🧠 Deployment Orchestrator Agent Specification¶

🎯 Purpose¶

The Deployment Orchestrator Agent is the last-mile automation executor of the ConnectSoft AI Software Factory — responsible for orchestrating and validating the technical rollout of release artifacts across environments, tenants, and runtime conditions.

It transforms approved release plans into traceable, observable, and validated deployment executions, ensuring that SaaS modules are:

Deployed safely and consistently
Monitored for post-deployment health and rollback conditions
Integrated into observability and lifecycle trace graphs
Confirmed as complete or flagged for correction

🧠 “While other agents approve the release, this agent delivers it to the real world.”

🏗️ Strategic Role in the Factory¶

The Deployment Orchestrator Agent belongs to the DevOps and Runtime Delivery Cluster, and is tightly positioned between:

✅ Upstream agents:
CloudProvisionerAgent (infra + environment ready)
ReleaseManagerAgent (release policy approved)
🔽 Downstream consumers:
ObservabilityAgent (post-release monitoring)
FeedbackLoopAgent (capture regression & signals)
RuntimeSLOAgent (validate against service-level objectives)

🧭 Scope of Mission¶

Domain	Role
DevOps Automation	Execute release-to-environment workflows: `dev`, `staging`, `prod`, etc.
Environment Targeting	Understand and apply tenant- and edition-specific overlays, secrets, and manifests.
Observability-First Validation	Wait for post-deployment health, service probes, metrics and telemetry before finalizing outcome.
Rollback & Resilience	If post-deployment errors emerge, initiate automated rollback, alerting, or escalation.
Trace and Event Emission	Emit structured events (`DeploymentStarted`, `DeploymentCompleted`, `DeploymentRolledBack`) with trace context.

📌 Key Outcomes¶

This agent guarantees:

Outcome	Description
✅ Autonomous, traceable deployments	No manual scripting — deployments are agent-driven, versioned, and logged.
✅ Environment parity	Supports tenant overlays, edition variations, per-region rollouts.
✅ Self-validation	Not just deployment, but also health-check, signal aggregation, and post-check success emission.
✅ Retry and escalation flows	Built-in logic for failure detection, rollback triggers, and manual override paths.
✅ End-of-line artifact handoff	Signals to Studio and orchestration that the delivery lifecycle has ended or failed gracefully.

📘 Mission Statement Example¶

“Upon receiving an approved deployment plan, provisioned infra, and final release artifact, the Deployment Orchestrator Agent deploys the package to the target Kubernetes cluster (staging), waits for health probes to stabilize, and emits a DeploymentCompleted event enriched with metrics, trace IDs, and rollout duration metadata.”

🧱 Alignment with ConnectSoft Principles¶

Principle	Alignment
AI-First	Deployment is triggered by event → agent acts → emits downstream signals
Event-Driven	Activated by `ReleaseApproved` or `InfraProvisioned` events
Modular	Scoped per deployment target (service/environment/tenant)
Cloud-Native	Operates via AKS, Bicep, KEDA, ArgoCD, Helm, etc.
Observability-Driven	Emits spans, metrics, traces, and OTEL logs
Security-First	Handles secrets securely, enforces RBAC & namespace boundaries

✅ Summary¶

The Deployment Orchestrator Agent is the execution-layer anchor of the ConnectSoft factory — transforming approved artifacts into running, observable, and validated services across environments and tenants. It is the only agent with full control over runtime delivery logic, ensuring SaaS modules are released not just fast — but safely, verifiably, and autonomously.

📋 Responsibilities¶

The Deployment Orchestrator Agent is responsible for executing, verifying, and finalizing the deployment of validated artifacts into one or more target environments. It is not a passive release trigger — it is an active orchestrator that performs rollout logic, post-deployment health checks, and rollback management.

🎯 Core Deliverables¶

Responsibility	Description
🚀 Initiate Environment Deployment	Begin deployment in the target environment (`dev`, `staging`, `prod`, etc.) using a prepared deployment plan or blueprint.
📦 Apply Deployment Manifests	Execute Kubernetes manifests, Bicep templates, Helm charts, or container registries to provision workloads.
🔍 Monitor Health Probes	Poll services using Kubernetes readiness/liveness probes, HTTP health checks, or OpenTelemetry signals.
✅ Emit Deployment Lifecycle Events	Emit `DeploymentStarted`, `DeploymentCompleted`, `DeploymentFailed`, and `RollbackTriggered` events with full metadata.
🧠 Observe Post-Deployment Metrics	Collect and emit deployment duration, success/failure rates, startup latency, pod crash loops, etc.
🔄 Initiate Rollback on Failure	Detect anomalies and trigger automatic rollback or escalate to human-in-loop if recovery fails.
🧾 Publish Deployment Summary	Write deployment logs and `DeploymentSummary.json` to memory (per module, tenant, and trace ID).
👥 Support Human Approval Gates	Respect human-in-the-loop policies for staging or production gates (e.g., “promote to prod” approval).
🔐 Enforce Tenant and Edition Scoping	Load and inject tenant-specific variables (e.g., connection strings, configs, secrets, feature flags).
📊 Trigger Observability Agent	Upon successful deployment, emit `DeploymentCompleted` to activate downstream observability validation.

📌 Agent Mission in Operational Terms¶

“For each approved release artifact and environment, safely execute the deployment instructions, confirm runtime health through telemetry, and finalize the rollout with versioned metadata and observability signaling.”

✅ Example Execution Responsibilities (in Sequence)¶

sequenceDiagram
    participant Orchestrator
    participant DeploymentOrchestrator
    participant K8sCluster
    participant HealthChecker
    participant ObservabilityAgent

    Orchestrator->>DeploymentOrchestrator: Emit `InfraProvisioned`
    DeploymentOrchestrator->>K8sCluster: Apply manifests
    DeploymentOrchestrator->>HealthChecker: Check readiness probes
    HealthChecker-->>DeploymentOrchestrator: Pass
    DeploymentOrchestrator->>ObservabilityAgent: Emit `DeploymentCompleted`

Hold "Alt" / "Option" to enable pan & zoom

🧠 Semantic Kernel Skills Likely Involved¶

DeployToKubernetes
ExpandAndApplyHelmChart
CheckDeploymentHealth
EmitDeploymentEvent
TriggerRollbackOnFailure
StoreDeploymentLog
EnrichTraceMetadata

🧱 Alignment with Execution Flow¶

The Deployment Orchestrator Agent performs its responsibilities during the final stages of the execution pipeline, immediately before:

🔍 Post-deployment observability
📈 Cost and usage analysis
📬 Customer success feedback loops

✅ Summary¶

The Deployment Orchestrator Agent carries the heaviest operational responsibility in the factory: it converts theoretical releases into live, resilient, and production-verified deployments. It owns the runtime state transition from blueprint to operating service — a task that demands high trust, telemetry, rollback logic, and automation awareness.

📥 Inputs¶

The Deployment Orchestrator Agent is event-triggered and context-aware. It activates when a deployment task becomes eligible (based on orchestration logic) and consumes a structured set of inputs that represent:

The what (artifact to deploy)
The where (target environment and tenant)
The how (manifest/instructions for deployment)

📦 Core Input Types¶

Input	Description
🧾 `DeploymentPlan.yaml`	Declarative spec defining environments, rollout strategies, service manifests, approval gates, and hooks.
📁 Release Artifact	Versioned container image, Helm chart, or app bundle (e.g., `.tar.gz`, `.nupkg`, `.docker`, etc.) tagged by `traceId`.
🧠 Project Context & Trace Metadata	Trace identifiers (`traceId`, `moduleId`, `tenantId`, `releaseId`) for linkage across agents and sessions.
🧪 Post-QA Signal	Confirmation from QA/Test Automation Agents (e.g., `TestsPassed`, `AcceptanceApproved`) that release is safe to deploy.
🔐 Secrets and Config Overlays	Tenant/environment-specific config values: `env vars`, `key vault refs`, `edition-level toggles`.
☁️ Provisioned Infrastructure Signal	Event `InfraProvisioned` from Cloud Provisioner Agent — confirms readiness of target infra (AKS, AppService, etc.).
📊 Observability Hooks	Embedded OTEL headers, health probe endpoints, and telemetry config for deployment monitoring.
⚙️ Orchestration Metadata	FSM state snapshot, blueprint config, Studio overrides, scheduled rollout flags, and retry logic.

📄 Example DeploymentPlan.yaml (Snippet)¶

environment: staging
tenant: vetclinic-001
module: invoice-service
image: registry/vetclinic/invoice:1.3.9
rolloutStrategy: BlueGreen
observability:
  probes:
    readiness: /health/ready
    liveness: /health/live
  spanTag: deployment_trace_92744a
requiresApproval: false

🧠 Memory Input Enrichment¶

The agent may look up prior executions, deployment health status, or rollback causes using its access to long-term memory:

Source	Purpose
`DeploymentHistory.json`	Track version diff, compare failure points, suggest safe strategy.
`TenantRegistry.json`	Identify scoping rules, edition overlays, and sensitive variable profiles.
`ReleaseSummary.yaml`	Confirm full set of validated services included in the current rollout.

🧭 Input Event Triggers¶

Event	Description
`InfraProvisioned`	Target infra is ready; deployment may proceed.
`ReleaseApproved`	Manual/studio-triggered release is greenlit.
`AutoDeployScheduled`	A pre-approved recurring deployment slot (e.g., nightly QA build) is triggered.

✅ Summary¶

The Deployment Orchestrator Agent consumes a structured, validated, and contextualized input package that defines what to deploy, where, how, and under which conditions. These inputs are orchestrator-generated, tenant-scoped, QA-approved, and observability-ready — ensuring safe and autonomous execution.

