microservice Blueprint DevOps Engineer Agent
Unit Test Test Blueprint Test Generator Agent
Integration Test Module Blueprint + Test Blueprint Test Automation Engineer
Security Scan Security Blueprint Security Engineer Agent
Deploy Staging Infrastructure Blueprint Deployment Orchestrator Agent
Post-Deploy Check Observability Blueprint Observability Agent

🧠 Job Metadata & Traceability

Every job is enriched with:

  • traceId: Matches upstream blueprint and microservice/module
  • agentId: Indicates which agent created this job block
  • jobIntent: e.g., "build", "verify-contracts", "apply-k8s-bicep"
  • regenerationHash: Used to detect changes and determine re-execution needs

🧱 Example Job Block (Generated)

jobs:
  - job: CompileService
    displayName: "Compile AuthService"
    steps:
      - task: UseDotNet@2
        inputs:
          version: '8.x'
      - script: dotnet build AuthService.sln --no-restore
        displayName: 'Build AuthService'

This job may be tied to:

  • A microservice named AuthService
  • Trace ID: ms-auth-01
  • Regeneration hash tied to microservice-blueprint.json version

🔁 Reusability

Job templates are versioned and reusable:

  • Across multiple pipelines
  • Across environments
  • With environment-specific overlays (e.g., dev, qa, prod)

🛂 Agent-Defined Stages and Gates

🎯 What Are Stages and Gates?

  • Stages group jobs into execution phases like Build, Test, Deploy, etc.
  • Gates are conditions, approvals, or validations that must be satisfied before moving from one stage to another.

The DevOps Architect Agent and Deployment Orchestrator Agent collaboratively define these constructs to ensure traceable, secure, and scalable delivery flows.

🧱 Stage Structure

Every pipeline blueprint is structured as:

stages:
  - Build
  - Test
  - Secure
  - Publish
  - Deploy
  - Observe

Each stage is a first-class citizen in the Factory, with:

  • Trace-linked metadata
  • Embedded blueprint sources
  • Regeneration-safe boundaries

🚧 Types of Gates

Gate Type Description Agent Owner
Approval Gate Manual or group-based approval before production DevOps Architect Agent
Environment Gate Environment readiness checks (e.g., storage, DNS) Infrastructure Engineer
Security Gate Validates security scan results Security Engineer Agent
Test Coverage Gate Blocks deploy if test coverage drops below threshold Test Coverage Validator
Observability Gate Validates OTEL probes, alerts, and health status Observability Agent

🔄 Dynamic Gate Injection

  • Gates are injected conditionally based on the target environment.
  • For example, security and approval gates may only apply to production or staging.
- stage: DeployToProd
  dependsOn: Publish
  condition: and(succeeded(), eq(variables['Build.SourceBranch'], 'refs/heads/main'))
  approvals:
    - group: "SRE-Owners"
  gates:
    - step: ValidateSecurityScan
    - step: CheckTestCoverage

🧠 Semantic Blueprint Traceability

Each stage and gate includes metadata:

  • stageIntent: e.g., "integration-testing", "pre-prod-check"
  • sourceBlueprint: e.g., microservice-blueprint.json
  • gateConditions: JSON logic tree

This ensures replayability, transparency, and agent-aware diffs across versions.

🧮 Pipeline Inputs and Runtime Matrix

🔧 What Are Inputs?

Inputs are the set of dynamic parameters, configurations, and runtime signals that shape a pipeline’s execution.

These inputs are defined by the DevOps Engineer Agent and linked to:

  • Blueprint context (e.g., environment, branch, runtime)
  • Trigger type (manual, commit, scheduled)
  • Semantic kernel inputs (agent metadata, trace scope)

📥 Input Categories

Input Type Examples Agent Owner
Build Inputs Project name, version, tags Microservice Generator
Test Inputs Coverage threshold, test type QA/Test Agents
Deploy Inputs Env, region, slot, infraId DevOps Architect
Security Inputs Scanner profile, ruleset Security Engineer
Runtime Settings OS image, CPU limit, parallelism DevOps Engineer

These are generated per-service and stored with traceable links.

🧠 Dynamic Input Injection

Inputs can be:

  • Fetched from upstream blueprints (infrastructure, security, test)
  • Overridden per environment
  • Augmented by trigger source (commit tags, Git ref, manual run)
parameters:
  - name: environment
    type: string
    default: 'dev'
  - name: project
    type: string
    default: 'InventoryService'

🧬 Runtime Matrix Strategy

The Runtime Matrix defines pipeline expansion based on input permutations:

strategy:
  matrix:
    linux:
      os: 'ubuntu-latest'
      runtime: 'dotnet'
    windows:
      os: 'windows-latest'
      runtime: 'dotnet'

This enables:

  • Cross-platform validation
  • Multi-environment deployment sweeps
  • Toolchain compatibility tests

🔗 Traceability of Inputs

Each input has:

  • inputSource: blueprint file or agent trace ID
  • defaultValue, validationRules
  • overrides section for environment layers

Inputs are documented in both .md and .json blueprint outputs.

