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📦 Infrastructure Blueprints

📘 What Is an Infrastructure Blueprint?

An Infrastructure Blueprint in the ConnectSoft AI Software Factory is a structured, traceable specification that defines the runtime, operational, and cloud-native deployment environment for a generated module or service.

It is not just YAML or Bicep — it is an intelligent artifact, enriched with context, constraints, observability hooks, and memory-linked lineage. It serves as the source of truth for provisioning, operating, and evolving the infrastructure behind services.

🎯 Why It Exists

Most infrastructure is either:

  • Duplicated and scattered across services
  • Inconsistent across environments
  • Poorly documented or manually crafted

The Infrastructure Blueprint solves this by offering:

  • 🧠 Agent-generated, deterministic, and diffable configurations
  • 📦 Output in GitOps-friendly formats: YAML, Bicep, Terraform, Helm
  • 🔁 Lifecycle management: regeneration, change tracking, rollback
  • 🎯 Alignment to service design, environment, security, and cost goals

🧩 Blueprint Types and Targets

Target Environment Examples
Kubernetes Deployments, Services, Ingress, HPA, etc.
Azure Infrastructure Bicep for App Services, Functions, Storage
CI/CD Fragments YAML steps, jobs, variables
Helm / Kustomize Outputs Templates with overlays
Monitoring & Security OTEL sidecars, RBAC, secrets, probes

🤖 Generated By

Agent Role
Infrastructure Architect Agent Designs blueprint format, conventions
Infrastructure Engineer Agent Resolves runtime overlays, emits deployable output
CI Pipeline Agent Uses blueprint to determine infra build steps
Observability Agent Hooks logs, traces, and metrics definitions

🔍 Output Shapes

Format Description
.yaml Kubernetes and CI/CD resources
.bicep Azure infrastructure declarations
.json Semantic model for inter-agent usage
.md Human-readable, editable specification
embedding Vector-indexed for search & trace link

📁 Naming & Location Convention

blueprints/infrastructure/{service-name}/infrastructure-blueprint.{yaml,md,json}

📎 Summary

Property Value
📄 Format Markdown + YAML + Bicep + JSON
🧠 Generated by Infra Architect + Infra Engineer Agents
🎯 Purpose Define full operational environment + deploy hooks
🔁 Lifecycle Regenerable, diffable, GitOps-compatible
📈 Tags traceId, agentId, serviceId, runtimeProfile

🧠 Role in AI Software Factory

🏭 Factory Layer Placement

Infrastructure Blueprints operate at the Infrastructure Orchestration Layer of the ConnectSoft AI Software Factory. They act as the contract between the logical system design (what needs to run) and the operational foundation (where and how it runs).

flowchart TD
  ServiceDesign["🧱 Service Blueprint"]
  InfrastructureBlueprint["🏗️ Infrastructure Blueprint"]
  IaCTemplates["📦 IaC Templates (YAML/Bicep)"]
  Cluster["☁️ Cluster / Cloud Runtime"]

  ServiceDesign --> InfrastructureBlueprint
  InfrastructureBlueprint --> IaCTemplates
  IaCTemplates --> Cluster
Hold "Alt" / "Option" to enable pan & zoom

🤖 Agent Flow and Consumption

Agent Role
Microservice Generator Agent Signals deployment needs for a new service
Infrastructure Architect Agent Designs blueprint structure and overlays
Infrastructure Engineer Agent Emits environment-specific infra manifests
CI Pipeline Agent Uses blueprint to determine pipeline infrastructure stages
Observability Agent Injects OTEL config and telemetry definitions
Security Agent (future) Validates policies and secrets compliance

🔁 Factory Lifecycle Integration

  1. Trigger: A new microservice or API gateway is planned
  2. Input: Service Blueprint + Edition Model + Runtime Profile
  3. Generated: Infrastructure Blueprint with Kubernetes, Helm, or Bicep artifacts
  4. Emitted: Stored in Git + Registered in Memory + Event emitted (InfrastructureBlueprintCreated)
  5. Used by: CI/CD pipelines, GitOps agents, QA validators, and Observability processors

🔄 Regeneration Triggers

Trigger Event Action Taken
EditionModelChanged Recompute resource overlays per environment
ServiceBlueprintUpdated Resync infra blueprint to new contracts
SecurityPolicyEvolved Re-validate and reapply secrets and RBAC
ObservabilityProfileChanged Re-inject metrics, probes, and OTEL configs

Every Infrastructure Blueprint is connected to:

  • 🔗 traceId: Original service + edition lineage
  • 📦 templateId: The IaC template used
  • 🧠 agentId: Who generated and reviewed it
  • 📈 usageMetrics: Deployment success, errors, and history
  • 📚 runtimeTags: Cluster, environment, tenant, namespace

📎 Summary

Concept Value
🎯 Purpose Bridge between logical services and physical environments
🏗️ Consumed by CI/CD, GitOps, QA, Observability, Security
🔁 Lifecycle Flow Generated → Emitted → Validated → Deployed → Traced
🧠 Memory Shape Markdown + IaC + Events + Semantic Vector + Policy Snapshots

🧠 In the Factory, infrastructure isn’t a deployment afterthought — it’s a traceable product of software intent, embedded into generation flows.

🏗️ Types of Infrastructure Artifacts

Infrastructure Blueprints generate a variety of artifacts depending on the target runtime, environment, and service type. These artifacts are not hardcoded — they are selected and parameterized by agents using templates, overlays, and profiles.

📦 Artifact Categories

Category Description
🛠️ Kubernetes Resources Declarative YAML for deployments, services, ingresses, config maps, etc.
🧱 Infrastructure-as-Code Azure Bicep modules, Terraform plans, or ARM templates
📦 Helm/Kustomize Charts Packaged service-specific Kubernetes deployments with overlays
🔧 CI/CD Deployment Fragments Pipeline YAMLs, reusable jobs, environments, deployment gates
🔍 OTEL & Observability Sidecars, logging rules, probes, and metrics exporters
🧪 Test Environments Infrastructure scaffolding for QA or ephemeral environments (dev/test)
🔒 Security Artifacts RBAC roles, pod security policies, secrets management

🧠 Artifact Selection Logic

The infrastructure generation pipeline dynamically chooses what artifacts to emit based on:

  • 🧩 Service Type (Worker, API, Gateway, Function)
  • 🌍 Target Environment (dev, staging, prod)
  • ☁️ Cloud Platform (Azure, Kubernetes, Static Hosting)
  • 🧠 Edition Constraints (Memory-only, Region-specific, Premium-only)
  • 🔒 Security Requirements (Secrets store, vault access, identity binding)

✨ Examples

Service Type Emitted Artifacts
Microservice deployment.yaml, service.yaml, otel-sidecar.yaml, rbac.yaml, bicep/
API Gateway ingress.yaml, tls.yaml, configmap.yaml, helm-values.yaml
Worker deployment.yaml, azure-function.bicep, autoscaler.yaml, queue-secrets.yaml
Library No runtime artifacts; blueprint emits metadata for pipelines

📁 Output Tree (Sample)

blueprints/infrastructure/notifications/
├── infrastructure-blueprint.md
├── deployment.yaml
├── service.yaml
├── otel-sidecar.yaml
├── configmap.yaml
├── rbac.yaml
├── bicep/
│   ├── function-app.bicep
│   └── storage.bicep
├── pipeline-deploy.yml

📎 Summary

Output Domain Examples
Kubernetes Deployment, Service, Ingress, ConfigMap
Bicep/Terraform AppService, Function, BlobStorage, KeyVault
Helm/Kustomize values.yaml, Chart.yaml, overlay/
CI/CD azure-pipelines.yml, env-deploy.yml
Observability otel-collector.yaml, probes, annotations
Security rbac.yaml, secrets.yaml, aad-pod-identity.yaml

🏗️ Infrastructure Blueprints don’t just produce one file — they emit a bundle of cloud-native, traceable building blocks tailored to the service's domain and execution context.

🧩 Blueprint vs Template vs Policy vs Manifest

The ConnectSoft AI Software Factory separates infrastructure concerns into four distinct but interrelated concepts to maintain clarity, modularity, and reuse.

🧭 Definitions

Concept Purpose
Blueprint Declarative, traceable plan describing what infrastructure is needed and why
Template Reusable, parameterized file that produces manifests (e.g., Bicep, YAML, Helm)
Policy Rule or constraint enforced during generation or validation (e.g., no public IPs)
Manifest The final, generated artifact — ready to apply or deploy in an environment

🧱 Flow Between Concepts

flowchart TD
  Blueprint["📘 Infrastructure Blueprint"]
  Template["📦 IaC Template (YAML/Bicep/Helm)"]
  Policy["🔐 Policy (Rules/Guards)"]
  Manifest["🧾 Final Manifest (applied)"]

  Blueprint --> Template
  Blueprint --> Policy
  Template --> Manifest
  Policy --> Manifest
Hold "Alt" / "Option" to enable pan & zoom
  • Blueprint defines the structure, intention, environment, and runtime behavior.
  • Template receives parameters from blueprint (e.g., image name, env vars).
  • Policy enforces correctness, security, cost, and compliance rules before emission.
  • Manifest is the final output to apply (e.g., via kubectl apply, GitOps, Terraform CLI).

🔁 Comparison Table

Aspect Blueprint Template Policy Manifest
📄 Format Markdown + JSON YAML / Bicep / Helm JSON / YAML / Rego YAML / JSON
🧠 Owner Infra Architect Agent Template Catalog (reusable) Security, Platform, Cost Agents CI/CD or GitOps Runner
🎯 Role Declares what infra is needed Provides how it should look Ensures it’s valid & compliant Used to apply infrastructure
🔄 Regenerable ✅ Yes (agent-owned) ✅ Yes (catalog updates) ✅ Yes (policy evolution) ❌ No (immutable output)

🧠 Agent Responsibilities

Agent Interaction
Infrastructure Architect Defines and updates blueprint structure and metadata
Infrastructure Engineer Maps blueprint → template → manifest
Security Agent Applies inline or external policy validations
Template Catalog Registrar Maintains available templates and their bindings
CI/CD Agent Validates and deploys manifests to runtime

📎 Summary

Concept Immutable? Human-Readable Machine-Parsed Used In
Blueprint ✅ Yes ✅ Yes ✅ Yes Agents + Docs
Template ✅ Yes ✅ Yes ✅ (parameterized) Generator Logic
Policy ✅ Yes ✅ Optional ✅ Yes Validation Pipeline
Manifest ✅ Yes ✅ Yes ✅ Yes CI/CD, GitOps

🧩 Together, these elements ensure that infrastructure is declarative, reusable, validated, and agent-ready — not brittle files in source control.