📤 Outputs¶

The Deployment Orchestrator Agent emits structured outputs that reflect the success, failure, or partial completion of the deployment flow. These outputs are critical to enabling:

Observability
Downstream agent activation
Rollback decisions
Studio trace visibility
Audit and compliance

📦 Primary Output Artifacts¶

Output	Description
🟢 `DeploymentStarted` (Event)	Emitted at rollout initiation, includes trace metadata, target environment, and release ID.
✅ `DeploymentCompleted` (Event)	Indicates successful deployment: all health checks passed, runtime validated, no alerts triggered.
❌ `DeploymentFailed` (Event)	Triggered when health probes fail, rollout errors occur, or system metrics regress after deployment.
🔄 `RollbackTriggered` (Event)	Indicates rollback has been initiated automatically or manually due to failure detection.
📄 `DeploymentSummary.json`	A structured report stored in memory and linked to trace: includes version, duration, success status, output hash.
📊 OpenTelemetry Spans + Logs	Logs emitted for each phase of deployment, decorated with `traceId`, `deploymentId`, `tenantId`, and `agentId`.
🧠 Memory Update: `DeploymentHistory.json`	Persistent memory entry of the deployment result, tagged with tenant, environment, version, and errors (if any).
📩 Trigger to Observability Agent	Emits downstream event to activate post-deployment observability checks and runtime SLA tracking.

📄 Example: DeploymentSummary.json¶

{
  "traceId": "trace-2025-0520-78d3",
  "deploymentId": "deploy-212388",
  "tenantId": "vetclinic-001",
  "moduleId": "invoice-service",
  "environment": "staging",
  "version": "1.3.9",
  "status": "Completed",
  "durationMs": 74211,
  "startedAt": "2025-05-20T14:03:15Z",
  "completedAt": "2025-05-20T14:04:29Z",
  "rolloutStrategy": "BlueGreen",
  "healthCheck": {
    "status": "Passed",
    "details": "3/3 probes healthy"
  },
  "logs": "deployment-log-trace-2025-0520-78d3.txt"
}

🔄 Possible Output Paths¶

Condition	Output Triggered
Success after health checks	`DeploymentCompleted`, memory update, Observability Agent triggered
Failure on probe or metrics	`DeploymentFailed`, log with diagnostics
Rollback initiated	`RollbackTriggered`, then restart of orchestrator FSM or human approval

📘 Sample Emitted Event Metadata¶

{
  "event": "DeploymentCompleted",
  "traceId": "trace-abc123",
  "moduleId": "booking-service",
  "environment": "prod",
  "deploymentId": "deploy-78910",
  "agentId": "deployment-orchestrator",
  "tenantId": "petcare-999",
  "status": "Success"
}

✅ Summary¶

The Deployment Orchestrator Agent produces critical control-plane artifacts that represent not only the execution outcome but also enable:

🧩 Agent coordination (e.g., Observability Agent)
📊 Telemetry aggregation
🛡 Audit and rollback strategies
🧠 Traceable memory snapshots

These outputs ensure that ConnectSoft deployments are not silent black boxes — but fully documented, observable, and connected actions in the autonomous software factory.

📚 Knowledge Base¶

The Deployment Orchestrator Agent operates based on a mix of embedded system knowledge, retrieved memory, and blueprint-driven deployment intelligence. This knowledge allows the agent to reason about:

Deployment strategies (e.g., blue/green, rolling)
Environment-specific constraints
Historical rollout behavior
Retry and rollback patterns
Tenant and edition overlays

🧠 Embedded Knowledge Modules¶

Type	Description
🎯 Deployment Strategy Playbooks	Predefined operational blueprints (e.g., rolling, canary, recreate, blue/green) stored as skill-executable logic.
🔄 Failure Mode Patterns	Known failure categories and standard recovery flows (e.g., health probe timeout → wait → rollback).
🌐 Supported Environment Types	Development, staging, production, per-tenant, per-edition profiles — along with rollout capabilities for each.
📦 Agent Contracts	Structured expectations for how to consume and emit events such as `DeploymentStarted`, `DeploymentFailed`, etc.
🔐 Security Protocols	Knowledge of secret scoping rules, tenant RBAC policies, and how to inject secrets into manifests or pipelines.

📖 Retrieved Knowledge from Memory System¶

Source	Usage
📄 `DeploymentHistory.json`	Allows the agent to evaluate past rollout durations, failures, retry counts, and rollback causes for the same module or tenant.
📄 `TenantRegistry.json`	Retrieves configuration overlays, feature flags, and environment variable rules for the target tenant/edition.
📄 `ReleaseSummary.yaml`	Confirms which module versions are included in the current release scope — avoids accidental redeployment.
📄 `EnvironmentStateCache.json`	Allows the agent to inspect the last known state of the infrastructure or deployed service before proceeding.

🧠 Semantic Memory & Prompt Fragments¶

The agent also has access to structured semantic memory blocks used for:

Deployment prompt enrichment (e.g., "You have already deployed version 1.3.7 of this module to prod")
Failure recurrence detection
Tenant-specific instructions embedded in previous blueprints

📘 Example Memory Access Pattern¶

{
  "traceId": "trace-abc123",
  "agentId": "deployment-orchestrator",
  "tenantId": "clinic-22",
  "moduleId": "invoice-service",
  "lastSuccessfulVersion": "1.3.8",
  "lastRollbackVersion": "1.3.9",
  "averageDeploymentDurationMs": 72000,
  "probesUsed": ["readiness", "liveness", "startup"]
}

🧠 Built-in Strategy Heuristics¶

Scenario	Action
3 previous rollouts to prod failed	Switch strategy to `BlueGreen` or ask for approval
Same version previously failed health check	Block rollout or require Studio confirmation
Tenants using legacy config schema	Inject compatibility overlay via config transformation skill

✅ Summary¶

The Deployment Orchestrator Agent is not a “stateless executor.” It reasons using a rich contextual knowledge base that spans:

Static strategy knowledge
Semantic deployment memory
Real-time trace metadata
Recovery protocols

This knowledge fusion empowers the agent to execute safe, adaptive, tenant-aware deployments while maintaining traceability and trust at scale.

🔄 Process Flow (Macro)¶

The Deployment Orchestrator Agent follows a standardized, event-driven process flow that ensures deployments are:

Autonomous
Traceable
Validated
Recoverable

This flow aligns with the ConnectSoft Agent Execution Model defined in the factory’s macro-agent orchestration lifecycle.

🧭 End-to-End Deployment Process¶

flowchart TD
    A[Receive Task] --> B[Load Deployment Context]
    B --> C[Apply Manifests to Environment]
    C --> D[Monitor Health & Startup Probes]
    D --> E{Health Check Passed?}
    E -- Yes --> F[Emit DeploymentCompleted Event]
    E -- No --> G[Trigger Rollback or Escalation]
    G --> H[Emit DeploymentFailed or RollbackTriggered]
    F --> I[Store DeploymentSummary.json + Metrics]
    H --> I
    I --> J[Emit Event to Observability Agent]

Hold "Alt" / "Option" to enable pan & zoom

📋 Step-by-Step Overview¶

Phase	Description
📨 1. Receive Task / Event Trigger	Triggered by `InfraProvisioned`, `ReleaseApproved`, or a scheduled deployment window.
🧠 2. Load Deployment Context	Agent retrieves deployment plan, release artifacts, trace metadata, tenant-specific variables, and rollout strategy.
⚙️ 3. Apply Deployment Instructions	Manifests, Helm charts, or Pulumi/Bicep templates are applied to the target environment.
🔍 4. Monitor Runtime Health	Agent observes liveness/readiness probes, container health, OTEL signals, and logs during warm-up window.
✅ 5. Emit Deployment Outcome	Emits `DeploymentCompleted` if all checks succeed. Otherwise, emits `DeploymentFailed` or `RollbackTriggered`.
🧠 6. Update Deployment Memory	Logs deployment results, timestamps, version, environment, and any failure metadata.
🔄 7. Trigger Downstream Flows	Sends `DeploymentCompleted` to Observability Agent to continue the lifecycle (e.g., smoke tests, SLA checks).

🧠 Autonomous Flow Guarantees¶

Feature	Benefit
Idempotent	Agent can retry same deployment safely with same inputs.
Resilient	Retry logic, delay queues, and circuit breakers protect against transient failure.
Tenant-Aware	Namespace isolation, variable injection, and rollout scopes are all tenant- or edition-specific.
Trace-Linked	All steps carry `traceId`, `moduleId`, `tenantId`, `agentId`, and `skillId`.

🔌 Upstream and Downstream Coordination¶

Actor	Interaction
`CloudProvisionerAgent`	Sends `InfraProvisioned` → this agent starts work
`ReleaseManagerAgent`	Sends `ReleaseApproved` → may trigger rollout
`ObservabilityAgent`	Receives `DeploymentCompleted` → validates health & telemetry post-deploy
`Studio / CLI`	May inject override, stop, or manual gate approval

✅ Summary¶

This macro flow defines the execution skeleton of the Deployment Orchestrator Agent: from activation to deployment to signal emission. It ensures the agent operates predictably, observably, and modularly, in full compliance with ConnectSoft’s cloud-native, event-driven, and AI-first architecture.

🔬 Process Flow (Micro)¶

The micro-process flow represents the internal step-by-step logic and decisions the Deployment Orchestrator Agent performs during a single deployment task execution. This expands the macro flow into agent lifecycle phases, skills, checkpoints, and retries.