🚦 Trigger Definitions and Event Hooks

🚀 What Are Triggers?

Triggers define when and how a pipeline starts. In the ConnectSoft AI Software Factory, triggers are:

  • Declarative
  • Event-aware
  • Blueprint-traceable

They are emitted by the DevOps Architect Agent and Deployment Orchestrator Agent.

📌 Trigger Types

Trigger Type Description Example Source
Commit Trigger Starts pipeline on code changes Git push, branch merge
Pull Request Validates changes before merging Azure DevOps PR hook
Scheduled Trigger Recurring runs for tests, health, or audit Cron, Azure Scheduler
Manual Trigger Explicit pipeline execution from UI Developer initiation
Event-Driven Starts from an external system or agent signal e.g., Event Grid, Bus

🧠 Agent-Originated Events

Some pipelines are triggered by agents:

  • Test Coverage Validator Agent → triggers test flow when coverage drops
  • Bug Resolver Agent → triggers redeploy after hotfix
  • Observability Agent → triggers probe verification if config changes

Triggers can be multi-source, with guardrails and filters applied.

🪝 Event Hook Configurations

Event hooks define:

  • filter: what events match the pipeline
  • conditions: branch, payload keys, author tags
  • payload: injected into pipeline runtime context
trigger:
  branches:
    include:
      - main
      - release/*
pr:
  branches:
    include:
      - main
schedules:
  - cron: "0 4 * * 1"
    displayName: Weekly Test Sweep
    branches:
      include: [ main ]

🔁 Regeneration-Safe and Declarative

Triggers are:

  • Stored in blueprint .json for version control
  • Regenerable from agent execution history
  • Overridable per environment or pipeline role

🧩 Output Mapping

Blueprint Field Description
triggerType e.g., commit, manual, event
sourceAgentId Which agent originated the signal
conditions Optional filters for event handling
retryPolicy If/when to retry upon event failure

🧱 CI/CD Templates and Strategy Inheritance

🧩 Why Templates?

In the Factory, pipelines are modular and reusable. We avoid copy-pasting long YAML files by generating layered templates for:

  • Build
  • Test
  • Deploy
  • Validate
  • Package
  • Promote

Templates are referenced, not duplicated — ensuring maintainability, consistency, and regeneration.

🧰 Types of Templates

Template Scope Description Source Agent
global Used across all projects (e.g., build.yaml) DevOps Architect
project-level Shared within a bounded context Deployment Orchestrator
service-level Custom to a microservice or module Microservice Generator
role-specific Aligned to security, observability, QA pipelines Domain Agents

Each template includes:

  • Inputs and parameters
  • Pre/post steps
  • Output contracts
  • Retry and error-handling logic

📜 Example Template Reference

extends:
  template: azure-pipelines/build/build-core.yml@templates
  parameters:
    project: $(projectName)
    version: $(version)

Template registry is maintained in Git and versioned by the DevOps Engineer Agent.

🧬 Strategy Inheritance & Overrides

Pipelines inherit strategies from their parent context:

  • A module inherits from its microservice
  • A microservice inherits from platform defaults

  • Overrides allowed via:

  • YAML parameters

  • Blueprint metadata
  • Agent signal injection

🔁 Agent-Guided Regeneration

Agents track which template was used and when it was last refreshed. Regeneration is aware of:

  • Upstream template changes
  • Local overrides
  • Breaking changes (noted via semver tags)

🔗 Blueprint Output Mapping

Field Description
extends.template Path to base template
parameters Pipeline-specific values
templateVersion Hash or semver tag
lastCheckedAt When template was last validated by agent

🏗️ Build Stage Definition & Enrichment

🧱 Core Build Responsibilities

The Build Stage is the first critical gate of a pipeline. In the ConnectSoft AI Software Factory, it's not just a dotnet build — it's a blueprint-aware, traceable process that:

  • Compiles source code
  • Validates dependencies
  • Embeds trace metadata
  • Outputs reproducible artifacts

📦 Build Artifacts

Generated artifacts are enriched with:

  • traceId, agentId, blueprintId
  • Semantic version tag (1.2.3-blueprint.v7)
  • Dependency graph snapshot
  • Observability hooks (e.g., OpenTelemetry injection)
- task: DotNetCoreCLI@2
  inputs:
    command: 'build'
    projects: '**/*.csproj'
    arguments: '--configuration Release'

