🧩 DevOps, Deployment, and Delivery Agents Overview¶

🎯 Purpose of the Cluster¶

The DevOps, Deployment, and Delivery Agents Cluster governs the automated path from code commit to production deployment within the ConnectSoft AI Software Factory.

These agents:

Activate when code is generated and committed
Generate CI/CD logic and infrastructure
Approve or block releases based on validation and policy
Execute the deployment to target environments
Ensure delivery is traceable, compliant, and observable

This cluster turns generated software into shippable, production-ready systems — autonomously, repeatably, and securely.

🧠 What the Cluster Owns¶

Lifecycle Stage	Owned By
✅ Code Commit & Pull Request	`Code Committer Agent`, `Pull Request Creator Agent`
✅ Pipeline Generation	`DevOps Engineer Agent`
✅ Environment Provisioning	`Cloud Provisioner Agent`
✅ Release Approval	`Release Manager Agent`
✅ Rollout Execution	`Deployment Orchestrator Agent`

These agents collectively form the control and execution fabric for ConnectSoft's CI/CD, release, and deployment ecosystem.

🧱 Position in the Factory Architecture¶

flowchart LR
    Planning[📘 Vision & Planning Cluster] --> Engineering[🧪 Engineering Cluster]
    Engineering --> DevOps[🚀 DevOps, Deployment & Delivery Cluster]
    DevOps --> QA[🧬 QA & Observability Cluster]
    QA --> Runtime[📡 Runtime SLO & Feedback Cluster]

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

Engineering agents produce code.
DevOps agents commit, prepare, approve, and deploy.
QA and Observability agents validate post-deployment outcomes.
Runtime agents measure long-term success and feedback.

🧠 Strategic Cluster Functions¶

Area	Description
🔁 CI/CD Automation	Ensure every generated service has build-test-deploy pipelines
🛡 Release Governance	Enforce human approvals, change freezes, security validation
📦 Deployment Execution	Apply Helm charts, Kubernetes manifests, Pulumi templates
🔍 Observability Integration	Emit metrics, spans, and health checks at each stage
🧠 Memory-Aware Control	Store `DeploymentHistory`, `ReleaseSummary`, `PipelineMetadata` for full trace audit

📘 Cluster Output Examples¶

Agent	Output
`Pull Request Creator`	GitHub/GitLab PR with `traceId`, changelog
`DevOps Engineer`	`azure-pipelines.yaml`, `PulumiStack.json`
`Release Manager`	`ReleaseApproved` event, `ApprovalRecords.yaml`
`Deployment Orchestrator`	`DeploymentCompleted`, `DeploymentSummary.json`

🧭 Execution Scope¶

This cluster activates during the mid-to-late phase of every trace, after:

Code generation is complete
QA plans are ready
Studio trace is registered
Infrastructure is provisioned

It operates independently per module, per environment, and per tenant — enabling scalable and parallel delivery of thousands of microservices.

✅ Summary¶

The DevOps, Deployment, and Delivery cluster ensures that every piece of generated software becomes a secure, validated, traceable release — with zero manual handoffs, full approval tracking, and observable deployment paths.

It is the AI Factory's execution engine, turning code into product, safely and autonomously.

🧠 Cluster Agent Roster¶

This cluster contains six core agents that coordinate the delivery pipeline from commit to production:

1. ✍️ Code Committer Agent¶

Path: agents/detailed-agent-specifications/code-committer-agent.md

| Role | Commits generated code to the source control repository, tagging with traceId, moduleId, and version metadata. | | Responsibilities |

Create commit from generated output
Include trace metadata in commit message
Prepare clean Git tree for PR agent
Support mono-repo or multi-repo structures

2. 🔀 Pull Request Creator Agent¶

Path: agents/detailed-agent-specifications/pull-request-creator-agent.md

Create PRs with structured titles: feat: add booking-service for trace-1234
Attach module diffs, trace references, test coverage summaries
Trigger pipeline evaluation by DevOps Agent

3. ⚙️ DevOps Engineer Agent¶

Path: agents/detailed-agent-specifications/devops-engineer-agent.md

Generate azure-pipelines.yaml, Dockerfile, helm/ folder, Pulumi stack if needed
Integrate test stages (test, build, scan, deploy)
Emit PipelineSnapshot.yaml into memory
Support GitHub Actions, Azure Pipelines, GitLab CI