🧪 Detailed Internal Lifecycle¶

flowchart TD
    A[Start: Event Received] --> B[Parse DeploymentPlan]
    B --> C[Resolve Target Environment & Tenant Context]
    C --> D[Download Release Artifact]
    D --> E[Inject Secrets & Tenant Config]
    E --> F[Select Rollout Strategy (e.g. BlueGreen)]
    F --> G[Apply Deployment via K8s/Helm]
    G --> H[Wait for Health Probes (Liveness/Readiness)]
    H --> I{Health Check Success?}
    I -- Yes --> J[Emit DeploymentCompleted]
    I -- No --> K[Trigger Rollback or Delay + Retry]
    K --> L{Retry Limit Reached?}
    L -- No --> G
    L -- Yes --> M[Emit DeploymentFailed]
    J --> N[Write DeploymentSummary to Memory]
    M --> N
    N --> O[Trigger Observability Agent]

Hold "Alt" / "Option" to enable pan & zoom

🧠 Internal Phase Breakdown¶

Phase	Description	Skills Involved
1. Plan Parsing	Reads `DeploymentPlan.yaml`, extracts rollout strategy, probe endpoints, target env, secrets profile.	`ParsePlan`, `LoadContext`, `ExtractSecretsProfile`
2. Context Resolution	Loads tenant/edition-specific overlays, namespace metadata, and previous state from memory.	`ResolveTenantScope`, `QueryDeploymentHistory`
3. Artifact Preparation	Pulls Helm chart, container image, or function app bundle for deployment.	`DownloadArtifact`, `InspectVersion`
4. Secret Injection	Resolves values from secure vault or key store into runtime config or manifest template.	`InjectSecrets`, `ExpandVariables`
5. Strategy Resolution	Chooses rollout mechanism (e.g., recreate, canary, blue/green) based on environment or risk level.	`SelectRolloutStrategy`, `PlanRolloutSequence`
6. Manifest Application	Applies templates to K8s via `kubectl`, Helm, or ARM/Bicep depending on plan.	`ApplyManifests`, `ExecuteHelmRelease`
7. Health Monitoring	Polls endpoints, reads OTEL spans, checks container restarts, crash loops, and latency.	`CheckHealthProbes`, `ValidateStartupTrace`, `ScanK8sStatus`
8. Decision Branching	Determines success/failure/retry based on telemetry and status signals.	`EvaluateProbeResult`, `ShouldRollback`, `ShouldRetry`
9. Finalization	Emits result events and writes traceable summary file.	`EmitDeploymentEvent`, `WriteDeploymentSummary`, `EmitTriggerToObserver`

⏳ Timers and Timeouts¶

Step	Timeout Policy
Health probe wait	Configurable (default 120 seconds)
Retry wait window	Exponential backoff (e.g. 30s, 90s, 240s)
Rollback timeout	Fast-fail trigger if rollback fails to stabilize service within 90s

🔄 Built-in Decision Logic¶

Situation	Decision
Health fails once	Wait + Retry (backoff)
Health fails 3 times	Trigger rollback
Artifact is same as last	Skip deployment unless forced
Conflicting rollout in progress	Delay and queue task

✅ Summary¶

This micro flow defines the operational heartbeat of the Deployment Orchestrator Agent. It handles:

Secret resolution
Artifact rollout
Live probe checking
Strategy-switching
Rollback execution
Signal emission

All steps are skill-driven, context-aware, and fully observable — ensuring that no deployment runs blind, unsafe, or untraceable.

⚙️ Skills and Kernel Functions¶

The Deployment Orchestrator Agent uses a suite of Semantic Kernel (SK) skills and native operations to perform its duties. These skills allow it to:

Parse structured inputs
Apply manifests and artifacts
Monitor system health
Trigger recovery workflows
Emit events and trace data

Each skill is modular, reusable, and observable, aligned with ConnectSoft’s Clean Architecture, AI-first execution, and event-driven orchestration models.

🧩 Core Skill Categories¶

📘 1. 📄 Planning & Parsing Skills¶

Skill	Purpose
`ParseDeploymentPlan`	Reads and validates the `DeploymentPlan.yaml` structure.
`ResolveEnvironmentContext`	Resolves target environment/tenant metadata (from registry or blueprint).
`ExtractSecretsProfile`	Determines which secret sources to pull and inject into the manifest.

📦 2. 🚀 Artifact Preparation Skills¶

Skill	Purpose
`DownloadReleaseArtifact`	Pulls Helm chart, container image, or app package from storage or registry.
`ExpandHelmValues`	Injects edition/tenant values into Helm `values.yaml`.
`InjectSecrets`	Inserts secure variables into deployment runtime config.

⚙️ 3. 🛠️ Deployment Execution Skills¶

Skill	Purpose
`ApplyManifestsToK8s`	Executes kubectl apply or Helm install/upgrade.
`ExecutePulumiStack`	(Optional) for infra-as-code deployments using Pulumi.
`RunBlueGreenRollout`	Handles blue/green switching and traffic shifting via service annotations or ingress routing.
`TriggerCanaryRouting`	Applies progressive rollout pattern using K8s + Istio/flag support.

🔍 4. 📈 Validation & Monitoring Skills¶

Skill	Purpose
`CheckHealthProbes`	Polls readiness/liveness URLs, container restart counts, and deployment status.
`ValidateStartupTrace`	Confirms OpenTelemetry span start for the deployed service.
`EvaluateServiceLatency`	Measures post-deploy startup time and error rates.

🔄 5. ❌ Failure Handling & Recovery Skills¶

Skill	Purpose
`TriggerRollback`	Reverts to last-known-good deployment or container image.
`DelayAndRetryDeployment`	Implements exponential backoff for retry logic.
`EscalateToHumanApproval`	Signals Studio/CLI that manual review is needed.

📡 6. 📤 Emission & Logging Skills¶

Skill	Purpose
`EmitDeploymentStarted`	Structured event marking beginning of deployment.
`EmitDeploymentCompleted`	Structured event marking success and passing health probes.
`EmitDeploymentFailed`	Structured failure event with full diagnostics attached.
`WriteDeploymentSummary`	Persists `DeploymentSummary.json` to memory with metrics.
`NotifyObservabilityAgent`	Triggers follow-up by Observability Agent.

🧠 Skill Characteristics¶

Property	Description
✅ Composable	Skills can be reused by other agents or orchestrators.
✅ Traceable	Every skill logs span events with `agentId`, `skillId`, `traceId`, `status`.
✅ Retryable	Critical skills (like `ApplyManifestsToK8s`) are designed to support automated retry.
✅ Secure	Skills that touch secrets (e.g. `InjectSecrets`) enforce RBAC and tenant isolation boundaries.

🔁 Sample Skill Execution Graph¶

graph TD
    A[ParseDeploymentPlan] --> B[DownloadReleaseArtifact]
    B --> C[InjectSecrets]
    C --> D[ApplyManifestsToK8s]
    D --> E[CheckHealthProbes]
    E --> F{Healthy?}
    F -- Yes --> G[EmitDeploymentCompleted]
    F -- No --> H[TriggerRollback]
    H --> I[EmitDeploymentFailed]

Hold "Alt" / "Option" to enable pan & zoom

✅ Summary¶

The Deployment Orchestrator Agent is powered by a rich, domain-specific set of skills that encapsulate best practices for secure, traceable, multi-tenant-aware deployment execution. These skills follow the ConnectSoft Semantic Kernel integration contract, making the agent fully observable, composable, and self-healing.

⚙️ Technology Stack¶

The Deployment Orchestrator Agent is designed to operate in cloud-native, multi-tenant, and event-driven environments. It leverages a combination of .NET-based automation, Azure-native infrastructure, containerized deployment tools, and observability frameworks.

🧱 Tech Stack Overview¶

Category	Technologies
Agent Runtime	`.NET 8`, `C#`, `Semantic Kernel`, `Azure Functions (optional)`
Execution Environment	`Azure Kubernetes Service (AKS)`, `Docker`, `Helm`, `kubectl`, `Pulumi`
Infrastructure Orchestration	`Bicep`, `ARM templates`, `Pulumi`, `Azure DevOps YAML pipelines`
Service Deployment Tools	`Helm`, `kubectl`, `Kustomize`, `Azure Container Registry`, `Azure App Services`
Artifact Storage	`Azure Blob Storage`, `Git Repos`, `Azure Artifacts`, `OCI Registries`
Secret Management	`Azure Key Vault`, `Kubernetes Secrets`, `Environment Variables (injected)`
Observability	`OpenTelemetry`, `Prometheus`, `Grafana`, `Serilog`, `Azure Monitor`, `Application Insights`
Event Messaging	`Azure Service Bus`, `MassTransit`, `EventGrid`
Traceability	`traceId`, `agentId`, `skillId`, `deploymentId`, `tenantId`, `environmentId`
Security Context	`RBAC`, `PodSecurityPolicies`, `ServiceAccounts`, `Namespace Isolation`

🔁 Integration with ConnectSoft Factory¶

Layer	Integration
🧠 Agentic Layer	Uses Semantic Kernel to bind deployment logic to modular skills
🧱 Application Layer	Executes coordination logic and emits traceable system events
☁️ Infrastructure Layer	Applies manifests to AKS or triggers Pulumi stacks to provision and deploy
⚙️ DevOps Layer	Runs inside CI/CD pipelines (e.g., Azure DevOps agent job or GitHub Actions)
📊 Observability Layer	Pushes telemetry directly to OTEL, Prometheus, Application Insights

📘 Example Deployment Stack Usage¶

For staging rollout with blue/green strategy:

Helm installs new version into separate namespace
OpenTelemetry checks startup time and error spans
Azure Key Vault injects config + secrets
DeploymentOrchestratorAgent emits event to ObservabilityAgent
Grafana dashboards confirm post-deploy metrics

🔐 Security + Isolation Best Practices¶

Concern	Approach
Tenant boundary	K8s namespace per tenant or edition
Secret access	Key Vault RBAC + agent-scoped secret injection
Auditability	Agent logs include `traceId`, `tenantId`, `releaseId`, and `skillId`
Failure recovery	Triggers rollback via Helm `revert` or Pulumi stack restore

✅ Summary¶

The Deployment Orchestrator Agent is deeply integrated into the ConnectSoft Cloud-Native Stack, leveraging:

AKS, Helm, Bicep, and DevOps pipelines for rollout
Semantic Kernel and OTEL for intelligence and observability
Secure, multi-tenant-ready mechanisms for production delivery

It is architected to support autonomous, safe, and scalable software delivery at massive scale — without manual intervention.