🔧 Build Enrichment Techniques

Enrichment Purpose
Trace Injection Correlates artifact to its generating agent
Blueprint Tags Adds metadata for future regeneration
Git Info Injects commit SHA and branch name
Semantic Hash Unique fingerprint for binary

🧠 Agent Collaboration

Agent Role
DevOps Architect Agent Defines build structure
Microservice Generator Indicates project layout and tools
Observability Agent Injects tracing and logging SDKs

🔍 Output Mapping

Field Description
buildTool e.g., dotnet, npm, docker, msbuild
projectPath Source-relative path to solution or project
traceMetadata Tags injected at build time
artifactOutput Folder, container, or storage path
enrichmentSteps List of post-compilation decorators used

✅ Test Stage Configuration & Coverage Metrics

🧪 Purpose of the Test Stage

The Test Stage verifies software quality before packaging and deployment. In the Factory, this stage is blueprint-driven, agent-enriched, and observability-connected.

It covers:

  • Unit tests
  • Integration tests
  • Contract tests (API, events)
  • Performance and regression snapshots
  • Code coverage validation

📂 Multi-Layered Test Support

Test Type Triggered By Managed By Agent
Unit Tests Build outputs Test Generator Agent
Integration Tests Service composition rules QA Engineer Agent
API Contract OpenAPI / event schemas Test Coverage Validator
E2E / Workflow User journey blueprints Test Automation Engineer

📈 Coverage Metrics & Gates

Coverage enforcement is embedded in pipeline logic:

  • ❗ Thresholds:

  • Unit: ≥ 85%

  • Integration: ≥ 70%
  • Mutation (optional): ≥ 60%
  • ✅ Validated against blueprint's critical paths
  • 📊 Exposed as pipeline metrics and test reports
- task: DotNetCoreCLI@2
  inputs:
    command: 'test'
    arguments: '--collect:"XPlat Code Coverage"'

📜 Reporting and Artifacts

Each test stage produces:

  • JUnit-compatible results
  • Coverage reports (.cobertura.xml)
  • Screenshot diffs (for UI tests)
  • Trace-linked reports for agent memory ingestion

🔁 Traceability & Enrichment

Embedded Tag Description
testId Unique ID per test suite
blueprintSection E.g., Microservice.Events.OrderShipped
coverageScope Targeted domain or submodule
generatedBy Agent name and trace

📦 Package & Publish Strategy

🎯 Purpose

Once the code is built and tested, the Factory's pipeline needs to:

  • Package artifacts deterministically
  • Embed traceable metadata
  • Publish to the correct internal or external registry
  • Allow versioned rollbacks and replays by other agents

This stage converts blueprinted outputs into reusable units of deployment or consumption.

📦 Supported Package Types

Artifact Type Example File / Target Used By
.nupkg NuGet libraries Internal SDK consumers
.docker Container images Kubernetes deployments
.zip Azure Functions, Static Web Apps Infrastructure agent
.tgz Helm charts Service mesh or gateway delivery

🧩 Semantic Versioning Logic

All packages follow strict semver, but blueprint tags are embedded:

1.4.7+blueprint.v3.microservice-generator.20240609

Version metadata includes:

  • blueprintId
  • generatorAgent
  • cycle (or commit timestamp)
  • Git SHA

🚀 Registry Targets

Type Registry Purpose
Container Registry ghcr.io, azurecr.io Runtime deployables
NuGet Feed Azure Artifacts, nuget.connectsoft Cross-service library reuse
Storage Container Azure Blob, S3 Non-runtime data (e.g., docs)
Artifact Repo Pipeline-native Used for rollback and diffing

🔒 Security and Authenticity

Publishing is gated with:

  • OpenPGP or SHA signing
  • Trace-verified pipeline stage ID
  • Agent signature in metadata block
  • Automatic rollback fallback staging

🛠️ Example (Azure Pipelines YAML)

- task: DotNetCoreCLI@2
  inputs:
    command: 'pack'
    packagesToPack: '**/*.csproj'
    versioningScheme: 'off'

- task: PublishBuildArtifacts@1
  inputs:
    pathToPublish: '$(Build.ArtifactStagingDirectory)'
    artifactName: 'drop'
    publishLocation: 'Container'

🚀 Deployment Stage & Rollout Strategies

🎯 Purpose

The Deployment Stage takes blueprint-defined infrastructure, packaged artifacts, and runtime signals to safely roll out software to the target environments. It blends IaC orchestration, canary logic, and environment-specific overrides.