4. ☁️ Cloud Provisioner Agent¶

Path: agents/detailed-agent-specifications/cloud-provisioner-agent.md

Use Pulumi or Bicep to generate infra stacks
Allocate namespaces, DNS records, key vaults, resource groups
Emit InfraProvisioned event to activate downstream agents
Store EnvironmentConfig.yaml

5. 🚀 Deployment Orchestrator Agent¶

Path: agents/detailed-agent-specifications/deployment-orchestrator-agent.md

Execute helm upgrade, kubectl apply, or Pulumi updates
Monitor readiness/liveness/startup probes
Emit DeploymentCompleted or RollbackTriggered
Store DeploymentSummary.json, update DeploymentHistory.json

6. 📦 Release Manager Agent¶

Path: agents/detailed-agent-specifications/release-manager-agent.md

| Role | Controls release readiness gates — QA, approval, security, change freeze — and authorizes final handoff. | | Responsibilities |

Evaluate test, security, compliance signals
Prompt for Studio approval (if requiresApproval: true)
Emit ReleaseApproved, ReleaseDelayed, or ReleaseRejected
Log ApprovalRecords.yaml, ReleaseSummary.json

📘 Agent Scope Summary¶

Agent	Phase	Output
Code Committer	Git Initiation	`commit with traceId`
Pull Request Creator	Pre-Pipeline	`pull request with diff + changelog`
DevOps Engineer	Pipeline Assembly	`azure-pipelines.yaml`, `PipelineSnapshot.yaml`
Cloud Provisioner	Infrastructure Setup	`EnvironmentConfig.yaml`, `InfraProvisioned`
Release Manager	Governance Control	`ReleaseApproved`, `ReleaseRejected`
Deployment Orchestrator	Delivery Execution	`DeploymentCompleted`, `RollbackTriggered`

✅ Summary¶

Each agent in this cluster has a clear, modular, and memory-driven role. Together they form a delivery control system that is:

Fully autonomous
Secure and policy-aware
Trace-linked from commit to production
Reusable across thousands of projects

🔄 Cluster Flow: From Commit to Deployment¶

This section outlines the end-to-end interaction sequence across the six agents in the cluster. The flow is event-driven, trace-bound, and memory-linked — forming the automation backbone of ConnectSoft’s software delivery engine.

📈 Sequence Diagram: Commit → CI/CD → Release → Deploy¶

sequenceDiagram
    participant Committer as Code Committer Agent
    participant PR as Pull Request Creator Agent
    participant DevOps as DevOps Engineer Agent
    participant Cloud as Cloud Provisioner Agent
    participant Release as Release Manager Agent
    participant Deployer as Deployment Orchestrator Agent
    participant Observability as Observability Agent

    Note over Committer,PR: Commit & Initiate DevOps Flow
    Committer->>PR: Emit `CommitReady`
    PR->>DevOps: Emit `PullRequestCreated`

    Note over DevOps,Cloud: CI/CD Assembly + Infra
    Cloud-->>DevOps: Emit `InfraProvisioned`
    DevOps->>Release: Emit `PipelineReady`

    Note over Release,Deployer: Release Evaluation + Deployment
    Release->>Deployer: Emit `ReleaseApproved`
    Deployer->>Observability: Emit `DeploymentCompleted`

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

🧠 Event Flow Summary¶

Step	Trigger	Source Agent	Target Agent	Description
1️⃣	`CommitReady`	Code Committer	Pull Request Creator	Code changes committed for a given trace.
2️⃣	`PullRequestCreated`	PR Creator	DevOps Engineer	Triggers generation of pipelines, deployment configs.
3️⃣	`InfraProvisioned`	Cloud Provisioner	DevOps Engineer	Indicates infra is ready for deployment configs.
4️⃣	`PipelineReady`	DevOps Engineer	Release Manager	CI/CD is valid; release may proceed.
5️⃣	`ReleaseApproved`	Release Manager	Deployment Orchestrator	Authorizes deployment based on QA, freeze, and approver state.
6️⃣	`DeploymentCompleted`	Deployment Orchestrator	Observability Agent	Notifies runtime layer that deployment succeeded.