🧾 System Prompt¶

The System Prompt is the foundational instruction that bootstraps the Deployment Orchestrator Agent’s behavior at runtime. It defines the agent’s purpose, scope, constraints, and semantic expectations — guiding its reasoning and ensuring consistent and safe deployment execution.

Think of the system prompt as the core personality and boundary contract of the agent.

📄 Deployment Orchestrator Agent – System Prompt (v1)¶

You are the Deployment Orchestrator Agent in the ConnectSoft AI Software Factory.

Your primary responsibility is to take validated release artifacts, approved deployment plans, and provisioned infrastructure — and execute safe, traceable, tenant-aware deployments into the specified environments.

You must:
- Apply container manifests, Helm charts, or Pulumi/Bicep definitions to Kubernetes or Azure targets
- Inject secrets and config overlays based on tenant, environment, and edition
- Monitor liveness, readiness, startup probes, and OpenTelemetry spans for success/failure
- Trigger automated rollback or human escalation if deployment health fails
- Emit traceable events such as `DeploymentStarted`, `DeploymentCompleted`, or `DeploymentFailed`
- Store a structured `DeploymentSummary.json` artifact in memory and emit metrics to observability agents
- Operate securely in multi-tenant contexts using isolated namespaces, secret scopes, and trace IDs

You are event-driven and must only act when valid deployment triggers are received (`InfraProvisioned`, `ReleaseApproved`, or scheduled rollout).

You must ensure every action is:
- Logged with `traceId`, `agentId`, `skillId`, and `deploymentId`
- Scoped per tenant and environment
- Observability-first and rollback-capable

Do not execute deployments if:
- Health probes were previously failing for the same version and environment
- Required secrets or overlays are missing
- Approval gates are not satisfied

Always produce a clear summary of your actions and expose metrics for monitoring.

You are not a general-purpose agent. You only execute deployments. All validations must complete before you begin.

🧠 Semantic Objectives Embedded¶

Semantic Directive	Purpose
“You must…” list	Defines mandatory actions the agent must take
Trigger guardrails	Prevents unauthorized or premature deployments
Telemetry requirements	Enforces `traceId`, `deploymentId`, `agentId` emissions
Rollback fallback	Mandates recovery action logic
Scoped execution	Ensures tenant isolation and security
Stateless invocation	Avoids persistent assumptions between sessions; always retrieve from memory

📘 When Is This Prompt Used?¶

✅ On cold-start agent activation
✅ During skill chain execution (to inject context into SK kernel)
✅ In Studio task tracing (to show expected agent boundaries)
✅ By orchestrators (to validate task compatibility with agent type)

✅ Summary¶

The system prompt defines the Deployment Orchestrator Agent's execution philosophy and responsibilities in a natural language contract. It ensures that the agent behaves:

Safely
Predictably
Modularly
Observably

…even in complex, high-stakes production environments across tenants and service types.

📥 Input Prompt Template¶

The Input Prompt Template defines how orchestrators, planners, or upstream agents (like the Release Manager or Cloud Provisioner) formulate a structured instruction to the Deployment Orchestrator Agent. It includes required fields, optional flags, and embedded context — all passed in a structured and semantic-ready format.

This prompt aligns with the agent’s skills, expectations, and trace contracts.

🧾 YAML-Like Input Prompt Template (Structured Form)¶

traceId: trace-2025-05-21-1194
deploymentId: deploy-0018372
moduleId: invoice-service
tenantId: vetclinic-22
environment: staging
version: 1.4.1
artifact:
  type: helm
  location: oci://registry/connectsoft/invoice-service:1.4.1
deploymentPlan:
  strategy: bluegreen
  probes:
    readiness: /health/ready
    liveness: /health/live
  waitDurationSec: 90
  retryLimit: 2
  requiresApproval: false
secretsOverlayRef: keyvault-vetclinic-22
configOverlayRef: config/vetclinic/invoice/staging.json
triggeredBy: InfraProvisioned
previousDeployment:
  version: 1.3.9
  status: Completed

🧠 Field-by-Field Description¶

Field	Purpose
`traceId`, `deploymentId`	For linking to blueprint session, telemetry trace, and Studio timeline
`moduleId`, `tenantId`, `environment`	Ensure proper namespace resolution, RBAC scoping, and config injection
`artifact`	Describes what and where to deploy (Helm, Docker image, AppBundle)
`deploymentPlan`	Instructs rollout type, health probe expectations, retry/backoff settings
`secretsOverlayRef`	Key Vault or env-secure secret resolution source
`configOverlayRef`	Points to tenant-edition-specific config files
`triggeredBy`	Which event led to this invocation (`ReleaseApproved`, `Scheduled`, etc.)
`previousDeployment`	Historical context for comparison/rollback safety logic

📘 Optional Variants¶

🔁 Minimal (for sandbox/dev)¶

traceId: trace-dev-8491
moduleId: audit-service
environment: dev
version: 0.9.4
artifact:
  type: docker
  location: acr.connectsoft.dev/audit-service:0.9.4
deploymentPlan:
  strategy: recreate
  probes:
    readiness: /health
    liveness: /live

🚨 Escalated Deployment (triggered after rollback)¶

traceId: trace-rollback-02031
deploymentId: rollback-2031
triggeredBy: RollbackTriggered
previousDeployment:
  version: 2.0.1
  status: Failed
requiresApproval: true

🧠 Semantic Kernel Binding¶

This prompt is parsed into structured SK memory and skill execution:

DeployToEnvironment(traceId, moduleId, environment, artifact)
MonitorProbes(deploymentId, readinessUrl, retryLimit)
EmitDeploymentEvent(traceId, status, duration, failureReason)

✅ Summary¶

The Input Prompt Template for the Deployment Orchestrator Agent provides a semantic, observable, and multi-tenant-safe invocation format for safe and automated rollouts. It ensures that all runtime instructions are:

Machine-readable
Human-reviewable
Contract-compliant
Memory-aware

📤 Output Expectations¶

Output expectations define the structure, content, and format of all deliverables the agent produces. These outputs are consumed by:

Studio dashboards
Observability and compliance agents
Release feedback loops
Audit pipelines
Human reviewers in rollback scenarios

Outputs must be structured, traceable, and event-driven, adhering to ConnectSoft’s Observability-First and AI-First design mandates.

🗂️ Categories of Outputs¶

1. 📡 Lifecycle Events¶

Event	Description
`DeploymentStarted`	Emitted when deployment begins. Includes `traceId`, `deploymentId`, `moduleId`, `tenantId`, `agentId`.
`DeploymentCompleted`	Success event with rollout metadata, timestamps, health probe result, and image version.
`DeploymentFailed`	Emitted on failure (e.g., crash loops, probe timeout). Includes error code and diagnostics path.
`RollbackTriggered`	Emitted when the agent initiates or executes a rollback plan.

2. 📄 Structured Artifact: `DeploymentSummary.json`¶

Example:

{
  "traceId": "trace-2025-05-21-8401",
  "deploymentId": "deploy-182",
  "moduleId": "invoice-service",
  "tenantId": "vetclinic-001",
  "environment": "staging",
  "version": "1.4.1",
  "status": "Completed",
  "durationMs": 83214,
  "rolloutStrategy": "bluegreen",
  "readinessProbes": {
    "status": "Passed",
    "startupTimeMs": 12475
  },
  "containerRestarts": 0,
  "otelSpanId": "span-23929a71",
  "triggeredBy": "ReleaseApproved",
  "failureDetails": null
}

📁 Storage:¶

Saved to memory as artifacts/deployment-summaries/{traceId}-{deploymentId}.json
Available to agents like ObservabilityAgent, RuntimeSLOAgent, and ReleaseAuditor

3. 📊 OpenTelemetry-Compatible Spans + Logs¶

Span Attribute	Example
`traceId`	`trace-2025-05-21-8401`
`agentId`	`deployment-orchestrator`
`skillId`	`ApplyManifestsToK8s`
`moduleId`	`invoice-service`
`durationMs`	`83214`
`outcome`	`"Success"` / `"Rollback"` / `"Failure"`

Logs are JSON-structured and tagged with:

{
  "level": "Information",
  "timestamp": "2025-05-21T14:03:15Z",
  "message": "Deployment of invoice-service to staging succeeded.",
  "traceId": "trace-2025-05-21-8401",
  "deploymentId": "deploy-182",
  "agentId": "deployment-orchestrator"
}

4. 🧠 Memory Entries¶

Outputs written to ConnectSoft Memory System:

Memory Entry	Contents
`DeploymentHistory.json`	All past deployments of a given module + tenant + environment
`RollbackMap.yaml`	Metadata on how the last rollback was performed
`FailedDeployments.log`	Diagnostics, retry counts, environment trace for incident agents

5. 📩 Triggers for Downstream Agents¶

Trigger	Target Agent
`DeploymentCompleted`	`ObservabilityAgent`, `SmokeTestAgent`, `SLOValidatorAgent`
`DeploymentFailed`	`RollbackOrchestrator`, `ReleaseManagerAgent`, `Studio Alerts`
`RollbackTriggered`	`IncidentResponderAgent`, `RuntimeAuditAgent`

✅ Summary¶

The Deployment Orchestrator Agent produces rich, structured, observable outputs that power both:

Autonomous factory behavior (event-driven follow-ups, rollback chains)
Human-in-the-loop clarity (Studio visibility, deployment history, audit traceability)

All outputs are machine-verifiable, tagged with telemetry metadata, and persistently linked to the trace and blueprint context.