🔀 Supported Rollout Strategies

Strategy Description Recommended Use Case
Standard One-shot deployment Internal tools, stable services
Blue/Green Parallel environments with cutover Core services with SLA impact
Canary Gradual rollout with traffic shifting Risky updates, public endpoints
Ringed Tiered by tenant/project/environment Multi-tenant SaaS environments
Shadow Deploy without routing traffic (test-only) Preview/testing pipelines

🛠️ Pipeline Tasks Per Strategy

Strategy Pipeline Additions
Blue/Green Traffic manager config + health probes
Canary Istio/Gateway API routing adjustments
Ringed Tenant-aware overlays + tag-based scheduling
Shadow Deploy to non-routed namespace

⚙️ Example: Helm-Based Canary Rollout

- task: HelmDeploy@0
  inputs:
    connectionType: 'Kubernetes Service Connection'
    chartType: 'FilePath'
    chartPath: 'charts/mysvc'
    releaseName: 'mysvc-canary'
    namespace: 'canary'
    overrideValues: 'replicaCount=2,image.tag=$(Build.BuildId)'

📍 Environment-Aware Configuration

Deployment stage honors:

  • Runtime profiles from infrastructure-blueprint.json
  • Secure secrets from Key Vault or pipeline variables
  • TraceId-aware telemetry injection (traceId used in pod annotations)

🧠 Rollout Control via Agents

Agent Role
Deployment Orchestrator Selects strategy based on blueprint & policies
Infrastructure Engineer Applies overlays and traffic control parameters
Security Engineer Validates deployment permissions and secrets

🔍 Post-Deployment Smoke & Telemetry Checks

🎯 Purpose

After deployment completes, the Factory initiates a smoke test and observability probe cycle to:

  • Ensure runtime health
  • Validate configuration correctness
  • Confirm telemetry readiness
  • Trigger alerting hooks if needed

This section serves as the final confidence gate before tagging the release as active or validated.

✅ Smoke Test Scenarios

Check Type Method
API Reachability Curl or Postman probes to /health, /api/status endpoints
Service Mesh Routing Ingress gateway routing test with retry
DNS & Certificate nslookup, TLS handshake validation
Config Verification Runtime env var checks, mounted secrets inspection

📈 Telemetry Validation Points

Signal Type Tooling / Expected Output
Logs FluentBit, Serilog – logs show startup completed
Metrics Prometheus – service exposes up, requests_total
Traces OpenTelemetry spans with matching traceId from pipeline
Alerts No pending alerts in Grafana, Azure Monitor, or Sentry

🧠 Agent Participants

Agent Action
Observability Agent Validates traces and telemetry destinations
Test Automation Agent Executes synthetic probes or smoke tests
Security Engineer Agent Confirms no secrets leaked via logs/env
Deployment Orchestrator Blocks release promotion on failed checks

🛠️ YAML Snippet – Health Check Task (Azure Pipelines)

- task: Bash@3
  inputs:
    targetType: 'inline'
    script: |
      curl -sf http://myservice/health || exit 1
      echo "Health check passed!"

🧪 Failure Handling

Failures in this phase:

  • Trigger rollback strategy if defined
  • Generate a post-deploy-failure-report.md
  • Create a trace-linked incident artifact
  • Notify agent channels for triage and remediation

🔍 Post-Deployment Smoke & Telemetry Checks

🎯 Purpose

After deployment completes, the Factory initiates a smoke test and observability probe cycle to:

  • Ensure runtime health
  • Validate configuration correctness
  • Confirm telemetry readiness
  • Trigger alerting hooks if needed

This section serves as the final confidence gate before tagging the release as active or validated.

✅ Smoke Test Scenarios

Check Type Method
API Reachability Curl or Postman probes to /health, /api/status endpoints
Service Mesh Routing Ingress gateway routing test with retry
DNS & Certificate nslookup, TLS handshake validation
Config Verification Runtime env var checks, mounted secrets inspection

📈 Telemetry Validation Points

Signal Type Tooling / Expected Output
Logs FluentBit, Serilog – logs show startup completed
Metrics Prometheus – service exposes up, requests_total
Traces OpenTelemetry spans with matching traceId from pipeline
Alerts No pending alerts in Grafana, Azure Monitor, or Sentry

🧠 Agent Participants

Agent Action
Observability Agent Validates traces and telemetry destinations
Test Automation Agent Executes synthetic probes or smoke tests
Security Engineer Agent Confirms no secrets leaked via logs/env
Deployment Orchestrator Blocks release promotion on failed checks

🛠️ YAML Snippet – Health Check Task (Azure Pipelines)

- task: Bash@3
  inputs:
    targetType: 'inline'
    script: |
      curl -sf http://myservice/health || exit 1
      echo "Health check passed!"

🧪 Failure Handling

Failures in this phase:

  • Trigger rollback strategy if defined
  • Generate a post-deploy-failure-report.md
  • Create a trace-linked incident artifact
  • Notify agent channels for triage and remediation

♻️ Rollback Hooks & Recovery Workflow

🎯 Purpose

In a failure scenario, pipelines must support graceful rollback and automated recovery actions — preserving uptime, user trust, and trace continuity. This section defines how the Factory integrates these flows into the deployment lifecycle.