🔁 Lifecycle from Blueprint to GitOps Deployment

The Infrastructure Blueprint lifecycle ensures that infrastructure artifacts move from idea → plan → validation → deployment through a traceable, agent-driven pipeline. This pipeline aligns with GitOps, IaC, and agentic automation principles in the ConnectSoft AI Software Factory.

📈 End-to-End Lifecycle Phases

Phase Description
1. Signal Service or gateway needs runtime infrastructure
2. Blueprint Infrastructure Architect + Engineer agents generate the base blueprint
3. Overlay Edition + environment + tenant overlays are applied
4. Templates CLI templates, Bicep, and YAML fragments are resolved
5. Policy Check Security and cost policies are enforced on blueprint + rendered output
6. Emission Final manifests are emitted to Git, blob, or artifact store
7. CI/CD Push CI Pipeline Agent or GitOps controller deploys manifest to target cluster
8. Trace Update Event, memory, and observability hooks are recorded post-deployment

🧠 Lifecycle Orchestration (Agent Flow)

flowchart TD
  Plan["🧠 Microservice Generator Agent"]
  InfraArchitect["🏗️ Infra Architect Agent"]
  InfraEngineer["👷 Infra Engineer Agent"]
  Policy["🔐 Security/Cost Policy Agent"]
  CI["🛠️ CI/CD Agent"]
  GitOps["☁️ GitOps Controller"]
  Cluster["🚀 Runtime Cluster"]

  Plan --> InfraArchitect
  InfraArchitect --> InfraEngineer
  InfraEngineer --> Policy
  Policy --> CI
  CI --> GitOps
  GitOps --> Cluster
Hold "Alt" / "Option" to enable pan & zoom

📦 Emission Outputs

Target Output Type
Git Repository (IaC) deployment.yaml, bicep, helm/
GitOps (ArgoCD/Flux) Sync target for kustomize or helm charts
Azure DevOps Pipeline Inline or templated YAML jobs
Blob Storage (Artifact Repo) Packaged charts or terraform plans
Memory Store (Graph/Vector DB) Blueprint, trace, metadata for reusability

🧬 Traceability in the Lifecycle

Every phase persists traceId, projectId, and agentId. This ensures a full graph can be reconstructed linking:

  • 🧱 Service Blueprint → 🏗️ Infra Blueprint → 🧾 Manifest → 🧠 Memory
  • 🔄 Regenerations and deltas
  • 📈 Deployment success, retries, failures

⏳ Blueprint Change Lifecycle

Change Type Triggered Regeneration Scope
Environment Overlays Updated Only deploy.yaml / kustomize overlays
Service Contract Changed Re-renders blueprint + inputs to template resolver
Policy Tightened Forces validation of affected artifacts only
CI Config Updated Rewrites pipeline fragments or jobs

📎 Summary

Stage Responsibility Stored In
Generation Infra Architect Agent Blob, Vector, Git
Validation Policy Agent CI/CD pipeline
Emission Infra Engineer Agent Git / Helm Repo
Deployment GitOps Agent / CI Agent Cluster + Monitoring
Traceability Orchestration Layer Memory Graph / Audit DB

🔁 Infrastructure isn’t static — it's living, regenerable, and observable throughout its lifecycle. Every version, every deployment, every error has a source.

🧷 Traceability-First Infrastructure Design

In the ConnectSoft AI Software Factory, infrastructure is not opaque. Every artifact, decision, and deployment is trace-linked across agents, services, environments, and runtime outputs.

🛰️ Traceability is the connective tissue that makes infrastructure auditable, observable, and regenerable — from blueprint to production runtime.

Each Infrastructure Blueprint includes embedded trace fields:

Field Purpose
traceId Unique ID tying infrastructure to service + edition
agentId Which agent generated or modified this blueprint
serviceId ID of the microservice, API gateway, or module
environment Deployment environment context (dev, test, staging, prod)
clusterName Target cluster or cloud region
templateVersion Template registry version used to render infrastructure
policySnapshot Reference to policy rules validated against this version
generationTime UTC timestamp of creation
deploymentEvents Linked logs, outcomes, and metrics from downstream deployment

📘 Sample Trace Annotation Block (in JSON)

{
  "traceId": "svc-notifications-trace-943a",
  "agentId": "infrastructure-engineer-agent",
  "serviceId": "notifications",
  "environment": "staging",
  "clusterName": "aks-core-west-eu",
  "templateVersion": "v2.7.1",
  "policySnapshot": "security-rules-v5",
  "generationTime": "2025-06-09T16:43:11Z"
}

📚 Trace Graph Use Cases

Use Case Example
🧠 Agent Memory Recall Regenerate identical blueprint from trace vector
🚀 Deployment Forensics Reconstruct full infra stack used during failed rollout
🔁 Delta Auditing Compare trace-linked versions of blueprint and manifests
📊 Reporting & Analytics Trace which agents, clusters, or templates are most used
🛡️ Compliance Verification Show policies validated and applied to specific resources

🧠 Observability Layer Integration

Trace IDs link directly to:

  • 📊 OpenTelemetry spans from the deployed services
  • 🧾 Deployment and pipeline logs (via CI agent hooks)
  • 🧠 Agent memory graphs and knowledge queries
  • 🧬 Cross-blueprint lineage (infra ↔ service ↔ test)

🔐 Security and Compliance Extensions

Trace linkage enables:

  • 🔍 Secret injection tracking (via Vault / KeyVault integration)
  • 🧾 RBAC evaluation trails (who had access and when)
  • 📋 Policy override justifications (e.g., “allowed for dev only”)

📎 Summary

Feature Value
Trace Granularity Per-service, per-environment, per-deployment
Embedded In Blueprint JSON, emitted manifests, pipeline metadata
Stored In Memory Graph, Trace DB, Git metadata
Enables Auditing, regeneration, analysis, forensic debugging, cross-agent reuse

🔗 Traceability is not just for logs — it’s how infrastructure becomes a living knowledge artifact within the AI Software Factory.

📐 Overlays and Environment-Specific Customization

🧬 What Are Overlays?

Overlays allow Infrastructure Blueprints to adapt to different environments (dev, test, staging, prod) or editions (e.g., premium vs. free). They define:

  • 🔄 Mutations of the base configuration (e.g., replica count, secrets)
  • 💼 Context-aware variables (e.g., subscription ID, region, tenant)
  • 🛡️ Scoped policies (e.g., public access allowed in dev, denied in prod)

They ensure reusability of blueprints while aligning deployments with operational constraints.

🧭 Overlay Application Workflow

flowchart TD
  Blueprint["📘 Base Infrastructure Blueprint"]
  Overlay["📐 Environment or Edition Overlay"]
  Result["🧾 Environment-Specific Manifest"]

  Blueprint --> Result
  Overlay --> Result
Hold "Alt" / "Option" to enable pan & zoom

Overlays are not imperative scripts — they are declarative, validated mutations applied via templating engines or JSON Patch-like transformations.

📁 Overlay Folder Structure

blueprints/infrastructure/notifications/
├── overlays/
│   ├── dev.yaml
│   ├── staging.yaml
│   ├── prod.yaml
│   └── premium-edition.yaml

🔁 Common Overlay Use Cases

Use Case Overlay Example
✅ Enable diagnostics in dev otel.sidecar.enabled: true
📦 Scale in prod replicaCount: 5
🔐 Vault in staging secretsProvider: azure-keyvault
🌍 Region switch location: westeurope
💡 Toggle feature flags features.asyncRetry: false

🤖 Overlay Generation

Agent Role
Infrastructure Engineer Maps service/environment to overlay targets
Edition Resolver Agent Applies edition-specific constraints
Security Agent Flags invalid overlays (e.g., disallowed ports)

🧠 Overlay Traceability

Each overlay mutation is trace-linked to:

  • overlayId
  • targetEnvironment
  • sourceBlueprintId
  • agentId
  • validationTimestamp

📎 Summary

Concept Value
📄 Format YAML (patch style or values)
🔁 Applied To Base blueprint before rendering templates
🧠 Generated By Infra Engineer + Edition Resolver
📈 Traceable Yes, every applied overlay is logged and versioned
🛡️ Validated By Security Agent + Environment Profiles

📐 Overlays allow a single blueprint to scale across environments, editions, and tenant contexts — without duplicating logic or risking drift.