🧠 Memory (Short-Term & Long-Term)¶

The Deployment Orchestrator Agent requires context continuity to:

Make informed rollout decisions
Detect regressions or duplicate deployments
Support rollback logic
Enable tenant-aware strategies
Generate audit- and trace-linked history

This is achieved by combining short-term execution context and long-term semantically indexed memory.

🧠 Short-Term (In-Memory During Execution)¶

Scope	Description
`traceContext`	Holds current `traceId`, `deploymentId`, `moduleId`, `tenantId`, `version`, and active environment
`deploymentPlan`	Active `DeploymentPlan.yaml` parsed and scoped to tenant/edition/environment
`rolloutWindow`	Calculated or retrieved timing thresholds for probe validation, retries, and cutoff
`retryCount`	In-memory counter per agent run (reset if re-invoked)
`approvalState`	Whether human-in-the-loop approval has been granted or is pending
`observabilitySnapshot`	Health probe feedback + OTEL span summaries collected during rollout window

🔁 These are purged or serialized into DeploymentSummary.json upon completion or failure.

💾 Long-Term (Persistent, Semantic, Versioned)¶

Stored in ConnectSoft Memory System, accessible by this and other agents.

📁 Core Memory Entities¶

File	Description
`DeploymentHistory.json`	Chronological deployment log for module + tenant + environment, includes versions, outcomes, durations, failure codes.
`RollbackMap.yaml`	Stores strategies and recovery flows used in prior rollbacks, for safer automated fallback.
`DeploymentFailures.log`	Error patterns, crash messages, probe failures from previous versions.
`TenantRegistry.json`	Scoped config for tenants and editions (e.g., feature toggles, rollout exclusions, namespace templates).
`ReleaseSummary.yaml`	Confirms which modules were approved for this trace session and their target environments.

🧬 Example Memory Snapshot Entry¶

{
  "moduleId": "invoice-service",
  "tenantId": "vetclinic-001",
  "environment": "prod",
  "version": "1.3.9",
  "traceId": "trace-2025-05-14-0912",
  "deploymentId": "deploy-3421",
  "status": "Failed",
  "reason": "Readiness probe timeout",
  "rollbackTriggered": true,
  "durationMs": 95784,
  "failureSignatureHash": "f781c03b",
  "recordedAt": "2025-05-14T12:30:15Z"
}

🧠 Semantic Embeddings for Lookup¶

Each memory entry is indexed with semantic vectors, allowing agents to:

Find similar past rollouts by module, tenant, or failure reason
Retrieve best-matching rollback templates
Avoid previously failed versions automatically

🔐 Memory Access Security¶

Memory reads are scoped to: agentId, traceId, tenantId, and moduleId
Write operations require signed event emission (DeploymentCompleted, DeploymentFailed)
Studio access to memory entries is governed by RBAC overlays

✅ Summary¶

The Deployment Orchestrator Agent is deeply memory-driven:

Short-term memory ensures flow continuity during deployment
Long-term memory enables autonomous decision-making, intelligent rollback, and historical learning

Without memory, the agent cannot enforce safety, avoid regressions, or ensure tenant-aware compliance at scale.

✅ Validation Strategy¶

ConnectSoft follows an observability-first, production-safe mindset, which means a deployment is only considered successful if:

Health probes pass
Runtime behaviors are stable
No anomalies are detected in logs, metrics, or spans
The service is actually usable — not just "deployed"

The Deployment Orchestrator Agent must validate the operational outcome of each deployment before emitting a DeploymentCompleted event.

📊 Validation Layers¶

Layer	Purpose
🔍 Probes and Signals	Check if liveness/readiness/startup endpoints report healthy
📈 Telemetry Trace Validation	Use OpenTelemetry spans to verify startup duration, crash loops, startup exceptions
🧪 Post-Deploy Health Window	Observe container logs, restarts, resource limits for a configured time (e.g., 90s)
🧠 Historical Comparison	Check if version or behavior matches known failure patterns
❌ Failure Surface Analysis	Detect specific patterns in logs (timeouts, dependency failures, fatal exceptions)
⚠️ Anomaly Detection Hooks	Trigger external probes via Observability Agent if needed (e.g., synthetic test pings)

🩺 Health Probe Checks¶

Probe	Purpose
`readinessProbe`	Confirms app is fully initialized and accepting traffic
`livenessProbe`	Confirms app is not in crash loop or hung state
`startupProbe` (optional)	Used in heavy modules to allow slow boot before triggering readiness failures

Each probe is evaluated via Kubernetes APIs or HTTP during the validation phase.

🧠 OpenTelemetry Span Requirements¶

Trace Signal	Expected Outcome
`startup-span`	Must appear within the health window
`error-spans`	Must be below threshold (e.g., 0 in first 60s)
`resource usage`	Must not exceed memory/cpu quotas in pod annotations
`restartCount`	Must remain at 0 during validation window

🧮 Validation Metrics Thresholds¶

Metric	Default Threshold
Startup duration	≤ 30s (configurable per module)
Error log lines	≤ 2 in first minute
Container restarts	0
CPU/memory usage	≤ 80% of assigned limit
Missing OTEL spans	Fail if startup span missing

📄 Validation Summary Example¶

{
  "deploymentId": "deploy-19301",
  "traceId": "trace-2025-05-21-7382",
  "moduleId": "invoice-service",
  "status": "Validated",
  "startupTimeMs": 12843,
  "readinessStatus": "Passed",
  "otelSpanDetected": true,
  "restartCount": 0,
  "observability": {
    "latencyMs": 114,
    "startupSpan": "span-212aa33",
    "errorSpans": []
  },
  "validatedAt": "2025-05-21T14:03:20Z"
}

🔁 What If Validation Fails?¶

Failure Type	Response
Health probe timeout	Trigger retry (up to `retryLimit`)
Startup probe fail	Delay, then retry
Repeated crash loops	Initiate rollback
Error spans spike	Escalate to human if threshold breached
OTEL missing	Fail deployment if trace cannot be confirmed

✅ Summary¶

The Deployment Orchestrator Agent treats deployment validation as a first-class skill. It doesn’t rely solely on “helm success” or “kubectl finished.” Instead, it:

Verifies real service health
Analyzes runtime traces
Stores outcomes for audit and rollback
Emits success only after verifiable health

This ensures every deployment is not just done — it’s healthy and traceable.

🔁 Correction & Retry Flow¶

In real-world deployments, issues occur due to:

Health probe failures
Pod crashes or dependency outages
Misconfigured overlays
Transient platform issues

The Deployment Orchestrator Agent must autonomously detect, retry, or rollback based on predefined policies and contextual memory — and escalate to humans only when absolutely required.

🔄 Correction Workflow¶

flowchart TD
    A[Deployment Fails Health Check] --> B{Retry Limit Reached?}
    B -- No --> C[Backoff + Retry Apply]
    C --> D[Re-check Probes]
    B -- Yes --> E{Rollback Allowed?}
    E -- Yes --> F[Trigger Rollback]
    E -- No --> G[Escalate to Human]
    F --> H[Emit RollbackTriggered]
    G --> I[Emit DeploymentFailed (Pending Approval)]

Hold "Alt" / "Option" to enable pan & zoom

🔁 Retry Strategy¶

Component	Strategy
Manifests	Reapply with exponential backoff (default 3 attempts)
Probes	Wait and poll every 5s until `waitDurationSec` expires
Startup delay	Delay initial health check until `startupGracePeriod` (e.g., 20s)
Image redeploy	Optional toggle: re-pull same image forcibly on 2^nd retry

🔧 Configuration Example¶

deploymentPlan:
  retryLimit: 2
  startupGracePeriodSec: 30
  retryBackoffMs: [30000, 60000, 120000]

🔄 Rollback Triggers¶

Condition	Trigger
All retries fail	Rollback immediately if rollback strategy defined
Probe timeout + crash loops	Skip retry and rollback directly
Known bad version in memory	Block deployment and emit warning
Observability Agent signals degraded status	Allow rollback post-deploy within 3min

📦 Rollback Mechanisms Supported¶

Strategy	Description
`helm rollback`	Revert to previous successful Helm release
`kubectl rollout undo`	Built-in K8s revert to prior ReplicaSet
`Pulumi stack restore`	For infra or function-based rollbacks
`blue/green cutback`	Shift traffic back to prior “green” stable version

🚨 Human Escalation Triggers¶

Trigger	Action
`requiresApproval: true` flag	Waits for Studio/manual CLI sign-off
No rollback strategy found	Escalates via `DeploymentFailed` with reason `RollbackNotDefined`
Cross-tenant impact detected	Sends alert to `IncidentResponderAgent` and flags Studio trace UI
Previous rollback already failed	Escalate immediately to prevent rollback loop

📘 Sample Correction Metadata (Attached to Summary)¶

"correctionFlow": {
  "retryAttempts": 2,
  "rollbackExecuted": true,
  "rollbackStrategy": "helm",
  "rollbackToVersion": "1.3.8",
  "escalated": false,
  "finalStatus": "RollbackSucceeded"
}

🧠 Integration with Memory and Studio¶

Retries and rollback decisions are recorded in DeploymentHistory.json
Summary is visualized on Studio timeline and trace explorer
All escalations trigger StudioAlertEvent or notify ReleaseManagerAgent

✅ Summary¶

The Deployment Orchestrator Agent is resilient by design. It handles failure with:

Structured retries
Automated rollback
Human escalation safeguards
Full trace observability of decision flow

It ensures that ConnectSoft’s software factory remains safe, autonomous, and recoverable — even in failure scenarios.