🧪 Rollback Triggers

Trigger Condition Example
Smoke test failure /health check fails
Observability signal regression Drop in SLO, increase in error rate
Blueprint diff conflict Detected incompatible changes
Security regression New CVEs, missing secrets rotation
Manual rollback request Triggered by QA or DevOps agent

🔄 Rollback Execution Paths

Type Strategy
Application-level Use blue/green or canary toggling (activeService: blue)
Infrastructure-level Re-deploy previous IaC state via Git commit or snapshot
Config-only Reset Kubernetes secrets or config maps
Data Safety Restore backup if schema migration or loss is detected

⚙️ YAML Snippet – Helm Rollback

- script: helm rollback myservice 1 --wait
  displayName: "Rollback to previous Helm release"

🧠 Agent Participation

Agent Role
Deployment Orchestrator Selects rollback method and sequence
Infrastructure Engineer Executes resource-level reversion
Pipeline Agent Logs all rollback attempts with traceId
Observability Agent Confirms system health after rollback

📎 Trace Handling

Every rollback is tagged as a sibling trace node to the original failed deployment. Memory diff includes:

  • Reason for rollback
  • Recovery steps taken
  • Signals and triggers

🛂 Multi-Environment Promotion Gates

🎯 Purpose

This section defines the automated promotion logic from one environment to the next — ensuring only validated, traceable, and compliant artifacts flow into staging, QA, or production.

Promotion gates act as intelligent checkpoints, backed by blueprint metadata, trace signals, and agent-reviewed criteria.

🧭 Common Promotion Flow

[dev] → [qa] → [staging] → [prod]

Each transition is conditional, not just time- or commit-based.

✅ Gate Criteria (per environment)

Condition Type Examples
Blueprint Validation Blueprint diff OK, not stale or incompatible
Test Coverage Gate ≥ 90% unit + integration + security coverage
Observability Check Logs, metrics, and traces match SLA within bounds
Security Scan Pass No CVEs, dependency vulnerabilities, or OWASP violations
Manual Approval Optional agent sign-off, e.g. QA Engineer, Security Architect
Promotion Delay Time-based delay (cooldown window) before next promotion

🔐 YAML – Example Gate for staging

- task: ManualValidation@0
  inputs:
    instructions: 'Approve promotion to staging if SLA, coverage, and security pass.'
    onTimeout: reject
    timeout: '30'

🤖 Agent Roles

Agent Role
QA Engineer Agent Verifies test quality gates are met
Security Engineer Agent Confirms no new violations post-scan
Deployment Orchestrator Checks blueprint trace and memory for promotion path
Pipeline Agent Executes gated transition and logs promotion trace

📡 Memory-Linked Result

Each promotion step is recorded as a traceable edge between blueprint executions:

  • fromEnvironment: dev
  • toEnvironment: staging
  • promotionGate: passed
  • gateFailures: []

🔔 Notification Matrix & Event Hooks

🎯 Purpose

Effective DevOps execution requires real-time visibility into pipeline events. This section defines the notification strategy for all relevant pipeline stages, including failures, promotions, approvals, and success milestones — emitted as structured events.

📬 Notification Types

Type Example Use Case
Success Notify product team on production deploy
Failure Alert DevOps or QA when build/test fails
Manual Approval Notify reviewers for sign-off
Rollback Trigger Notify stakeholders on auto-rollback execution
Security Violation Alert SecOps when scans fail

🧩 Notification Channels

Channel Description
Microsoft Teams DevOps alerts via webhook cards
Email Formal escalation and summaries
Slack Developer-focused thread updates
Azure DevOps Built-in approvals, gate prompts
ServiceNow Optional integration for enterprise IT ops

🧠 Agent Participation

Agent Role
Deployment Orchestrator Formats contextual message + metadata
Pipeline Agent Emits structured events per stage
QA Engineer Agent Notified on approval requirement or test failure
Security Engineer Agent Subscribed to security-specific pipeline signals

🔗 Webhook Format (JSON Payload)

{
  "event": "pipeline-stage-complete",
  "service": "referral-api",
  "stage": "qa",
  "status": "failed",
  "traceId": "a3f2-75be",
  "detailsUrl": "https://dev.azure.com/project/pipelines/..."
}

📋 Notification Matrix Template

Pipeline Stage Success Failure Manual Approval Rollback
Build Dev Team Dev Team ❌ ❌
Test Dev Team QA Team ❌ ❌
QA QA Lead QA Lead QA Lead Dev Team
Staging Product DevOps Product DevOps
Production All All SecOps All

🧱 Pipeline-as-Code Template Types

🎯 Purpose

This section defines the canonical pipeline templates used in the ConnectSoft AI Software Factory. These templates ensure consistency, security, observability, and traceability across microservices, libraries, gateways, and infrastructure modules.