📦 IaC Template Resolution & Linking

🔧 Supported Infrastructure-as-Code Targets

Infrastructure Blueprints can resolve into multiple IaC formats, selected based on:

  • Service profile (e.g., real-time API, batch ETL, event-driven)
  • Platform capabilities (Kubernetes, Azure App Services, etc.)
  • Target environment (dev/staging/prod/tenant)
  • Agent preferences and blueprint trace lineage
Format Use Case Examples
Bicep Azure-native services: App Services, Storage, SQL
Helm Kubernetes microservices, sidecars, Ingress
Kustomize Simple patch-based overlays for shared YAML
Pulumi Complex, conditional logic using .NET code
Terraform Multi-cloud base infra, secrets, resource groups

🤖 Template Selection by Agent Logic

Agent Role
Infrastructure Architect Suggests primary IaC format per domain and cloud guidelines
Infrastructure Engineer Translates blueprint semantics into template blocks
DevOps Architect Links blueprint to CI/CD jobs, pipelines, pre/post conditions

Each template is not hardcoded — it is selected and rendered by the Factory based on structured metadata in the blueprint:

deploymentTarget:
  runtime: kubernetes
  templateStrategy: helm
  cloud: azure

🔗 Template Linking Mechanism

Each blueprint references:

  • ✅ The template source (e.g., connectsoft.helm.templates.microservice)
  • 🔁 The binding context (serviceId, runtimeProfile, traceId)
  • 📎 The render engine (Helm, Pulumi runner, Terraform CLI)
  • 📄 The output destination (/generated/infrastructure/xyz.yaml)

This allows agents to:

  • 💾 Inject context-aware parameters (e.g., resourceName, env)
  • 🔐 Insert secrets and RBAC safely
  • 📈 Attach observability plugins
  • 🧪 Validate the rendered output pre-deployment

🧩 Hybrid Template Strategies

In many cases, multiple formats are combined:

templateResolution:
  helmChart: "connectsoft-microservice"
  bicep: "infra/common.bicep"
  pulumiOverlay: true
  overlays:
    - "overlays/dev.yaml"

This allows the Factory to:

  • Deploy infra with Pulumi + .NET automation
  • Overlay service logic with Helm charts
  • Declare shared dependencies with Bicep or Terraform

📁 Output Convention

generated/infrastructure/{service-name}/
├── main.bicep
├── main.tf
├── Chart.yaml
├── values.yaml
├── main.cs (for Pulumi.NET)
└── generated-from: traceId + agentId

🧠 Traceability & Regeneration Support

Every generated file includes metadata headers like:

# generated-by: Infrastructure Engineer Agent
# traceId: 74dc55ac
# blueprintVersion: v3.2
# linkedTemplate: pulumi-dotnet

These enable:

  • 🔁 Fast diffs
  • 📜 Regeneration triggers
  • 🔍 CI drift checks

🧠 Input Sources: Service Blueprint, Product Constraints, Observability Metadata

🔌 Core Upstream Inputs

The Infrastructure Blueprint is not an isolated artifact. It is generated by synthesizing signals from upstream blueprints and constraints, specifically:

Input Source Purpose
🧱 Microservice Blueprint Defines runtime profile, messaging needs, exposed ports
📘 Product Blueprint Captures functional requirements, user flows, feature flags
🔍 Observability Tags Enriches with metrics, traces, logs, alert conditions
📜 Security Policies RBAC, port access, secret scopes
📦 Platform Target Azure/K8s resolution for templates (via Cloud Context Agent)

🧬 These inputs form a memory-traced dependency chain, allowing regeneration and auditing when any upstream input changes.

🧠 Memory Graph Lineage

Each Infrastructure Blueprint links upstream via trace edges:

InfrastructureBlueprint
├── traceFrom: MicroserviceBlueprint:notifications
├── traceFrom: ProductBlueprint:notification-system
├── traceFrom: ObservabilityProfile:otel-basic

This ensures every resource is:

  • ✅ Aligned with system goals
  • 🧠 Regenerable from first principles
  • 🔁 Validatable during CI pipeline runtime

🎛️ Derived Properties from Inputs

Property in Infra Blueprint Derived From
replicaCount Load profile + deployment environment
serviceType Microservice blueprint runtime config
sidecars.enabled Observability profile (otel, prometheus)
resources.limits Edition profile + product performance constraints
containerRegistry Platform context (azure, private, shared)

These values are not manually entered — they are resolved by agents from linked inputs.

🧠 Agent Collaboration Flow

flowchart LR
  Product["📘 Product Blueprint"]
  Microservice["🧱 Microservice Blueprint"]
  Observability["📊 Observability Profile"]
  Infra["🧠 Infrastructure Blueprint"]

  Product --> Infra
  Microservice --> Infra
  Observability --> Infra
Hold "Alt" / "Option" to enable pan & zoom

This diagram is traceable per service and visible in Studio or via API.

🧪 Validation Snapshot

When generating the infrastructure blueprint, the system emits a trace validation snapshot, such as:

{
  "blueprintId": "infra-xyz",
  "inputs": {
    "microservice": "notifications",
    "productFeature": "event-routing",
    "observability": "otel-basic"
  },
  "validationStatus": "ok"
}

This snapshot is stored in the trace index and injected into CI logs.

🔁 Regeneration and Change Propagation Flow

🔄 Why Regeneration Is Essential

Infrastructure in the Factory is not static — services evolve, environments scale, and policies change. The Infrastructure Blueprint is designed to be:

  • 🔁 Regenerable on demand
  • 🧠 Trace-aware (based on upstream blueprint changes)
  • 🧪 Validation-capable (for safe updates and diffs)

This eliminates drift, removes manual YAML hacks, and enables automated CI/CD verification at scale.

🔁 Triggers for Regeneration

Trigger Source Reason
Microservice Blueprint update New endpoint, new port, queue subscription
Product Blueprint change New feature flags, runtime behavior shifts
Observability Profile update New logs/metrics/traces injected
Runtime Profile shift Stateless → durable, sync → async
Security Policy revision Secrets moved to Key Vault, RBAC tightened

Each trigger leads to selective blueprint regeneration using trace-backed agents.

⚙️ Agent-Orchestrated Flow

sequenceDiagram
  actor Dev
  participant ProductAgent
  participant MicroserviceAgent
  participant InfraArchitect
  participant InfraEngineer

  Dev->>ProductAgent: Change in feature flags
  ProductAgent->>MicroserviceAgent: Propagates new config
  MicroserviceAgent->>InfraArchitect: Notifies infrastructure impact
  InfraArchitect->>InfraEngineer: Triggers regeneration of blueprint
Hold "Alt" / "Option" to enable pan & zoom

This flow ensures intelligent, non-destructive updates to IaC artifacts.

🧪 Blueprint Diffing and Impact Surface

Before overwriting the deployed blueprint, a diff plan is generated:

plan:
  - action: add
    resource: AzureStorageAccount
    target: bicep
  - action: update
    resource: KubernetesDeployment
    field: env.POSTGRES_URL
  - action: remove
    resource: HelmIngressPath

This is reviewed in CI and can be vetoed by change control agents or manual approvers.

📦 Immutable Snapshot Versioning

Each regenerated blueprint includes:

  • versionId: v1.2.3
  • traceParentId
  • generatedByAgentId
  • diffMetadata

The platform preserves all blueprint versions, enabling:

  • ⏪ Rollbacks
  • 🔍 Audit trails
  • 🧠 Memory re-ingestion of historical infra

🛡️ Security Scaffolding: RBAC, Network Policies, Secrets

🧱 Built-in Security Foundation

Each Infrastructure Blueprint enforces Security-First Architecture by embedding:

  • 🧩 Role-Based Access Control (RBAC) definitions
  • 🌐 Network isolation rules (e.g., namespace, subnet, policy)
  • 🔐 Secure secret injection and management
  • 🕵️ Runtime policy hooks (e.g., Azure Defender, OPA constraints)

Security scaffolding is generated per service based on blueprint lineage and environment tier.

🔐 Secrets and Sensitive Configuration

Secrets are never hardcoded — instead:

Mechanism Strategy
Azure Key Vault Used in cloud-native deployments with identity binding
Kubernetes Secrets Injected via sidecar or environment mount, base64 encoded
Pulumi.Secret<T> Used in .NET Pulumi blueprints with encryption at rest

Each secret is trace-labeled with:

secret:
  name: postgresPassword
  scope: service
  origin: secret-manager-agent
  usage: environment

🔐 Secrets Linking Example

env:
  - name: POSTGRES_PASSWORD
    valueFrom:
      secretKeyRef:
        name: postgres-secret
        key: password

Or Pulumi equivalent:

var dbPassword = config.RequireSecret("dbPassword");

These declarations are generated automatically based on service dependencies and runtime profile.

👮 RBAC and Access Profiles

RBAC is defined based on:

  • 📦 Service roles (e.g., background processor, gateway, controller)
  • 🔁 Event bus permissions (e.g., topic publish/subscribe)
  • 🔍 Observability scopes (e.g., can emit logs, metrics)

Kubernetes RBAC snippet:

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: notification-writer
rules:
  - apiGroups: [""]
    resources: ["pods", "events"]
    verbs: ["create", "get", "list"]

🔒 Network Policies and Egress Rules

Network boundaries are established using:

Context Enforcement Layer
In-cluster isolation NetworkPolicy (Kubernetes)
Cross-service access Service Mesh (e.g., Linkerd, Istio)
Public exposure Ingress Gateway, Firewall Rules

Each rule is auto-generated based on declared dependencies.

🧠 Traceable Security Layer

Security declarations include:

  • traceId
  • generatedBy: security-scaffolder
  • environment: staging | prod
  • linkedTo: microservice-blueprint.notifications

This allows for automated audits, regeneration after incident response, and compliance mapping.

📡 Observability Integration (OTEL, probes, logging targets)

🔭 Built-In Observability Hooks

Every Infrastructure Blueprint includes observability scaffolding aligned with the Observability-Driven Design principle. It ensures:

  • 🔁 Full traceability across agents, services, and runtime
  • 📈 Unified telemetry streams: traces, logs, metrics
  • 📦 Ready-to-wire probes for readiness, liveness, and startup
  • 🧠 Semantic links to monitoring dashboards, alerts, and SLOs

Observability is first-class, not an afterthought — embedded into blueprint lifecycle.