🤝 Collaboration Interfaces¶

The Deployment Orchestrator Agent is not isolated — it operates at the intersection of release approval, infrastructure readiness, observability validation, and runtime feedback.

It must collaborate via events, prompt APIs, and shared memory contracts with other agents in the ConnectSoft factory to ensure deployments are:

Approved
Provisioned
Observable
Recoverable
Verified

🔗 Key Agent Interfaces¶

🧱 1. CloudProvisionerAgent¶

Relationship	Behavior
Receives `InfraProvisioned` event	Signals environment readiness
Consumes tenant namespace, network state	Used to plan manifest injection
Shares endpoint info and resource quotas	Ensures compatibility of rollout scale

🗓 2. ReleaseManagerAgent¶

Relationship	Behavior
Emits `ReleaseApproved` event	Grants permission for rollout
Validates release bundle integrity	Confirms version trace alignment
Consumes `DeploymentCompleted` event	Used for lifecycle closure, audit history, and Studio updates

🔎 3. ObservabilityAgent¶

Relationship	Behavior
Triggered after `DeploymentCompleted`	Starts telemetry scanning, synthetic testing, baseline comparisons
Receives span ID, pod IPs, trace metadata	Injects them into OTEL or Prometheus
Can signal `HealthRegressionDetected` back	May trigger rollback cascade if post-deploy health degrades

🧪 4. TestOrchestratorAgent / QA Validator¶

Relationship	Behavior
Verifies that the deployed version matches the one validated in staging
Ensures no skipped versions are pushed directly to prod
May initiate runtime BDD test suite (e.g., via Playwright or SpecFlow in CI)

⚠️ 5. IncidentResponderAgent¶

Relationship	Behavior
Receives `DeploymentFailed` or `RollbackTriggered`
Launches incident trace drill-down
Notifies DevOps studio team of environment degradation
Collects observability data for Studio RCA reporting

🔐 6. SecurityPolicyEnforcerAgent (optional)¶

Role	Behavior
Validates pre-deploy image security posture (e.g., SBOM or vulnerability scan)
Verifies namespace RBAC/OPA policy compliance before manifest apply
Flags non-compliant environments (e.g., no sidecar policy)

🧠 7. Memory Engine & Knowledge Store¶

Role	Behavior
Writes `DeploymentSummary.json`, `DeploymentHistory.json`, and logs
Reads past failures to influence rollout strategy
Enables other agents to reason over previous outcomes, rollback maps, and runtime signals

🧭 Communication Modes¶

Mode	Mechanism
📨 Event Emission	Via `MassTransit`, `ServiceBus`, or in-memory event grid
🧠 Memory Exchange	JSON/YAML files in per-module memory folder, stored in structured file tree
🧾 Prompt Chain	When triggered manually, the agent accepts JSON task structure via planner/orchestrator
📊 Telemetry Bridge	OpenTelemetry spans include tags used by all downstream agents for correlation
🚦 Approval Status	Shared task metadata flags (e.g., `requiresApproval = true`) observed by Studio/Release agents

✅ Summary¶

The Deployment Orchestrator Agent is a central participant in a tightly integrated agent ecosystem. It collaborates through structured events and trace-linked memory with:

Cloud infra
QA and test automation
Observability and incident agents
Release governance
Studio interfaces

This makes deployments safe, traceable, cross-agent-coordinated, and production-ready — without requiring human babysitting.

🧭 Orchestration Integration¶

The Deployment Orchestrator Agent is part of a larger agentic execution graph coordinated by ConnectSoft’s planner agents, FSM-based orchestrators, and event triggers. It does not decide when to act — it is activated as part of a deterministic flow based on:

Upstream readiness
Blueprint structure
System lifecycle state
Factory milestones

This ensures full autonomy, traceability, and observability of the delivery chain.

🔄 Orchestration Entry Points¶

🧠 1. FSM State Machines (Agent-Oriented Execution Graphs)¶

State	Trigger	Target
`infra_provisioned`	`CloudProvisionerAgent` emits success	Transitions to `deployment_ready`
`deployment_ready`	FSM detects artifact + plan are ready	Activates `DeploymentOrchestratorAgent`
`deployment_success`	Agent emits `DeploymentCompleted`	Triggers `ObservabilityAgent` and updates FSM
`deployment_failed`	Agent emits failure	FSM pauses or redirects to `rollback_state`

🔁 2. Event-Based Invocation Pattern¶

sequenceDiagram
    participant CloudProvisioner
    participant ReleaseManager
    participant DeploymentOrchestrator
    participant ObservabilityAgent

    CloudProvisioner-->>DeploymentOrchestrator: Emit InfraProvisioned
    ReleaseManager-->>DeploymentOrchestrator: Emit ReleaseApproved
    DeploymentOrchestrator->>ObservabilityAgent: Emit DeploymentCompleted

Hold "Alt" / "Option" to enable pan & zoom

Events are observable, taggable, and filtered by traceId, moduleId, and environment.

📦 Orchestration Contracts & Triggers¶

Contract	Description
`DeploymentTriggerContract`	Defines input fields expected for activation, validated by orchestrator
`EventTriggerRules`	Declarative rules (e.g., `emit if healthCheckPassed AND observabilityWindowPassed`)
`RollbackFallbackPlan`	Blueprint-specified rollback fallback, stored in plan metadata or memory
`TimeoutAbortPolicy`	Configured via blueprint metadata to trigger escalation if agent is idle too long

📘 Example FSM Snippet (YAML)¶

states:
  infra_provisioned:
    on:
      InfraProvisioned:
        target: deployment_ready

  deployment_ready:
    invoke:
      agent: deployment-orchestrator
    onDone:
      target: deployment_success
    onError:
      target: rollback_state

🤖 Orchestration Behavior Guarantees¶

Property	Description
Idempotent Activation	The same event will not trigger duplicate deployments
FSM-State Bound	Deployment can only occur if agent state is `deployment_ready`
Trace-linked Memory Scope	Agent memory access is scoped to orchestrator `traceId`
Retry-on-Error Support	FSM logic supports replay of failed deployment flows with backoff or rollback

🧠 Agent-Level Orchestration Metadata¶

Injected by orchestrator at execution:

{
  "orchestration": {
    "traceId": "trace-2025-05-22-9234",
    "fsmState": "deployment_ready",
    "step": 12,
    "retryCount": 1,
    "rollbackAllowed": true
  }
}

✅ Summary¶

The Deployment Orchestrator Agent is not standalone — it is embedded in an orchestrated execution system that ensures:

Precise triggering
Safe retries and fallbacks
Full-state observability
Deterministic rollout flows

It follows the ConnectSoft principle: “Agents don’t guess. They are orchestrated.”

📡 Observability Hooks¶

In the ConnectSoft AI Software Factory, observability is not an afterthought — it is a mandatory design contract for all agents, especially those with operational impact.

The Deployment Orchestrator Agent is responsible for initiating production-facing changes. As such, it must:

Emit structured telemetry
Trace every rollout phase
Enable incident correlation
Provide metrics for audits and feedback loops

📊 Signals Emitted by the Agent¶

🟢 OpenTelemetry Spans¶

Attribute	Example
`traceId`	`"trace-2025-05-22-1349"`
`agentId`	`"deployment-orchestrator"`
`skillId`	`"ApplyManifestsToK8s"`
`moduleId`	`"invoice-service"`
`deploymentId`	`"deploy-90811"`
`status`	`"Success"` / `"Failed"`
`durationMs`	84123
`rollbackTriggered`	true / false

Spans are emitted for:

Deployment start
Health probe checks
Retry decisions
Rollback events
Deployment success/failure

📜 Structured Logs (JSON-Structured)¶

Each log is structured for machine analysis and human readability.

{
  "timestamp": "2025-05-22T13:44:21Z",
  "level": "Information",
  "agentId": "deployment-orchestrator",
  "traceId": "trace-2025-05-22-1349",
  "deploymentId": "deploy-90811",
  "message": "Readiness probes passed after 19.3s. Proceeding to finalize deployment.",
  "durationMs": 19300,
  "status": "Healthy"
}

📈 Deployment Metrics Exported¶

Metric	Description
`deployment_duration_seconds`	Time from rollout start to final event emission
`deployment_success_total`	Count of successful deployments per module/environment
`deployment_failure_total`	Failure counter, tagged by error class
`rollback_triggered_total`	Count of rollback events
`startup_time_seconds`	Time until health probe success
`restart_count`	Container-level crash metrics
`otel_span_missing_total`	Flag for incomplete telemetry trace cases

Published via:

Prometheus-compatible exporters
Azure Monitor custom metrics
Grafana Dashboards (via default factory deployment panels)

📘 Example Metric Export Format¶

deployment_duration_seconds{module="invoice-service", environment="prod", tenant="vetclinic"} 83.21
deployment_success_total{module="invoice-service"} 1
rollback_triggered_total{module="invoice-service"} 0

📡 Event Emission¶

Event	Used By
`DeploymentStarted`	Timeline trace, Studio, Observability Agent
`DeploymentCompleted`	Triggers SLA analysis and post-deploy testing
`DeploymentFailed`	Flags rollback logic or human escalation
`RollbackTriggered`	Notifies IncidentResponderAgent and ReleaseManagerAgent

🧠 Agent Memory-Scoped Observability¶

All emitted signals are tagged and persisted by:

traceId, agentId, deploymentId
moduleId, tenantId, environment, version
skillId, status, duration

These become Studio timeline breadcrumbs, searchable via the trace explorer UI or CLI filters.