Templates are modular, extensible, and conditionally injected by agents.

📦 Blueprint-Based Template Selection

Blueprint Type Template Used
Microservice Blueprint pipeline-template-microservice.yml
API Gateway Blueprint pipeline-template-gateway.yml
Library Blueprint pipeline-template-library.yml
Infrastructure Blueprint pipeline-template-infrastructure.yml
Frontend Blueprint pipeline-template-frontend.yml
Test Blueprint pipeline-template-test.yml

🧩 Modular Structure

Each pipeline template is composed of:

  • Stage Definitions (build, test, deploy, release)
  • Conditional Steps (e.g., publish only if IsRelease)
  • Reusable Jobs (defined as YAML fragments)
  • Environment Blocks (per blueprint environment definition)
  • Trace Injection (trace ID, project ID, blueprint hash)

⚙️ Format and Storage

Format Description
.yml Azure DevOps YAML pipelines
.json Parsed/compiled variant for agent processing
.md Documentation-ready version with usage guide
embedding Semantically indexed in memory graph

📁 Template Registry Location

/templates/pipelines/{template-name}.yml
/templates/pipelines/registry.json

Each template includes metadata for compatibility, versioning, and supported blueprint types.

🤖 Used by Agents

Agent Role
DevOps Engineer Agent Selects and applies correct template per blueprint
Deployment Orchestrator Validates execution stage per target environment
Pipeline Agent Assembles and finalizes YAML before runtime evaluation

🔐 Secure Secrets & Token Injection

🎯 Purpose

This section defines how secrets, tokens, keys, and credentials are injected securely into pipelines — with a focus on least privilege, ephemeral usage, and cross-environment compliance.

Pipelines never store or hardcode secrets — they reference secured sources injected just-in-time.

🔑 Supported Secret Sources

Source Use Case
Azure Key Vault Preferred for most cloud resources
GitHub/Azure DevOps Secrets Tokens for source control or actions
Environment Variables Ephemeral runtime values (scoped per stage)
Service Connections Azure, Docker, Kubernetes credentials
Vault (optional) For enterprise secrets federation

🔐 Secrets Injection Modes

Mode Description
Environment Variable Temporarily available to step or job
Pipeline Variable Scoped at pipeline level, with isSecret: true
Token Mount Mounted into containerized steps (e.g., /secrets)
Agent Context Injected directly by agent (e.g., Git token)

🧠 Agent Responsibilities

Agent Role
Pipeline Agent Determines which secrets are needed per stage
DevOps Engineer Agent Declares secret requirements in blueprint and links to source
Security Engineer Agent Validates scope, leakage prevention, and access policies

🔄 Secret Lifecycle Hooks

  • ✅ Pre-step injection: secrets are made available just before the step begins
  • 🧹 Post-step cleanup: variables and tokens are wiped after execution
  • 🔁 Rotation ready: tokens fetched dynamically from source (e.g., Key Vault reference)

📁 Example Snippet (Azure DevOps YAML)

variables:
  - group: ConnectSoft-Secrets

jobs:
  - job: deploy
    steps:
      - script: az login --service-principal -u $(client_id) -p $(client_secret)
        env:
          client_id: $(CLIENT_ID)
          client_secret: $(CLIENT_SECRET)

❤️ CI/CD Health Probes & Feedback Hooks

🎯 Purpose

This section defines how real-time health checks, observability signals, and feedback hooks are integrated into every stage of the pipeline. This allows agents and humans to trace, monitor, and respond to failures or delays throughout the build and deployment lifecycle.

Pipelines are not black boxes — they emit traceable, observable signals at every stage.

🩺 Health Probes

Type Purpose
🧪 Pre-checks Verify tooling, secrets, repo state
🧱 Build Integrity Confirm successful compilation/artifacts
🧪 Test Coverage Probe Validate expected test thresholds
🚦 Deployment Readiness Ensure environment health and slot availability
🔁 Rollback Liveness Check rollback plan and infrastructure state

📡 Feedback Hooks

Channel Use Case
Azure DevOps Pipelines UI Stage badges, visual summaries
Slack / Teams Real-time status updates
Trace Graph (memory layer) Inject success/failure trace per run ID
GitHub/Azure Comments Annotate PRs with validation status
Observability Agents Trigger traces, logs, metrics collection

🧠 Agent Interactions

Agent Role
Pipeline Agent Adds probe stages and instrumentation metadata
DevOps Architect Agent Defines default hooks and feedback loops per environment
Observability Agent Parses emitted traces and correlates with blueprint/service ID

✅ Example: Health & Hook Definitions in YAML

steps:
  - task: HealthCheck@1
    inputs:
      target: build-artifacts
  - task: SlackNotification@1
    inputs:
      message: "✅ Build complete for $(Build.Repository.Name)"

🧩 Trace Metadata Emitted

  • runId
  • traceId
  • stageId
  • status
  • duration
  • agentId
  • observabilityTags

🌐 Environment-Aware Execution Graphs

🎯 Purpose

This section introduces dynamic execution graphs that adapt to target environments — such as dev, qa, staging, production. These graphs model what should run, in what order, under what conditions, per environment.