📦 OTEL Sidecar and Instrumentation

OpenTelemetry is configured by default for all services using:

Resource Type Strategy
otel-collector Injected via Helm or Bicep templates
exporters Azure Monitor, Jaeger, Prometheus
instrumentation .NET SDK auto-injected
sidecar strategy Optional: envoy/collector sidecars

Helm/K8s snippet:

containers:
  - name: otel-collector
    image: otel/opentelemetry-collector
    volumeMounts:
      - name: config
        mountPath: /etc/otel

Pulumi (.NET) snippet:

var otelConfigMap = new ConfigMap(...);
var otelCollector = new Deployment(...);

📟 Probe Configuration

Each service includes default health probes:

Probe Type Purpose Default Path
readinessProbe Traffic control on boot /healthz
livenessProbe Crash recovery trigger /livez
startupProbe Slow initialization safety check /startz

Example YAML:

readinessProbe:
  httpGet:
    path: /healthz
    port: 8080
  initialDelaySeconds: 10
  periodSeconds: 5

📜 Logging Targets and Strategy

Target Mechanism
stdout/stderr Default for containers
Azure Log Analytics Exporter via OTEL or Azure Monitor agent
Application Insights Auto-wired for App Services

Logging structure is enriched with:

  • correlationId
  • traceId
  • agentId
  • deploymentVersion

📈 Metric Enrichment

Each blueprint embeds a metrics profile with:

  • Service-level metrics: http_requests_total, latency, error_rate
  • Custom domain metrics: emails_sent, jobs_processed
  • System metrics: CPU, memory, IO, GC, DB

All metric definitions are stored in the trace memory graph and reused in dashboards.

Blueprint includes:

observability:
  dashboardTemplate: grafana/notification-service.json
  alertRules:
    - condition: error_rate > 0.05
      action: page_team

These files are stored in /dashboards/{service} and /alerts/ for deployment.

⚙️ Runtime Profiles: stateless, durable, real-time

🧬 Why Runtime Profiles Matter

Different services require different execution guarantees, resource strategies, and runtime behaviors. Each Infrastructure Blueprint declares a runtime profile that informs:

  • Provisioning strategy
  • Resource limits and autoscaling
  • Volume mounts and persistence
  • Message/event guarantees (e.g., at-least-once delivery)

The runtime profile defines how the infrastructure is shaped around the execution model of the service.

🧩 Supported Runtime Profiles

Profile Characteristics
stateless Ephemeral, horizontally scalable, auto-restarted (e.g., API, gateway)
durable Persistent state, bounded concurrency, backed by volume or DB (e.g., processors)
real-time Low-latency, fast recovery, health-sensitive (e.g., streaming processors)
cron Scheduled workloads with specific retry and success behavior
long-running Background workers with checkpointing and visibility (e.g., hangfire)

📦 Profile-Driven Infrastructure Adjustments

Aspect Example (Stateless) Example (Durable)
Storage None PVC or volume mount
Liveness probe Aggressive restart allowed Slow restart with grace period
Deployment strategy Rolling Recreate or blue/green
Retry policy Fast fail Circuit-breaker with retry delay
Scaling strategy HPA (CPU, latency) Queue length-based autoscaling
Network configuration Ingress + Service Mesh Private cluster + direct routing

These configurations are auto-injected from the runtime profile declared in the Service Blueprint.

🧠 Runtime Profile Declaration

In infrastructure-blueprint.json:

{
  "runtimeProfile": {
    "type": "durable",
    "source": "microservice-blueprint.notifications",
    "scaling": {
      "minReplicas": 1,
      "maxReplicas": 5,
      "trigger": "queueLength"
    },
    "probes": {
      "liveness": "/livez",
      "readiness": "/readyz"
    }
  }
}

In Pulumi or Bicep, this feeds resource provisioning logic and annotations.

🔍 Profile Validation and Overrides

The Infrastructure Engineer Agent validates runtime profiles against:

  • Product tier (e.g., Free tier disables real-time processing)
  • Hosting environment (e.g., stateless only on Azure App Service)
  • Security and cost policies (e.g., no durable workloads on preview cluster)

Override is allowed only via:

runtimeProfile:
  override: true
  reason: "approved exception for backpressure testing"

🌍 Multi-Tenant & Multi-Region Blueprinting

🏢 Supporting Scalable SaaS Architectures

In ConnectSoft’s Factory, infrastructure must support multi-tenant SaaS and regional failover as first-class concerns. Each Infrastructure Blueprint defines explicit metadata and overlays to:

  • 🏢 Isolate tenant data, access, and identity
  • 🌐 Deploy workloads across geo-distributed regions
  • 🔄 Route traffic using smart ingress/load balancers
  • 🧠 Encode tenant-awareness into agents and observability

This ensures that generated infrastructure aligns with the SaaS maturity model and operates globally with tenant separation and failover guarantees.

🧩 Tenant Models Supported

Mode Description
Shared One infra shared across tenants, app-level isolation
Isolated Each tenant gets dedicated namespace or resource group
Hybrid Shared app, isolated DB/storage (most common setup)

Each blueprint explicitly declares:

tenancy:
  model: hybrid
  isolationLevel: data + auth
  strategy: sharedApp + tenantDb

🌐 Regional Deployment Strategies

Strategy Description
Multi-region Replicate services in active-active mode
Failover-ready Passive standby cluster with DNS-based switch
Geo-aware Ingress routes users to nearest region

Example overlay:

regions:
  - name: eastus
    mode: active
  - name: westeurope
    mode: standby

🧠 DNS and Traffic Control

Blueprints include:

  • DNS zones and records (e.g., tenant123.example.com)
  • Azure Front Door / Traffic Manager config
  • Ingress annotations for geo-routing, tenant-routing
  • TLS certificates (wildcard or per-tenant)

Ingress YAML:

annotations:
  nginx.ingress.kubernetes.io/use-regex: "true"
  nginx.ingress.kubernetes.io/server-snippet: |
    if ($host ~* "^tenant123\.") {
      set $tenant_id "123";
    }

🧪 Testing and Isolation Checks

Agents simulate and verify:

  • Can tenant X access tenant Y’s resources? (should fail)
  • Is regional failover possible within 60s?
  • Are tenant-specific metrics separated?

These validations are embedded in the Test Blueprint and enforced in CI environments.

🔐 Identity Scopes per Tenant

Each blueprint includes tenant-aware security scopes for:

  • Azure AD / B2C instances
  • Secrets and KeyVault permissions
  • Event bus routing (e.g., per-tenant topics or partitions)

🔁 Lifecycle Hooks & Deployment Triggers

🚀 Declarative Lifecycle Management

Each Infrastructure Blueprint defines explicit lifecycle hooks and triggering conditions to:

  • Automate environment provisioning
  • Control staging → production promotion
  • Coordinate rollbacks, restarts, and blue/green transitions
  • Activate observers and cleanup logic post-deployment

Lifecycle behavior is not hidden in CI/CD scripts — it’s declaratively encoded in the blueprint for traceability and regeneration.

📦 Hook Types

Hook Type Purpose Trigger Event
preDeploy Checks, secrets, service discovery validation Before first deployment
postDeploy Indexing, version tagging, success alert After successful rollout
preDestroy Backup, notification, graceful disconnect Before deletion
onRollback Metrics reset, alert suppress, state restore On failure or manual rollback

Example:

lifecycle:
  preDeploy:
    - validateSecrets
    - checkDependencies
  postDeploy:
    - emitVersionTag
    - sendSlackAlert

🔗 Hook Execution Mechanisms

Environment Hook Driver
Azure Pipelines .yml job steps or reusable tasks
Pulumi (.NET) Stack.RunAfter(...)
Kubernetes Init containers, Jobs, postStart
Helm Charts pre-install, post-upgrade hooks

Agents are responsible for injecting and validating these hooks during the orchestration flow.

⚠️ Idempotency and Failure Strategy

All hooks are:

  • Idempotent by default (repeatable, no side effects)
  • Version-aware (e.g., postDeploy triggers only for major version change)
  • Isolated in execution scope with dedicated logs and retry policy

Failure behavior is declared explicitly:

onRollback:
  strategy: revertToPreviousVersion
  alert: "ops@connectsoft.ai"
  cleanState: true

🧪 Hook Testing and Dry-Runs

Blueprints optionally define simulation runs:

testHooks:
  mode: dry-run
  targets:
    - preDeploy
    - postDeploy

This is executed in non-production CI environments to ensure hook correctness before real deployment.

🧱 Environment Overlays (dev, staging, prod)

🧭 Why Overlays Matter

Different environments (e.g., dev, staging, prod) require distinct infrastructure behaviors. The Environment Overlay mechanism in the Infrastructure Blueprint enables:

  • Resource configuration overrides
  • Scaling adjustments
  • Logging verbosity changes
  • Secret and endpoint injection
  • Safe testing before production rollout

Instead of duplicating YAML or pipelines, the blueprint supports environment-aware overlays with traceable deltas.

🧩 Overlay Structure

Each blueprint includes a base and optional overlays:

infrastructure:
  base:
    cpu: 500m
    replicas: 1
    logLevel: debug
  overlays:
    staging:
      replicas: 2
      logLevel: info
    prod:
      cpu: 1
      replicas: 3
      logLevel: warn

Overlays apply on top of the base, and override only the specified values.

🧪 Use Cases for Overlays

Concern Example (dev) Example (prod)
Logging debug level warn or error
Scaling 1 replica 3+ replicas + HPA
Secrets Dummy values Linked to Key Vault
Image Pull Policy Always IfNotPresent
Cost Constraints Use B1 SKU Use P1v2 SKU
Ingress Exposure Internal Public with TLS

📦 Supported Environments

Factory standard supports:

  • dev (sandbox)
  • qa (test suite + agents)
  • staging (pre-prod simulation)
  • prod (live workloads)
  • hotfix (temporary patch)
  • preview (limited beta access)

Blueprints can opt into subsets:

environments:
  - dev
  - staging
  - prod

And map agent behavior accordingly.

🔁 Overlay Validation

Overlays are validated by the Infrastructure Engineer Agent:

  • All required keys are present
  • Base + overlay = valid full spec
  • Conflicts and misalignments are flagged

Example validation result:

{
  "environment": "prod",
  "status": "valid",
  "warnings": ["logLevel=warn may reduce diagnostic ability"]
}

💰 Cost Profile & Resource Tiering

🎯 Cost-Conscious Infrastructure by Design

To align with SaaS business models and operational budgets, each Infrastructure Blueprint encodes a Cost Profile that determines:

  • Resource allocation (CPU, memory, disk)
  • Service plan or SKU selection
  • Pricing tier constraints (e.g., free vs. premium tier)
  • Deployment strategies that balance performance and cost

This ensures that every deployed service matches its expected cost envelope, enabling the Factory to remain scalable and financially predictable.