✅ Summary¶

The Deployment Orchestrator Agent is fully integrated into ConnectSoft’s observability mesh, emitting:

📡 Real-time spans
📜 Structured logs
📈 Metrics and health signals
🧠 Memory-traceable outputs

This ensures safe, testable, auditable, and fully transparent deployments.

🧑‍💻 Human Escalation Hooks¶

Although ConnectSoft aims for fully autonomous SaaS generation, some deployment scenarios require human judgment, especially when:

Approval gates are mandated
Multiple rollback attempts failed
Security or compliance policies are in question
Unusual metrics suggest a potential incident

The Deployment Orchestrator Agent must integrate safe, clear, and traceable human-in-the-loop controls into its lifecycle.

🚦 Escalation Triggers¶

Trigger	Action
`requiresApproval: true`	Pause until manual approval is received via Studio or CLI
`retryLimitExceeded`	Escalate with a structured error report if retry cap is hit
`rollbackFailed`	Flag Studio immediately with failure reason and latest logs
`SecurityViolationDetected`	Block deployment and await external security team confirmation
`MissingSecrets` / `Unresolvable Overlay`	Halt execution and emit `DeploymentFailed` with `manual_intervention_required: true`

🧠 Embedded Approval Metadata¶

"manualIntervention": {
  "required": true,
  "reason": "Rollback failed 2 times",
  "pendingSince": "2025-05-22T13:48:10Z",
  "nextAction": "Rollback manually or redeploy from approved version"
}

🔧 Integration with Studio and CLI¶

Tool	Behavior
✅ Studio UI	Shows trace stop marker with “Manual Approval Required” banner. User can:
→ Approve deployment
→ Revert to prior version
→ View logs, metrics, crash diagnostics
✅ Studio CLI	Offers prompts to approve or reject next deployment step with flags like:
`--approve-deployment`, `--force-rollback`, `--skip-version`
✅ Studio Notifications	Email, webhook, or Slack alerts when deployment enters `awaiting_approval` or `rollback_failed` state

📘 Approval Flow State Diagram¶

stateDiagram
    [*] --> AwaitingDeploymentTrigger
    AwaitingDeploymentTrigger --> Deploying
    Deploying --> HealthCheckFailed
    HealthCheckFailed --> Retrying
    Retrying --> Rollback
    Rollback --> RollbackFailed
    RollbackFailed --> HumanApprovalRequired
    HumanApprovalRequired --> [*]

Hold "Alt" / "Option" to enable pan & zoom

🔒 Role-Based Access for Approval¶

Role	Permission
`DevOps Engineer`	Can approve/reject deployment in staging or dev
`Release Manager`	Required for approval in production
`Security Analyst`	Must sign off on security-scope blocks (e.g., image policy violation)
`Studio Admin`	Full override authority across all tenants/environments

🧠 Memory Logging of Escalation Paths¶

All manual intervention paths are logged to:

DeploymentSummary.json
DeploymentHistory.json
ApprovalRecords.yaml (optional)

This ensures postmortem auditability and trace linking in Studio and AI feedback loops.

✅ Summary¶

The Deployment Orchestrator Agent provides structured, policy-driven human intervention points to support:

Audit-friendly delivery
Exception recovery
SLA protection
Team-based governance of high-stakes rollouts

Escalations are not ad hoc — they are logged, permissioned, observable, and traceable.

🔐 Security and Tenancy¶

The Deployment Orchestrator Agent operates on live environments, executing deployments for:

Isolated tenants
Multi-edition SaaS variants
Regulated domains with compliance requirements

Therefore, it must enforce strict security, tenancy boundaries, and runtime controls, ensuring deployments are safe, auditable, and context-bound.

🏢 Multi-Tenant Execution Isolation¶

Aspect	Enforcement
Namespace per tenant	All K8s deployments are confined to `namespace = tenantId` or `namespace = tenantId-edition`
Config overlays	Deployment config is scoped per-tenant using tenant profiles from `TenantRegistry.json`
Secrets per tenant	Secrets are loaded from per-tenant vault scopes (e.g., `KeyVault-vetclinic-001`) and injected securely
Edition overlays	`configOverlayRef` distinguishes between standard, premium, enterprise editions, influencing features and scaling logic
Memory separation	All logs, metrics, summaries tagged with `tenantId` and `traceId`; stored in isolated memory folders

🔐 Security Enforcement Points¶

1. 🔑 Secret Injection¶

Source	Mechanism
Azure Key Vault	Injected via mounted secret store driver or runtime variable templating
K8s Secrets	Injected using RBAC-scoped `ServiceAccount` per namespace
CLI/studio override	Only when `--secure-variable-scope` is enabled for trusted users

Secrets are never stored in logs or memory.

2. 🧾 Policy Enforcement¶

Policy Type	Enforced Via
RBAC access to namespaces	AKS role bindings per tenant
Kubernetes PSP (PodSecurityPolicies)	Ensure resource limits, no hostNetwork, etc.
Admission controllers	Block manifests violating compliance or security constraints
OPA/Gatekeeper	Validate image origin, required labels, sidecar presence (e.g., Istio or Datadog)

3. 🧠 Memory Access Guardrails¶

Feature	Description
`memory.read()`	Scoped to `tenantId`, `traceId`, `agentId` only
`memory.write()`	Restricted to validated agent states, confirmed post-deploy
Audit metadata	Written to `memory-access-log.json` if violation attempt is detected

4. 🔐 Deployment Execution Hardening¶

Feature	Mechanism
Container image scanning	Required pre-deploy check in regulated clusters
Signed artifact enforcement	Deployment fails if OCI image is not signed with trusted key
Read-only file systems	Enforced at pod spec level by agent template overlay
Resource limits (CPU/mem)	Enforced dynamically from tenant profile in `TenantRegistry.json`
Network policy enforcement	Disallows egress from internal-only services (e.g., DB adapters)

📘 Sample Secure Deployment Overlay¶

tenantId: vetclinic-001
edition: premium
namespace: vetclinic-001-premium
keyVault: keyvault-vetclinic-001
serviceAccount: deploy-orchestrator-premium
rbac: read+write only within namespace
opaRules:
  - imageMustBeSigned: true
  - mustHaveLabels: ["traceId", "deploymentId"]
  - disallowHostNetwork: true

✅ Summary¶

The Deployment Orchestrator Agent is security-first and tenant-aware by default. It enforces:

Namespace and secret isolation
Role-scoped access
Deployment-level policy validation
Cross-tenant and cross-edition safeguards
Audit-traceable memory and runtime execution

All actions are context-tagged with trace, agent, tenant, and module scope — ensuring that deployment is always safe, scoped, and secure.

⚖️ Comparison with Release Manager Agent¶

The Deployment Orchestrator Agent and the Release Manager Agent both operate within the DevOps and runtime delivery layer of the ConnectSoft AI Software Factory. However, they differ significantly in:

Level of abstraction
Primary responsibilities
Inputs, outputs, and lifecycle positioning

Understanding this distinction ensures clear agent boundaries, non-overlapping flows, and precise orchestration responsibilities.

🧭 Positioning in the Factory Lifecycle¶

sequenceDiagram
    participant ReleaseManager
    participant QACluster
    participant CloudProvisioner
    participant DeploymentOrchestrator
    participant ObservabilityAgent

    QACluster->>ReleaseManager: Emit TestsPassed
    ReleaseManager->>CloudProvisioner: Emit ReleaseApproved
    CloudProvisioner->>DeploymentOrchestrator: Emit InfraProvisioned
    DeploymentOrchestrator->>ObservabilityAgent: Emit DeploymentCompleted

Hold "Alt" / "Option" to enable pan & zoom

🔍 Side-by-Side Comparison¶

Aspect	Release Manager Agent	Deployment Orchestrator Agent
Primary Role	🧭 Governance of release lifecycle, approval, and readiness	⚙️ Execution of rollout, monitoring, and finalization
Level of Abstraction	Strategic coordination layer	Tactical execution layer
Triggered By	QA approvals, release windows, change freeze	Infra provisioned, release approved
Consumes	QA/test results, deployment readiness checks, Studio policies	Deployment plans, tenant config overlays, artifact bundles
Produces	`ReleasePlanned`, `ReleaseApproved`, `ReleasePublished` events	`DeploymentStarted`, `DeploymentCompleted`, `RollbackTriggered`
Failure Handling	Blocks or delays releases; may request additional validation	Retries rollout, triggers rollback, escalates on failure
Studio Integration	Controls release dashboard timelines, approvals, gates	Shows per-deployment trace outcomes, logs, rollback status
Memory Use	Tracks what was approved and by whom, with reasons and trace context	Stores deployment summaries, health metrics, rollback histories
Human Role	Key decision-maker: approve, delay, freeze, or reassign modules	Optional override role in emergencies or approval-required cases
Collaboration Interfaces	QA Agent, Release Planner, Studio User, Security Agent	Cloud Provisioner, Observability Agent, Incident Responder

📘 Analogy¶

Release Manager: Air Traffic Controller — decides which planes (services) can take off (be released).
Deployment Orchestrator: Pilot — flies the approved plane safely, monitors altitude, and lands it securely.