Each environment has distinct expectations, policies, and constraints — pipelines reflect that declaratively.

🧱 Key Concepts

Concept Description
Environment Matrix Defines supported environments with overrides
Conditional Execution Steps/stages gated by if or variable values
Approval Gates Manual or policy-based gates in protected environments (e.g., prod)
Branch/Tag Awareness Rules for main, release/*, feature/*, tags like v1.0.0
Fan-Out Execution Parallel deploys to multiple regions or slots

🌍 Example Environment Graph

flowchart TD
  Dev["🔧 Dev"] --> QA["🧪 QA"]
  QA --> Staging["🚀 Staging"]
  Staging --> Production["🏁 Production"]
Hold "Alt" / "Option" to enable pan & zoom

Each node represents a conditional stage block with different parameters, variables, or secrets.

🧠 Agent Roles

Agent Role
DevOps Architect Agent Defines the environment graph and transition policies
Pipeline Agent Implements conditional logic and matrix definitions in blueprint
Deployment Orchestrator Agent Executes ordered, policy-compliant rollout across environments

📁 Example: Azure DevOps Multi-Environment Stage

stages:
  - stage: Deploy_Dev
    condition: eq(variables['Build.SourceBranch'], 'refs/heads/develop')
  - stage: Deploy_Prod
    condition: and(succeeded(), startsWith(variables['Build.SourceBranch'], 'refs/tags/'))
    approval: manual

📦 Output Artifacts

  • .yaml with environment block overrides
  • .md summary with graph visual
  • .json metadata for pipeline planner agents

🧩 Pipeline Templates & Reuse Strategies

🎯 Purpose

This section defines how pipeline logic is modularized into reusable templates, enabling the Factory to avoid duplication and enforce DRY principles across services, libraries, environments, and teams.

Blueprints don’t just generate YAML — they generate composable pipeline building blocks with versioning, parameters, and lifecycle control.

📦 Template Types

Template Type Example Usage
build-template.yml Restore, compile, test, publish
deploy-template.yml Azure deployment (e.g., Function App, AKS, Bicep)
test-template.yml Test execution with results publishing
infra-init-template.yml Pulumi/Bicep bootstrap
ci-header.yml Trigger, variables, and pipeline-wide setup

🔄 Reuse Strategies

Strategy Description
Centralized Template Repository Factory-managed repo of versioned templates
Inline Includes (template:) Shared YAML files referenced in consuming blueprints
Version Pinning Agents inject specific git SHA or semantic version per pipeline
Parameterized Inputs Enable overrides for env, service name, runtime, test filter
Extension via Custom Steps Inject project-specific logic before/after standard templates

🧠 Agent Roles

Agent Role
DevOps Architect Agent Curates templates and ensures alignment to Factory standards
Pipeline Agent Selects appropriate templates and parameter mappings
Deployment Orchestrator Agent Resolves runtime substitutions and matrix definitions

📁 Example Template Usage in YAML

- template: templates/build-template.yml
  parameters:
    projectName: 'CustomerService'
    dotnetVersion: '8.0'
    runTests: true

🧠 Metadata Traced

  • templateId
  • templateVersion
  • parametersUsed
  • originPipelineBlueprint

🧠 Semantic Tags, Trace IDs & CI Telemetry

🎯 Purpose

This section formalizes how each pipeline instance becomes traceable, observable, and semantically rich — fully integrated into the AI Factory’s memory graph and runtime telemetry systems.

Every execution is more than just logs — it’s a knowledge node with lineage, purpose, result state, and semantic labels.

🏷️ Semantic Metadata Embedded in Pipelines

Metadata Key Purpose
traceId Unique execution lineage identifier per Factory run
agentId Source agent responsible for the pipeline blueprint
projectId Scoped context of the SaaS product or system
blueprintId Origin blueprint file used in generation
pipelineType e.g., build, test, deploy, security, post-release
stageLabels Human-friendly tags per stage (e.g., unit-test, hotfix)
commitHash Git version pinned for traceability
execOutcome success, failure, skipped, partial, etc.