🗂 Memory Artifacts Along the Flow¶

Artifact	Written By	Used By
`commit metadata (traceId)`	Code Committer	All agents
`PipelineSnapshot.yaml`	DevOps Engineer	Studio + Release Manager
`EnvironmentConfig.yaml`	Cloud Provisioner	DevOps + Deployment Orchestrator
`ReleaseSummary.json`	Release Manager	Studio UI + Auditors
`DeploymentSummary.json`	Deployment Orchestrator	Observability Agent, SLA Validators

📘 Execution Boundaries Across Tenants/Envs¶

All events, logs, and files are tagged with:

traceId, releaseId
tenantId, environment, moduleId
agentId, skillId, status

This ensures each agent operates within precisely scoped boundaries and avoids cross-tenant leakage.

✅ Summary¶

The DevOps, Deployment, and Delivery cluster executes as a deterministic, event-coordinated graph that:

Converts committed code into deployed services
Enforces CI/CD consistency
Applies governance policies
Emits all observability and approval state as signals
Scales per trace, per module, per tenant

This makes it the backbone of secure, autonomous, and production-safe software delivery.

🧠 CI/CD Ownership and Responsibility Split¶

In a factory that autonomously generates thousands of microservices, CI/CD is not a single pipeline — it is a composable sequence of agent-managed artifacts and skills.

This section defines how each agent contributes to the CI, CD, and Release Governance lifecycle, while maintaining clear ownership, traceability, and tenant/environment scope.

⚙️ CI/CD Responsibilities per Agent¶

Stage	Owner	Responsibilities
Commit & Trace Binding	Code Committer Agent	Creates Git commit with embedded `traceId`, `moduleId`, `version`, and changelog anchor.
Pull Request Creation	Pull Request Creator Agent	Generates traceable PR with changelog, affected files, links to Studio trace.
Pipeline Generation (CI)	DevOps Engineer Agent	Generates `azure-pipelines.yaml`, `Dockerfile`, `test.yaml`, and `build.yaml`.
Secrets & Config Scaffolding	DevOps Engineer Agent	Sets up Key Vault links, secrets mapping, `appsettings.Template.json`.
Infrastructure Provisioning (CD Pre-Req)	Cloud Provisioner Agent	Allocates AKS namespaces, DNS zones, blob storage, config maps, etc.
Release Approval (CD Gate)	Release Manager Agent	Validates that the build artifacts are safe and approved for environment promotion.
Deployment Execution (CD)	Deployment Orchestrator Agent	Applies manifests or Helm charts into provisioned environments; monitors readiness.

📘 Example Output Files per Agent (CI/CD-Scoped)¶

Agent	Output
DevOps Engineer	`azure-pipelines.yaml`, `PipelineSnapshot.yaml`
Cloud Provisioner	`EnvironmentConfig.yaml`, `DNSBindings.yaml`
Deployment Orchestrator	`DeploymentSummary.json`, `RolloutPlan.yaml`
Release Manager	`ApprovalRecords.yaml`, `ReleaseSummary.json`

All files are emitted with traceId, moduleId, and environment annotations.

🧬 Ownership Boundaries: Who Does What?¶

Operation	Agent Responsible
Generate unit test stage	DevOps Engineer Agent
Provision test cluster	Cloud Provisioner Agent
Block pipeline during weekend freeze	Release Manager Agent
Roll back failed deploy	Deployment Orchestrator Agent
Expose deploy metrics to Grafana	Deployment Orchestrator + DevOps Engineer Agent
Revoke deployment due to CVE policy	Release Manager Agent

📦 CI/CD Shared State Summary¶

PipelineSnapshot.yaml
├─ stages:
│  ├─ build
│  ├─ test
│  ├─ scan
│  ├─ deploy
├─ traceId: trace-2025-05-23-0012
├─ module: invoice-service
├─ environment: staging
├─ version: 1.3.8

Each agent contributes to this evolving file based on its domain (e.g., scan by DevOps Engineer, deploy by Deployment Orchestrator).

✅ Summary¶

CI/CD ownership in the ConnectSoft AI Factory is:

Distributed and composable across agents
Trace-driven and version-aware
Policy-bound and environment-scoped
Observable and audit-persistent

This ensures that every stage of delivery — from test to release — is modular, reusable, and automatable at scale.