🧩 Blueprint-Level Cost Profile

costProfile:
  tier: standard
  sku: B1
  autoscaling:
    enabled: true
    maxReplicas: 3
  budgetLimit: $40/month

Each service explicitly defines a tier, and the blueprint ensures it matches expected performance.

💡 Tiering Strategy Examples

Tier Description Typical Use Case
free Minimal resources, no scaling, cold start allowed Sandbox, previews
basic Low fixed capacity, limited IOPS Internal tools
standard Scalable, moderate SLA, mid-tier pricing Default microservices
premium High-availability, larger resources, enterprise SLA Public APIs, AI inference
enterprise Multi-region, DR-ready, dedicated node pools Regulated workloads, compliance

📦 Cost-Linked Provisioning Drivers

Platform Mapping Mechanism
Azure App Service Plan SKU, Function Tier, DB performance
Kubernetes Resource limits, node pool labels, HPA
Pulumi/Bicep Cost tags, SKU types, subscription scoping

The agent ensures that tier metadata propagates through IaC and appears in dashboards and observability exports.

🧠 Agent Validation and Optimization

The Infrastructure Engineer Agent applies:

  • Cost-linting: Warnings when projected monthly cost > defined budget
  • SKU compatibility checks: Ensures proper SKUs for selected regions
  • Scaling simulation: Predicts burst impact and budget overflows

Example:

{
  "projectedCost": "$36.92",
  "compliant": true,
  "recommendation": "Enable CPU-based HPA for better burst tolerance"
}

📈 Cost Reporting Integration

Cost metadata is integrated with:

  • Azure Cost Management + tagging
  • Grafana dashboards (estimated cost per service)
  • Developer sandbox constraints
  • Billing pipelines for usage attribution

🔐 Secret Management & KeyVault Mapping

🗝️ Purpose-Driven Secret Handling

Every service in the Factory must manage secrets securely and predictably — never hardcoded, never duplicated.

The Infrastructure Blueprint defines:

  • Where secrets come from (source of truth)
  • How secrets are injected (runtime strategy)
  • How agents trace, validate, and rotate secrets

Secrets are not just config values — they’re classified, permissioned, and mapped across environments using formalized patterns.

📦 Secret Declaration Schema

secrets:
  - name: "DatabaseConnectionString"
    source: "AzureKeyVault"
    key: "db-conn-prod"
    injection: "env"
    required: true
    scope: "prod"
  - name: "StripeApiKey"
    source: "AzureKeyVault"
    key: "stripe-live-key"
    injection: "mount"
    rotationPolicy: "30d"

Each entry is validated and embedded into CI/CD, Kubernetes, or Function deployment via secure links.

🧠 Secret Handling by Agents

Agent Role
Infrastructure Engineer Validates secret injection paths, formats, and permissions
DevOps Agent Injects secret references into pipelines and runtime configuration
Security Agent Checks for leaks, validates scopes, enforces policy compliance

🧩 Injection Methods

Method Description Target
env Exposes secret as environment variable App Services, Functions
mount Mounts secret as volume (e.g., secret file) Kubernetes containers
arg Passes secret as CLI flag at startup Jobs, ephemeral containers

🔒 Integration with Azure Key Vault

All blueprints support:

  • KeyVault reference linking by name and secret key
  • Automatic ARM permissions assignment (via accessPolicies)
  • Rotation metadata and expiration alerts
  • Conditional fallback (e.g., dev secrets from .env.local)

Example:

keyVault:
  name: "cs-factory-kv"
  accessPolicies:
    - principalId: "<managed-identity-guid>"
      permissions:
        secrets: [get, list]

🧪 Secret Validation and Tracing

Each deployment run triggers:

  • Existence check in the secret store
  • Scope validation (env and service matching)
  • Expiry and rotation checks
  • Cross-agent linkage (e.g., used in observability exporters or 3rd-party APIs)

Trace view example:

{
  "secret": "StripeApiKey",
  "usedBy": ["payments-api", "web-frontend"],
  "injectedAs": "env",
  "rotationDueIn": "12 days"
}

📡 Messaging & Event Bus Resources

🧬 Event-Driven Architecture as First-Class Infra

In the Factory, messaging infrastructure is declaratively provisioned and trace-linked. Every service that consumes or publishes events gets its own:

  • Topic or queue definitions
  • Subscription configurations
  • Event filtering and routing
  • Security scopes and retention policies

Messaging is not a manual afterthought — it’s modeled into the blueprint and connected to the semantic domain.

🧩 Event Infrastructure Block

messaging:
  bus: "azure-service-bus"
  topics:
    - name: "UserCreated"
      access: "publish"
      retention: "7d"
    - name: "EmailQueued"
      access: "subscribe"
      filter: "recipientType = 'external'"
  queues:
    - name: "dead-letter"
      purpose: "Failed message archive"
      maxDeliveryCount: 5
      ttl: "14d"

Supports both topics and queues, with filters, DLQs, and TTL settings.

🧠 Agent-Managed Messaging Definitions

Agent Role
Infrastructure Engineer Emits queue/topic infra specs and links to domain events
Microservice Generator Agent Registers input/output event types and usage patterns
Security Agent Applies RBAC on bus access, validates namespace-level policies

⚙️ Supported Buses

Type Usage Context
Azure Service Bus Reliable pub-sub, event contracts
Azure Event Grid Lightweight system-wide notifications
Azure Storage Queue Simple FIFO buffer for background jobs
RabbitMQ (optional) For private deployments or hybrid

Each blueprint declares the target platform, and agents render the infrastructure accordingly.

🔒 Messaging Security Profiles

Every bus connection includes:

  • Managed identity or shared access signature (SAS)
  • Scope limitation (topic/queue + permission granularity)
  • Forwarding rules, poison message handling, and encryption at rest

Example:

security:
  messaging:
    role: "Publisher"
    policy: "WriteToUserCreated"
    principal: "payments-api-msi"

🔁 Message Replay & Observability

Blueprints optionally configure:

  • Message archiving to blob or Cosmos DB
  • Replay configuration (via diagnostic settings)
  • Trace tagging per message (for OpenTelemetry propagation)
observability:
  messaging:
    enableTracing: true
    traceContextPropagation: "W3C"
    archiveTo: "blob://cs-bus-archive/user-events"

🗄️ Storage & Database Bindings

🧠 Declarative Persistence for Microservices

Microservices often require databases, blob storage, or distributed caches — but provisioning is frequently inconsistent, insecure, or untracked.

In the ConnectSoft Factory, data-layer resources are explicitly declared in the Infrastructure Blueprint, allowing:

  • Schema-aware provisioning
  • Secure injection of connection details
  • Policy-enforced performance and backup guarantees
  • Linked traceability to domain aggregates and usage agents

📦 Storage Declaration Schema

storage:
  databases:
    - name: "UserDb"
      engine: "PostgreSQL"
      tier: "standard"
      sku: "B2"
      backupRetention: "7d"
      secrets:
        - name: "DbConnectionString"
          source: "AzureKeyVault"
  blobStores:
    - name: "UserMedia"
      tier: "hot"
      container: "media"
      publicAccess: false
      lifecycleRules:
        - deleteAfter: "30d"
  cache:
    - name: "UserCache"
      type: "Redis"
      sku: "Basic"
      ttlDefault: "60s"

🧩 Supported Backends

Type Platform Services
Relational Azure SQL, PostgreSQL, MySQL
NoSQL Cosmos DB (SQL, MongoDB, Table APIs)
Blob Azure Blob Storage
Cache Redis, Azure Cache for Redis
File System Azure Files (optional)

🔐 Secrets & Connection Injection

Every declared storage resource links to a secret reference, never exposing raw credentials.

Secrets are retrieved via managed identity and injected via environment variables or configuration volume mounts.

injection:
  method: "env"
  secretRef: "DbConnectionString"

🧠 Agent-Driven Storage Handling

Agent Responsibility
Infrastructure Engineer Provisions backend, enforces config schema and cost tiers
Security Agent Ensures encrypted at rest, access scoped to microservice
Observability Agent Adds performance and usage metrics to dashboards
Microservice Generator Adds connection handling boilerplate and retry policies

🔁 Storage Lifecycle & Observability

Blueprints include optional retention, cost limits, and traceability:

observability:
  storage:
    enableMetrics: true
    linkTo: "grafana://cs-dashboard/storage"
  policy:
    maxCost: "$20/month"
    maxSize: "5GB"

Each resource can also be annotated with business-criticality tags, for downstream impact analysis.

🌐 Ingress, Routing & DNS

🌍 Service Exposure with Observability & Security

In a cloud-native, multi-tenant platform, exposing services requires precision. The Infrastructure Blueprint defines how each service is exposed via:

  • DNS endpoints
  • Ingress paths
  • Custom domains and SSL policies
  • Route-level observability and authentication

Every public-facing surface is declarative, traceable, and consistent with platform security standards.

🔧 Ingress Schema Example

ingress:
  type: "kubernetes"
  host: "api.connectsoft.dev"
  path: "/users"
  tls:
    enabled: true
    certificateRef: "cs-wildcard-cert"
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /
  auth:
    method: "oidc"
    provider: "connectsoft-auth"
    scopes: ["user.read", "user.write"]

🌐 DNS & Domain Configuration

Field Description
host DNS hostname (api.example.com)
path Base path (/users, /admin)
tls TLS termination and certificate reference
customDomain Optional branded domain mapping
provider Ingress controller (e.g., NGINX, Azure Front Door)

🔐 Authentication & Identity Proxy

Ingress can enforce authentication at edge level:

  • OAuth2 / OpenID Connect
  • JWT validation
  • Scope- and claim-based route protection
  • Header injection for identity propagation
auth:
  method: "jwt"
  header: "X-User-Id"
  validation:
    issuer: "https://auth.connectsoft.dev"
    audience: "microservice-users"

🧠 Agent Collaboration for Ingress

Agent Role
Infrastructure Engineer Generates ingress manifests and cert bindings
Security Agent Validates authentication setup and TLS requirements
DevOps Agent Adds DNS records and updates certificate automation pipelines
Observability Agent Hooks ingress metrics and traces

📈 Ingress Observability

Integrated metrics and tracing for:

  • Request rate, latency, error rate per route
  • SSL cert expiry monitoring
  • Auth success/failure telemetry
  • DNS resolution failures and fallback behavior

Example:

observability:
  ingress:
    enableTracing: true
    metricsLabels: ["host", "path", "status"]
    alerts:
      - type: "certificate_expiry"
        thresholdDays: 10

⚙️ CI/CD Hooks & Pipeline Fragments

🔄 Declarative CI/CD Anchors for DevOps Agents

The Infrastructure Blueprint defines what CI/CD steps are required to build, deploy, verify, and promote the infrastructure linked to a service. These hooks are modular, traceable, and automatically included in GitOps or multi-stage pipelines.