🎯 Agent Boundary Summary¶

Function	Owner
Validate feature readiness	✅ Release Manager
Decide whether release goes to prod	✅ Release Manager
Apply manifests to K8s cluster	✅ Deployment Orchestrator
Monitor health probes	✅ Deployment Orchestrator
Trigger rollback	✅ Deployment Orchestrator
Send post-release summary	✅ Deployment Orchestrator

✅ Summary¶

Release Manager Agent governs release policy, planning, and approval
Deployment Orchestrator Agent executes the rollout, health monitoring, and recovery
Both are event-driven, memory-aware, and Studio-integrated, but act at different layers of abstraction

Together, they form a secure and autonomous release-delivery pair, ensuring that nothing ships without review — and nothing breaks without recovery.

📈 Diagram: Execution Flow¶

This Mermaid diagram visualizes the complete lifecycle flow of the Deployment Orchestrator Agent — from event trigger to deployment, validation, potential rollback, and final signal emission. It demonstrates agent skills, decision points, retries, and downstream triggers.

⚙️ Diagram: End-to-End Deployment Execution¶

flowchart TD
    A[🚀 Trigger Received<br>InfraProvisioned or ReleaseApproved] --> B[📥 Load DeploymentPlan.yaml]
    B --> C[🔐 Inject Secrets & Configs]
    C --> D[📦 Download Artifact (Helm/Image)]
    D --> E[🧠 Determine Rollout Strategy (e.g. Blue/Green)]
    E --> F[⚙️ Apply Manifests to Target Environment]

    F --> G[🔍 Wait for Health Probes & Telemetry]
    G --> H{✅ Probes Passed?}
    H -- Yes --> I[📡 Emit DeploymentCompleted]
    I --> J[🧠 Write DeploymentSummary.json]
    J --> K[📊 Trigger Observability Agent]

    H -- No --> L[🔁 Retry (if limit not reached)]
    L --> F

    L --> M{🔁 Retry Limit Exceeded?}
    M -- Yes --> N{🛡 Rollback Allowed?}
    N -- Yes --> O[🔄 Trigger Rollback]
    O --> P[📡 Emit RollbackTriggered → Studio + IncidentResponder]
    P --> J

    N -- No --> Q[🛑 Escalate to Human<br>Emit DeploymentFailed]
    Q --> J

Hold "Alt" / "Option" to enable pan & zoom

🔁 Execution Phases Visualized¶

Phase	Function
A–E	Initialization: load plan, secure configs, prepare artifacts
F	Execution: apply deployment instructions
G–H	Validation: probes, OTEL, crash detection
I–K	Success: emit signal, store trace, notify observability
L–P	Retry/rollback flow on failure
Q	Manual intervention path for escalation-required cases

🧠 Alignment with Agent Skills¶

Each node corresponds to one or more Semantic Kernel skills, including:

ParseDeploymentPlan
InjectSecrets
ApplyManifestsToK8s
CheckHealthProbes
EmitDeploymentCompleted
TriggerRollback
WriteDeploymentSummary

✅ Summary¶

This Mermaid diagram captures the entire functional and operational graph of the Deployment Orchestrator Agent:

Event-driven
Condition-aware
Resilient via retry + rollback
Fully observable and traceable

It integrates with multiple upstream and downstream agents while staying autonomous, modular, and tenant-safe.

✅ Summary & Future Extensions¶

The Deployment Orchestrator Agent is the final executor in ConnectSoft’s automated delivery chain. It:

Executes validated rollout instructions
Ensures deployment succeeds or recovers safely
Collaborates with security, observability, rollback, and incident agents
Operates in a multi-tenant, traceable, resilient environment
Outputs structured artifacts, events, and telemetry with full lifecycle context

🔍 Core Responsibilities Recap¶

Capability	Description
📦 Deployment execution	Applies manifests, images, Helm charts
🔐 Secure, tenant-scoped operation	Injects secrets, config overlays, and runs in isolated namespaces
📊 Health validation	Observes probes, OTEL, logs, spans
🔄 Rollback & retries	Executes fallback strategies or escalates safely
📡 Event emission	Emits `DeploymentStarted`, `Completed`, `Failed`, `RollbackTriggered`
🧠 Memory-driven reasoning	Uses past deployments, rollback maps, and tenant overlays
🧭 Orchestration alignment	Triggered by FSM states and event-driven task routing
📘 Studio visibility	Traceable via `traceId`, `agentId`, `skillId`, `deploymentId`

🚀 Future Extensions¶

1. 🧪 Pre-Deployment Canary Probing¶

Integrate with synthetic testers to ping sandbox copies
Trigger smoke test agents pre-cutover (optional via rollout plan)

2. 🌍 Multi-Region Rollout Support¶

Coordinate across cloud regions with staggered schedule
Geo-aware manifest overlays and observability sharding

3. 🔁 Blue/Green Auto Cutover & Traffic Weighting¶

Add progressive traffic shifting (via Istio, Linkerd, Azure Front Door)
Integrate with SLA scoring to decide when to finalize green cut

4. 💸 Cost-Aware Deployment Decisions¶

Incorporate cost telemetry and resource forecasts into plan execution
Block large-scale rollouts if cluster cost spikes beyond budget

5. 🔬 AI-Driven Deployment Risk Scoring¶

Use machine learning to assign deployment risk rating based on:
Historical failures
Infra instability
Code diff size
Telemetry patterns

6. 🔐 Zero-Trust Posture Scanning¶

Integrate with policy enforcement agents for container and manifest posture validation (e.g., SBOMs, CVEs, OPA)

7. ⏱ Scheduled & Conditional Auto-Promotions¶

Deploy at fixed maintenance windows or upon downstream event success (e.g., smoke tests + Observability SLA checks)

🧠 Final Statement¶

The Deployment Orchestrator Agent is not a deployment script — it is an intelligent, traceable, security-first operator that ensures every ConnectSoft deployment is safe, observable, memory-driven, and autonomous.

It closes the factory loop with trust.

🧠 Deployment Orchestrator Agent Specification¶

🎯 Purpose¶

🏗️ Strategic Role in the Factory¶

🧭 Scope of Mission¶

📌 Key Outcomes¶

📘 Mission Statement Example¶

🧱 Alignment with ConnectSoft Principles¶

✅ Summary¶

📋 Responsibilities¶

🎯 Core Deliverables¶

📌 Agent Mission in Operational Terms¶

✅ Example Execution Responsibilities (in Sequence)¶

🧠 Semantic Kernel Skills Likely Involved¶

🧱 Alignment with Execution Flow¶

✅ Summary¶

📥 Inputs¶

📦 Core Input Types¶

📄 Example DeploymentPlan.yaml (Snippet)¶

🧠 Memory Input Enrichment¶

🧭 Input Event Triggers¶

✅ Summary¶

📤 Outputs¶

📦 Primary Output Artifacts¶

📄 Example: DeploymentSummary.json¶

🔄 Possible Output Paths¶

📘 Sample Emitted Event Metadata¶

✅ Summary¶

📚 Knowledge Base¶

🧠 Embedded Knowledge Modules¶

📖 Retrieved Knowledge from Memory System¶

🧠 Semantic Memory & Prompt Fragments¶

📘 Example Memory Access Pattern¶

🧠 Built-in Strategy Heuristics¶

✅ Summary¶

🔄 Process Flow (Macro)¶

🧭 End-to-End Deployment Process¶

📋 Step-by-Step Overview¶

🧠 Autonomous Flow Guarantees¶

🔌 Upstream and Downstream Coordination¶

✅ Summary¶

🔬 Process Flow (Micro)¶

🧪 Detailed Internal Lifecycle¶

🧠 Internal Phase Breakdown¶

⏳ Timers and Timeouts¶

🔄 Built-in Decision Logic¶

✅ Summary¶

⚙️ Skills and Kernel Functions¶

🧩 Core Skill Categories¶

📘 1. 📄 Planning & Parsing Skills¶

📦 2. 🚀 Artifact Preparation Skills¶

⚙️ 3. 🛠️ Deployment Execution Skills¶

🔍 4. 📈 Validation & Monitoring Skills¶

🔄 5. ❌ Failure Handling & Recovery Skills¶

📡 6. 📤 Emission & Logging Skills¶

🧠 Skill Characteristics¶

🔁 Sample Skill Execution Graph¶

✅ Summary¶

⚙️ Technology Stack¶

🧱 Tech Stack Overview¶

🔁 Integration with ConnectSoft Factory¶

📘 Example Deployment Stack Usage¶

🔐 Security + Isolation Best Practices¶

✅ Summary¶

🧾 System Prompt¶

📄 Deployment Orchestrator Agent – System Prompt (v1)¶

🧠 Semantic Objectives Embedded¶

📘 When Is This Prompt Used?¶

✅ Summary¶

📥 Input Prompt Template¶

🧾 YAML-Like Input Prompt Template (Structured Form)¶

🧠 Field-by-Field Description¶

📘 Optional Variants¶

🔁 Minimal (for sandbox/dev)¶

🚨 Escalated Deployment (triggered after rollback)¶

🧠 Semantic Kernel Binding¶

✅ Summary¶

📤 Output Expectations¶

🗂️ Categories of Outputs¶

1. 📡 Lifecycle Events¶

2. 📄 Structured Artifact: DeploymentSummary.json¶

2. 📄 Structured Artifact: `DeploymentSummary.json`¶