📈 CI/CD Telemetry Targets

Channel Description
Azure DevOps Pipelines Native telemetry with enriched variables and logs
OpenTelemetry Collector Export pipeline spans, traces, and metrics
Factory Observability DB Internal trace graph used for historical QA & insights

🔄 Traceability Behavior

  • All generated pipelines inject traceId and agentId as global variables.
  • Telemetry agents hook into stage-level events, durations, outcomes.
  • Artifacts (logs, test results, deploy metadata) are stored and linked by traceId.

📁 Example YAML Injection

variables:
  - name: traceId
    value: $(Build.BuildId)-$(Build.SourceBranchName)
  - name: agentId
    value: microservice-generator-agent

🤖 Agent Involvement

Agent Responsibility
Pipeline Agent Injects semantic tags and trace identifiers
Observability Agent Connects pipeline to telemetry systems
QA Validator Agent Uses trace-linked builds to match tests & expectations

🛑 Rollback Logic, Safeguards & Controlled Releases

🎯 Purpose

This cycle introduces standardized rollback, deployment safeguards, and release control mechanisms into every pipeline blueprint. The goal is to enable safe, reversible, and policy-compliant delivery with zero human-in-the-loop assumptions.

Rollback is not an afterthought — it is a first-class stage in the Factory pipeline lifecycle.

🧪 Built-In Safeguards

Safeguard Type Description
❌ Failed Deployment Auto-trigger rollback stage based on health checks, status codes
🔄 Canary Rollback Restore previous version if traffic drop or metric alert triggered
🧪 Test Failure Hook Prevent promotion if validation suite fails
🔐 Security Policy Gate Block promotion if vulnerabilities exceed threshold (CVSS score, etc.)

🚨 Rollback Actions Supported

Rollback Mode Action Performed
🕰️ Infrastructure State Pulumi or Bicep state rollback to last successful deployment
📦 Artifact Reversion Re-deploy last known good Docker image or ZIP artifact
🔁 Traffic Routing Flip Repoint traffic to previous slot/version (AppService, AKS, etc.)
🧠 Memory Graph Restore Update observability state and memory links after rollback

📋 Controlled Release Mechanisms

Mechanism Use Case
Feature Flags Enable progressive feature rollout
Approval Gates Manual or AI-assisted validation steps
Environment Rings Dev → QA → Staging → Production promotion
Blue/Green Routing Instant fallback with DNS/traffic split

🤖 Agent Involvement

Agent Responsibility
DevOps Architect Agent Defines rollback paths and controls
Deployment Orchestrator Agent Executes rollback logic via agents/pipeline
Security Engineer Agent Enforces policy thresholds before promotion

📁 YAML Fragment Example

- stage: Rollback
  condition: failed()
  jobs:
    - deployment: RevertToPrevious
      environment: 'production'
      strategy:
        runOnce:
          deploy:
            steps:
              - task: Pulumi@1
                inputs:
                  command: 'destroy'
                  stack: 'prod'

🧩 Pipeline Blueprint Summary & Observability Tags

🎯 Purpose

To complete the pipeline blueprint, this cycle defines a summary section consolidating key metadata, observability hooks, and traceability annotations — enabling both human oversight and downstream agent automation.

The blueprint is not just a file — it's a living traceable node in the Factory memory graph, optimized for both reuse and inspection.

📄 Summary Block

Each pipeline blueprint includes a summary section at the end of the Markdown form and embedded in the JSON model:

summary:
  blueprintType: pipeline
  generatedBy: pipeline-agent
  traceId: "trace-92b3f"
  agentId: "devops-architect-agent"
  pipelineId: "ms-orders-deploy"
  pipelineType: "build-test-deploy"
  runtimeStack: "dotnet-core"
  ciProvider: "azure-devops"
  retryPolicy: "onFailure"
  rollbackPolicy: "auto"
  environmentScope: "QA → STG → PRD"
  lastUpdated: "2025-06-09T14:45:00Z"

🧠 Observability Tags and Export Fields

Tag / Field Use Case
traceId Used by all agents to cross-reference builds, errors, test coverage
pipelineId Enables identification of related artifacts (logs, metrics, results)
agentId Allows agent lineage and responsibility attribution
blueprintHash Deduplicates generation when same blueprint version exists
execOutcome Logs result of last execution, visible in trace explorer tools
versionTag Optional semver or custom tag (e.g., v1.0.3-hotfix)

📦 Blueprint Output Shapes (Recap)

Format Description
.yaml Main executable pipeline artifact
.md Human-readable studio/editable version
.json Parsed input/output for pipeline orchestration
embedding Semantic memory form for retrieval and search

✅ Final Agent Contributions Recap

Agent Key Contribution
Pipeline Agent Blueprint orchestration and execution
DevOps Architect Agent Overall structure, policies, gates
Security Engineer Agent Security stage and gate enforcement
Deployment Orchestrator Agent Runtime resolution and rollout logic