Blueprints don’t just define runtime — they emit the DevOps wiring to get you there.

🔧 CI/CD Fragment Schema

cicd:
  triggers:
    - on: "push"
      branches: ["main", "release/*"]
  steps:
    - name: "Provision Infra"
      uses: "connectsoft/actions/bicep-deploy"
      with:
        template: "infra/main.bicep"
        environment: "dev"
    - name: "Run Tests"
      uses: "connectsoft/actions/integration-test"
      with:
        config: "tests/integration.json"
  variables:
    - name: "DEPLOY_ENV"
      value: "dev"

🧠 Agent Responsibilities

Agent Role
CI Pipeline Agent Emits YAML/JSON fragments, step templates, secrets injection
DevOps Agent Composes pipelines, handles cross-service triggers and approvals
Infrastructure Engineer Links templates to resource declarations, enforces step ordering

🧩 Supported CI/CD Platforms

Platform Support Details
Azure DevOps .yaml fragments, templates, multi-stage compatibility
GitHub Actions Reusable workflows, action composition, secret injection
GitLab CI/CD include: fragments, stage mapping
Bitbucket Pipelines Optional adapter layer (under evaluation)

🔁 Blueprint-to-Pipeline Mapping

Blueprint Section Mapped Pipeline Stage
messaging, storage infra-provisioning
observability telemetry-setup
secrets secure-context-injection
routing, ingress expose-service
validationRules post-deploy checks

📜 Example Fragment (Azure DevOps YAML)

stages:
  - stage: DeployInfra
    jobs:
      - job: BicepDeploy
        steps:
          - template: templates/deploy-infra.yml
            parameters:
              environment: "dev"
              serviceName: "user-service"

🔍 Observability of CI/CD Steps

Each step can include:

  • Tracing (stepId, agentId, outputHash)
  • Status telemetry (success/failure with linked cause)
  • Regeneration hints for failed or outdated steps
  • Secure secret reference validation

♻️ Resilience & Recovery Mechanisms

🛡️ Fault Tolerance as First-Class Infrastructure

Modern microservices require built-in resilience, not as an afterthought but as an architectural mandate. The Infrastructure Blueprint allows explicit modeling of failure strategies across infrastructure layers.

Every service includes recovery semantics — so reliability is planned, not patched.

🧩 Resilience Configuration Schema

resilience:
  retryPolicy:
    maxAttempts: 3
    backoff: "exponential"
    initialDelay: "500ms"
  circuitBreaker:
    failureThreshold: 5
    recoveryTimeout: "60s"
    slidingWindow: 10
  timeouts:
    requestTimeout: "30s"
    startupProbeTimeout: "90s"
  probes:
    liveness:
      path: "/health/live"
      interval: "15s"
    readiness:
      path: "/health/ready"
      initialDelay: "10s"

🔧 Supported Recovery Mechanisms

Mechanism Use Case
Retry Policy Transient network or dependency failures
Timeout Config Hung or long-running external calls
Circuit Breaker Protect against cascading failure
Health Probes Automated restarts or traffic isolation
Pod Disruption Budget Safe draining and upgrade resilience
Preemption Resistance Avoid low-priority eviction

🤖 Agent Contributions

Agent Role
Infrastructure Engineer Applies all runtime controls and annotations
Resilience & Chaos Agent Validates coverage and recommends thresholds
Observability Agent Links metrics and alerts for failures and recoveries

📈 Example Output: Kubernetes Annotations

annotations:
  resilience.connectsoft.dev/retry-max: "3"
  resilience.connectsoft.dev/breaker-threshold: "5"
  resilience.connectsoft.dev/timeout: "30s"

And in Azure Bicep:

resource api 'Microsoft.Web/sites@2021-02-01' = {
  name: 'user-service-api'
  properties: {
    siteConfig: {
      alwaysOn: true
      appSettings: [
        {
          name: 'StartupTimeout'
          value: '90'
        }
      ]
    }
  }
}

🔬 Observability Hooks

Each resilience feature emits traceable data:

  • Retry attempts and reasons
  • Circuit breaker open/close events
  • Probe response times
  • Auto-heal and redeploy triggers
observability:
  resilience:
    traceRetries: true
    traceFailures: true
    alertOn: ["consecutiveFailures", "probeTimeout"]

🔐 Security Boundaries & Policies

🛡️ Defense-in-Depth by Design

Every infrastructure element in the ConnectSoft AI Software Factory is security-scoped by default, with clear policy declarations, perimeter boundaries, and access rules encoded in the Infrastructure Blueprint.

Security isn't optional or implicit — it's a versioned, verifiable infrastructure layer.

🧱 Declarative Security Schema

security:
  network:
    ingress:
      allowedSources: ["10.0.0.0/16", "api-gateway"]
    egress:
      allowedDestinations: ["*.connectsoft.dev", "azure.keyvault"]
  identity:
    servicePrincipal: "svc-user-api"
    managedIdentity: true
  secrets:
    vault: "connectsoft-kv"
    accessPolicy:
      - identity: "svc-user-api"
        permissions: ["get", "list"]
  rbac:
    roles:
      - "Reader"
      - "KeyVaultSecretsUser"
    scopes:
      - "subscriptions/abc123/resourceGroups/infra-dev"

🔐 Security Zones

Zone Description
Internal Only Access via private VNet / Service Mesh
Perimeter Exposed endpoints with auth, rate limits
DMZ Auth-proxy + WAF-filtered ingress
Isolated Air-gapped or event-only communication

Each microservice or infra component is tagged with a security zone, influencing:

  • Allowed egress/ingress
  • Routing policies
  • Auth method (OIDC, mTLS, JWT, etc.)

🔑 Identity & Secrets

Type Details
Managed Identity (MSI) Azure resource-attached principal
Service Principal Fine-grained role-based identity with scoped assignments
Secrets via Key Vault Integrated secret resolution and injection
Certificate Rotation Automatic renewal for TLS and mTLS certificates

Blueprints declare which identities are needed and who can access what, when.

🧠 Security Agent Integration

Agent Function
Security Agent Validates blueprint against org-wide security policies
Infrastructure Engineer Enforces zone-based configuration
DevOps Agent Applies RBAC and secret injection in pipelines

🧪 Policy Validation and Drift Detection

Each security section is validated at multiple layers:

  • Static policy evaluation (e.g., Rego/OPA)
  • CI pipeline enforcement
  • Runtime drift monitors (e.g., Azure Defender, KubeBench)
  • Audit logging for access attempts and configuration changes

Example:

validation:
  policy: "denyPublicIngressUnlessApproved"
  driftDetection: true

🔑 Secrets Management & Vault Integration

🔐 Secrets as First-Class Infrastructure

Secrets in the AI Software Factory are never hardcoded or inline. Instead, they are declared, versioned, and securely injected using enterprise-grade vaults like Azure Key Vault, HashiCorp Vault, or other cloud-native solutions.

Secrets are resolved, not stored — always trace-linked and zero-leaked.

🔏 Secrets Declaration Schema

secrets:
  vault: "connectsoft-kv"
  managedIdentity: "svc-user-api"
  values:
    - name: "DB_CONNECTION_STRING"
      path: "secrets/db/connection"
      injectAs: "env"
    - name: "JWT_SIGNING_KEY"
      path: "secrets/jwt/key"
      injectAs: "file"

📦 Supported Vault Providers

Provider Features Used
Azure Key Vault RBAC, versioned secrets, managed identity access
HashiCorp Vault Policies, leases, dynamic secrets, audit logging
AWS Secrets Manager (Pluggable) IAM-based injection, rotation
Local Dev Secrets .env or dotnet user-secrets fallback

🔄 Injection Modes

Mode Use Case
env For application startup dependencies
file For mTLS certificates, PEM, or PFX
arg For CLI tooling with temporary access
mount For sidecar-injected secret volumes

🤖 Agent Responsibilities

Agent Role
Infrastructure Engineer Declares vault path, format, injection targets
Security Agent Validates access controls and encryption standards
DevOps Agent Ensures runtime secret availability in CI/CD context

🚨 Rotation & Expiration Metadata

Secrets are monitored for expiration and rotation policies:

rotationPolicy:
  autoRotate: true
  checkEvery: "24h"
  rotateAfter: "30d"
  alertBefore: "5d"

📈 Observability & Traceability

Secrets in the blueprint include:

  • Trace ID for origin and lineage
  • Injection timestamp audit records
  • Vault read verification (via canary probes)
  • Non-reversible secret redaction in logs and observability outputs

🕸️ Service Mesh & Networking Overlay

🌐 Unified Service-to-Service Communication

Modern distributed systems require secure, observable, and resilient service-to-service communication. The Infrastructure Blueprint optionally enables a Service Mesh Layer (e.g., Istio, Linkerd, or Azure Service Mesh) to inject capabilities such as:

  • Zero-trust communication (mTLS)
  • Fine-grained traffic control
  • Policy-based routing
  • Built-in telemetry and retries

Services gain advanced networking features without changing application code.

🧩 Mesh Configuration Schema

serviceMesh:
  enabled: true
  implementation: "istio"
  mTLS:
    mode: "strict"
    autoInject: true
  trafficPolicy:
    retries:
      attempts: 3
      perTryTimeout: "2s"
    connectionPool:
      maxConnections: 100
  observability:
    enableTracing: true
    exportTo: ["prometheus", "jaeger"]

📦 Mesh Features Available

Feature Description
mTLS All traffic encrypted with identity validation
Retries & Timeouts Centralized failure policies
Circuit Breakers Prevent cascading failures
Canary Releases Percentage-based rollout, traffic shifting
Rate Limiting Protect against abuse and spikes
Tracing & Metrics Auto-captured spans and service graphs
Fault Injection Used for chaos testing and failure simulation

🧠 Agent Collaboration

Agent Role
Infrastructure Engineer Declares mesh usage, annotations, and security modes
Resilience Agent Configures retry, circuit breaking, and failure tolerance
Observability Agent Enables span tracing, metrics collection, and dashboards
Security Agent Validates mTLS and workload identity enforcement

🔧 Kubernetes Integration Example

annotations:
  sidecar.istio.io/inject: "true"
  traffic.sidecar.istio.io/includeInboundPorts: "80,443"
  traffic.sidecar.istio.io/excludeOutboundPorts: "3306"

📈 Mesh Observability Traces

Traces automatically include:

  • Source/Destination workload and namespace
  • Response codes and latency
  • Retry attempts and circuit breaker states
  • Mesh policy application status

Example span metadata:

{
  "source": "user-service",
  "destination": "email-service",
  "protocol": "HTTP",
  "mtls": true,
  "retries": 2,
  "latency_ms": 147
}

🧱 Cluster Profiles & Runtime Targets

🌍 Platform-Aware Deployment Targets

Not all services in the AI Software Factory run on the same compute substrate. The Infrastructure Blueprint defines where and how each service or component should run — aligned with scalability, cost, and platform constraints.

This enables smart orchestration between Kubernetes, Azure Functions, App Services, Containers, or edge compute.

🎯 Cluster Targeting Schema

runtimeTarget:
  clusterProfile: "production-k8s"
  provider: "Azure"
  nodeSelector:
    workload: "stateful"
  affinity:
    requiredDuringScheduling:
      - key: "zone"
        operator: "In"
        values: ["1", "2"]
  tolerations:
    - key: "dedicated"
      value: "infra"
      effect: "NoSchedule"

🔍 Target Types

Target Type Use Case
Kubernetes Cluster General microservices with container needs
Azure App Service Lightweight web or background APIs
Azure Function Event-driven serverless execution
Container Apps Isolated workloads, simple container runs
Edge Runtime IoT or CDN-edge workers

Each service declares its preferred and fallback runtime targets.

🔧 Cluster Profiles

Profiles are declared by platform architects and selected per-service:

clusterProfiles:
  production-k8s:
    region: "eastus"
    nodePool: "gp-spot"
    costClass: "low"
    autoscale:
      enabled: true
      minPods: 2
      maxPods: 10
  internal-dev:
    region: "centralus"
    debugMode: true
    observabilityLevel: "detailed"

🧠 Agent-Driven Deployment Logic

Agent Role
Infrastructure Architect Defines cluster profiles and global deployment logic
DevOps Agent Uses profile to deploy using CLI/Bicep/YAML
Security Agent Tags services by profile to apply compliance rules
Cost Optimization Agent Monitors usage per profile and suggests changes

📈 Observability & Drift Validation

Each deployment target is continuously:

  • Monitored for usage, saturation, and quota
  • Evaluated against service's blueprint target
  • Annotated for trace-based diagnostics and cost mapping

Example output:

{
  "service": "file-storage-service",
  "target": "container-apps",
  "profile": "production-k8s",
  "status": "Running",
  "deviation": false
}

⚙️ Deployment Dependencies & Orchestration Order

🧬 Multi-Component Dependency Awareness

In complex systems, certain infrastructure components must be provisioned and stabilized before others. The Infrastructure Blueprint defines an explicit, DAG-based orchestration graph that describes:

  • Startup sequencing
  • Service-to-infra dependency chains
  • Parallelization opportunities
  • Blocking conditions

This prevents common bootstrapping issues such as missing databases, unavailable secrets, or unready messaging backbones.

📋 Declarative Dependency Model

dependencies:
  - name: "redis"
    dependsOn: []
    tier: "core"
  - name: "auth-service"
    dependsOn: ["redis", "postgres"]
    tier: "infra-service"
  - name: "api-gateway"
    dependsOn: ["auth-service"]
    tier: "entrypoint"

Dependencies can be annotated with:

  • retryPolicy
  • timeoutSeconds
  • healthProbePath

🧭 Supported Tiers & Types

Tier Typical Components
core Redis, RabbitMQ, Secrets Vault
infra-service Auth, Identity, Monitoring Agents
internal-api Internal services and orchestrators
edge-facing Gateways, frontends, public APIs

🛠️ Orchestration Engine Usage

Dependency graphs are emitted for:

  • Terraform or Pulumi orchestration logic
  • Azure DevOps and GitHub Actions pipelines
  • Helm hooks or Kustomize overlays

🧠 Agent Coordination

Agent Role
Infrastructure Engineer Emits service graph and tier annotations
DevOps Agent Generates pipeline jobs based on dependency tiers
Observability Agent Traces boot order and readiness metrics

📈 Observability Annotations

Each dependency in the DAG supports trace-linked metadata:

trace:
  serviceId: "auth-service"
  parentId: "redis"
  bootLatency: "3.4s"
  state: "healthy"

These are used to:

  • Detect cyclic dependencies
  • Visualize cold-start chains
  • Drive self-healing orchestration logic

🧪 Environment Variables & Runtime Config

🔧 Configuration as First-Class Infrastructure

Most services require runtime configuration: environment variables, connection strings, feature flags, and secrets. The Infrastructure Blueprint explicitly defines these configurations, linking them to:

  • Deployment targets
  • Secret stores (e.g., Azure Key Vault)
  • Feature toggles
  • Agent-generated runtime values

This ensures that all runtime context is declarative, diffable, and validated before deployment.

🧾 Environment Variables Schema

runtimeConfig:
  environment:
    ASPNETCORE_ENVIRONMENT: "Production"
    FEATURE_CACHE_ENABLED: "true"
    SERVICE_TIMEOUT_SECONDS: "10"
    LOG_LEVEL: "Information"

Values may be:

  • Static ("true")
  • Derived (${{ secrets:RedisConnectionString }})
  • Agent-resolved (@context.vision.maxDuration)

🔐 Secrets & Key Vault References

Sensitive variables are never embedded directly in the blueprint output. Instead, they reference secure stores:

secrets:
  - name: "RedisConnectionString"
    source: "AzureKeyVault"
    key: "kv-app-redis-conn"
    required: true

This enables:

  • Safe propagation across CI/CD
  • Centralized auditing
  • Dynamic regeneration with rotations

🧠 Agent Contribution & Validation

Agent Role
Infrastructure Engineer Declares standard config structure
Security Agent Validates secret references and masking policies
DevOps Agent Maps values to pipeline/environment injection formats
Test Generator Agent Reads config values to simulate production context

🌍 Per-Environment Overlays

overlays:
  production:
    LOG_LEVEL: "Warning"
    SERVICE_TIMEOUT_SECONDS: "6"
  staging:
    LOG_LEVEL: "Debug"
    FEATURE_CACHE_ENABLED: "false"

Overrides are applied automatically based on deployment scope or pipeline stage.

📈 Observability Tags & Context

Each runtime value is tagged for traceability:

{
  "configKey": "SERVICE_TIMEOUT_SECONDS",
  "origin": "product-blueprint.feature.duration",
  "appliedProfile": "production",
  "injectedBy": "infra-engineer-agent"
}

⚡ Agent-Based Provisioning Triggers

🧠 Infrastructure That Reacts to Context

In the ConnectSoft AI Software Factory, infrastructure is not provisioned statically. Instead, it reacts to:

  • Signals from upstream blueprints (e.g., Microservice, Security, Product)
  • Dynamic agent findings and constraints
  • Target environment and lifecycle stage
  • Human-injected tags or system events

This enables agent-driven, reactive, and context-aware provisioning with full traceability and regeneration capabilities.

🔁 Trigger Types

Trigger Type Description Example Use Case
onBlueprintChange React to changes in the service/module blueprint Add Redis when caching is enabled
onEnvironmentSwitch Environment-specific behaviors and overlays Inject prod-only Key Vault references
onSecurityFinding Triggered by detected vulnerabilities or misconfigurations Apply stricter firewall rules
onDriftDetected Infra drift observed during CI/CD validation Auto-correct Helm/Kustomize differences
onManualTag Explicit blueprint or DevOps tag "requires-messaging": true triggers bus provisioning

🧬 Declarative Provisioning Triggers (Example)

provisioningTriggers:
  - type: onBlueprintChange
    condition: "featureFlags.includes('CACHE')"
    action: "provision-redis"
  - type: onEnvironmentSwitch
    environment: "production"
    action: "inject-secrets:azure-key-vault"
  - type: onManualTag
    tag: "requires-bus"
    action: "provision-service-bus"

🤖 Responsible Agents

Agent Role & Contribution
Infrastructure Architect Defines trigger schema and links to IaC blocks
Infrastructure Engineer Emits actual templates based on resolved triggers
DevOps Agent Adjusts pipeline steps and GitOps logic
Security Agent Injects secret scan or runtime patching triggers

📈 Observability and Trace Model

All triggers are tracked through trace tags and deployment logs:

{
  "trigger": "onBlueprintChange",
  "source": "microservice-blueprint",
  "action": "add-redis-pvc",
  "executedBy": "infrastructure-engineer-agent",
  "timestamp": "2025-06-09T08:10:00Z",
  "traceId": "trace-redis-injection-994b"
}

These traces are stored in the memory graph, indexed for future agent evaluations and observability dashboards.

🔍 Observability-First Validation Matrix

📡 Infrastructure with Built-In Observability Contracts

In the ConnectSoft Factory, infrastructure must be observable by design, not as an afterthought. The Infrastructure Blueprint includes validation matrices that:

  • Specify required metrics, logs, and traces for each component
  • Define expected health probes, liveness/readiness checks
  • Enforce telemetry exposure through standard interfaces (e.g., OTEL, Prometheus)
  • Allow agents to test observability coverage before deployment

This ensures all infrastructure components are visible, measurable, and debuggable from day one.

✅ Matrix Structure

observabilityMatrix:
  - component: "auth-service"
    metrics:
      - name: "auth_requests_total"
        type: "counter"
        required: true
    logs:
      - stream: "stdout"
        format: "json"
        required: true
    traces:
      - spanName: "ValidateToken"
        required: true
    probes:
      - path: "/health"
        type: "liveness"
        interval: 10
        timeout: 3

Each section may be validated against:

  • Agent-scanned Dockerfile
  • Health probe definitions
  • OTEL injection policies
  • Sidecar container configuration

🧠 Validated By Agents

Agent Responsibility
Observability Agent Validates telemetry, probes, and coverage
Test Generator Agent Injects test traffic to simulate trace/log flow
Infra Engineer Agent Configures OTEL/Prometheus/FluentBit sidecars
DevOps Agent Ensures all observability data is piped to backend

📊 Coverage Report Example

{
  "component": "auth-service",
  "telemetryCoverage": {
    "metrics": "100%",
    "logs": "100%",
    "traces": "83%",
    "probes": {
      "liveness": "OK",
      "readiness": "MISSING"
    }
  },
  "issues": [
    {
      "type": "trace-missing",
      "span": "ValidateUser",
      "severity": "warning"
    }
  ]
}

These results are shown to the developer/architect and optionally block promotion to production.

🔁 Regeneration Hooks & Change Diffing

🔄 Declarative Evolution of Infrastructure

In the Factory, infrastructure isn’t fixed — it evolves in response to upstream changes. To support this, Infrastructure Blueprints define regeneration hooks and change diffing logic, enabling:

  • Safe, automated updates to IaC and runtime layers
  • GitOps-aware pull requests for regenerated artifacts
  • Agent-visible trace diffs that explain why a regeneration occurred
  • Guardrails to prevent breaking changes during partial regeneration

Every change is traceable, replayable, and recoverable. No more “what changed and why?” mystery.

🔁 Regeneration Trigger Conditions

Regeneration is triggered automatically based on:

  • Changes to upstream blueprints (Product, Microservice, Security)
  • Manual annotations or version bumps
  • Detected configuration drift during deployment
  • Time-based (e.g., weekly scheduled regeneration)

Example config block:

regeneration:
  onBlueprintChange: true
  onDriftDetected: true
  schedule: "0 5 * * 1"  # Weekly on Monday 5 AM UTC
  manualTag: "force-regenerate"

📊 Diffing Model Example

{
  "target": "service-auth",
  "source": "microservice-blueprint",
  "previous": {
    "ingress": false,
    "replicas": 2
  },
  "current": {
    "ingress": true,
    "replicas": 3
  },
  "changeType": "auto",
  "generatedBy": "infrastructure-architect-agent",
  "approvedBy": "devops-agent"
}

🧠 Regeneration Agents & Roles

Agent Role
Infrastructure Architect Defines safe regeneration patterns
Infra Engineer Applies diffs to IaC templates
DevOps Agent Runs validation pipeline for new version
Product Owner Agent Can approve manual regenerations

🧩 Safeguards and Overrides

  • Dry-run by default for destructive changes
  • Regeneration requires approval in protected environments
  • Diff summary attached to pull request as Markdown + JSON
  • Regenerated blueprint includes previousVersionHash for traceability

🚀 GitOps Compatibility & Environment Promotion

🌐 Infrastructure Blueprints as GitOps Artifacts

In the ConnectSoft Factory, infrastructure blueprints are designed to fit seamlessly into GitOps workflows. They produce declarative, version-controlled, and environment-aware IaC outputs that can:

  • Be stored in dedicated Git repositories per environment
  • Drive automated ArgoCD / Flux / Azure Pipelines promotions
  • Embed observability and validation metadata
  • Support multi-stage promotions (dev → staging → production) with clear diff visibility

The blueprint becomes the source of truth for deployable infra, across all environments.

🧬 Promotion Workflow

Example infra-blueprint.yaml excerpt:

gitOps:
  repository: "infra-deployments"
  path: "clusters/dev/auth-service"
  promotionPolicy: "manual"
  environments:
    - name: dev
      autoPromote: true
    - name: staging
      approvalAgent: "product-owner-agent"
    - name: production
      approvalAgent: "security-agent"
      requireSecrets: true

This enables per-environment control while maintaining a single blueprint definition.

🧠 Agents Driving GitOps

Agent Role
DevOps Agent Syncs blueprint changes into GitOps repositories
Infrastructure Engineer Splits artifacts per environment overlays
Security Agent Blocks production promotion until secrets + RBAC validated
Observability Agent Adds health/telemetry checks before promotion

🔁 Promotion Validation Matrix

Environment Promotion Trigger Required Agents Validations
dev Commit to main DevOps Lint, dry-run apply
staging Manual via approval tag Product Owner Feature alignment, resource diff, canary
production Scheduled + manual gate Security + Observability Full compliance, probe verification, SLAs

📈 Observability & Traceability

Each promotion is fully observable with metadata stored in:

  • Trace logs (with promotionId)
  • Memory graph (promotion lineage)
  • Agent prompts (explaining reasoning)

Example:

{
  "environment": "production",
  "trigger": "scheduled",
  "approvedBy": "security-agent",
  "timestamp": "2025-06-09T10:02:00Z",
  "changes": ["Add WAF", "Update ingress to https-only"]
}

🌍 Multi-Environment Overlay Strategy

🧱 Single Blueprint, Multiple Deployments

To support dev/stage/prod isolation without duplicating infrastructure logic, the Factory applies a multi-environment overlay strategy within each Infrastructure Blueprint.

Each environment overlay modifies:

  • Resource scaling, sizing, and quotas
  • Secrets, keys, and connection strings
  • Feature flags and toggles
  • Observability/reporting levels
  • Network access rules

This ensures that infrastructure is consistent by design, environment-specific by overlay.

🔧 Overlay Structure in Blueprint

environments:
  - name: dev
    replicas: 1
    cpuLimit: "250m"
    telemetryLevel: "basic"
    secretsRef: "dev-secrets"
  - name: staging
    replicas: 2
    cpuLimit: "500m"
    telemetryLevel: "enhanced"
    secretsRef: "staging-secrets"
  - name: production
    replicas: 4
    cpuLimit: "1000m"
    telemetryLevel: "full"
    secretsRef: "prod-secrets"

Each section overrides the default base definition when promoted.

🧠 Agents Using Overlays

Agent Overlay Role
Infrastructure Architect Designs overlay schema, supported keys, validation rules
Infra Engineer Agent Resolves overlays into IaC or Helm targets
Security Agent Ensures overlay-specific keys and secrets applied
Observability Agent Enables more verbose telemetry in higher environments

🔁 Rendering Output per Env

Output IaC files are rendered per environment, such as:

clusters/
  dev/
    auth-service.yaml
  staging/
    auth-service.yaml
  production/
    auth-service.yaml

Each file is rendered from the same blueprint, with overlays applied declaratively and traceably.

📋 Traceability of Overrides

Overlay application is logged and diffed, with output like:

{
  "service": "auth-service",
  "env": "production",
  "overrides": {
    "replicas": 4,
    "telemetryLevel": "full"
  },
  "traceId": "deploy-923af",
  "source": "infrastructure-blueprint"
}

🕰️ Immutable History and Rollback Anchors

🔒 Blueprint as a Versioned, Auditable Artifact

In the ConnectSoft AI Software Factory, every Infrastructure Blueprint is immutable once applied. This enables agents, DevOps engineers, and orchestrators to:

  • Track every infrastructure change across services and environments
  • Roll back to a known-good version with full context
  • Generate human-readable diffs and machine-verifiable rollback plans
  • Preserve trust and auditability in GitOps-driven workflows

A blueprint is not just a YAML file — it’s a snapshot in time, with trace-aware metadata.

📦 Version Tags and Anchors

Each blueprint version is anchored with metadata:

version: v3
hash: 9f31a76b9d7a
createdAt: "2025-06-09T11:01:12Z"
generatedBy: "infrastructure-engineer-agent"
originTrace: "microservice-blueprint:auth-service:v5"

Anchors are used to:

  • Reproduce IaC templates
  • Rebuild runtime manifests
  • Re-trigger monitoring configuration
  • Apply secure, validated rollbacks

🔁 Rollback Mechanics

Rollback plans can be generated and applied automatically:

{
  "rollbackTarget": "v2",
  "reason": "new WAF caused ingress loop",
  "approvedBy": "product-owner-agent",
  "rollbackPlan": [
    "revert ingress config",
    "scale down replicas to previous setting",
    "restore secrets version 42"
  ]
}

All rollback actions are trace-stamped and version-hashed.

🧠 Agent Roles in History and Rollbacks

Agent Role
DevOps Agent Issues rollback plans, verifies consistency
Infra Engineer Agent Applies rollback to YAML/Bicep/Pulumi/Helm targets
Observability Agent Confirms post-rollback telemetry restoration
Product Owner Agent Provides override approval for production-level rollbacks

📁 File System Structure for History

blueprints/infrastructure/auth-service/
  ├── infrastructure-blueprint.v1.yaml
  ├── infrastructure-blueprint.v2.yaml
  ├── infrastructure-blueprint.v3.yaml
  └── history.log.json

This allows agents or humans to see the complete lifecycle of infra evolution.

✅ Summary

Property Value
⛓️ Blueprint Identity Immutable, versioned, and diffable
🧠 Rollback Enabled By Agent logic + version anchors
🔍 Traceability Each version includes traceId and originPrompt
📦 History Persistence Markdown + JSON + Git-anchored snapshots