🔁 Trace-Driven Workflows¶

In the ConnectSoft AI Software Factory, every workflow is tied to a unique traceId, which anchors:

All commits
PRs and CI/CD runs
Release approvals
Deployment executions
Studio visualizations
Memory artifacts

This ensures that each delivery cycle is self-contained, observable, auditable, and replayable.

🧭 Core Trace Fields Carried by Agents¶

Field	Description
`traceId`	Unique identifier for the full factory execution thread
`moduleId`	The software component or microservice being delivered
`releaseId`	Logical identifier for the version + environment being promoted
`environment`	Target: `dev`, `staging`, `production`, etc.
`tenantId`	Optional tenant-specific execution scope
`agentId`	The acting agent at each step
`skillId`	The specific skill or function executed by that agent

These identifiers are embedded into all files, spans, events, and memory logs.

🧠 Trace-Driven Memory Pattern¶

Example: Release Manager Memory Files¶

memory/releases/
  └─ trace-2025-05-23-0012/
     ├─ ReleaseSummary.json
     ├─ ApprovalRecords.yaml
     ├─ ReleaseHistory.json

Each trace creates its own memory directory, enabling parallel, isolated trace execution.

🔁 Example Trace Execution Thread¶

sequenceDiagram
    participant Studio
    participant PR as Pull Request Creator
    participant DevOps as DevOps Engineer
    participant Release as Release Manager
    participant Deployer as Deployment Orchestrator

    Studio->>PR: CreatePullRequest(traceId)
    PR->>DevOps: GeneratePipeline(traceId)
    DevOps->>Release: Emit PipelineReady(traceId)
    Release->>Deployer: Emit ReleaseApproved(traceId)
    Deployer->>Studio: Emit DeploymentCompleted(traceId)

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

Every action, artifact, and outcome is tagged with the same traceId.

📘 Trace-Scoped Artifacts¶

File	Agent	Purpose
`PipelineSnapshot.yaml`	DevOps Engineer	CI/CD definition with build/test/deploy stages
`EnvironmentConfig.yaml`	Cloud Provisioner	Scoped infrastructure config
`DeploymentSummary.json`	Deployment Orchestrator	Deployment health, latency, restarts
`ApprovalRecords.yaml`	Release Manager	Human-in-the-loop decision trail

All of these sit under a trace-specific directory or memory shard.

🧪 Benefits of Trace-Driven Workflows¶

Benefit	Impact
Isolated execution	Multiple modules can execute in parallel without race conditions
Replayability	Every trace has full memory to support retries, revalidations, or postmortems
Studio Integration	Timeline, event logs, approvals, and metrics are tied to `traceId`
Cross-agent coordination	Allows agents to interact without tightly coupling via APIs

✅ Summary¶

Trace-driven workflows make the DevOps cluster:

✅ Safe to run at scale (thousands of traces)
✅ Easy to debug and audit (single-trace memory)
✅ Flexible to extend and integrate (traceId is the unifying key)
✅ Secure, observable, and isolated (each trace is sandboxed)

It is the core design pattern that enables scalable, autonomous delivery in ConnectSoft.

🔄 Agent Collaboration Interfaces¶

The DevOps cluster is highly collaborative by design — no single agent operates in isolation. Instead, agents pass control, emit signals, and share memory as they:

Transform code into deployable artifacts
Validate infrastructure and policy readiness
Govern and execute deployment flows

This section describes how agents interact and depend on each other, forming a decentralized but orchestrated CI/CD fabric.

🤝 Intra-Cluster Collaboration Table¶

Source Agent	Target Agent	Signal / Memory Shared	Purpose
Code Committer Agent	Pull Request Creator Agent	`CommitReady` event	Triggers PR with changelog and trace metadata
Pull Request Creator Agent	DevOps Engineer Agent	`PullRequestCreated` event	Starts pipeline generation
Cloud Provisioner Agent	DevOps Engineer Agent	`EnvironmentConfig.yaml`	Enables pipeline to target provisioned environment
DevOps Engineer Agent	Release Manager Agent	`PipelineSnapshot.yaml` + `PipelineReady` event	Allows evaluation of readiness
Release Manager Agent	Deployment Orchestrator Agent	`ReleaseApproved` event + `ReleaseSummary.json`	Triggers actual deployment
Deployment Orchestrator Agent	Studio, Observability Agent	`DeploymentCompleted`, `DeploymentSummary.json`	Notifies end of delivery and readiness for monitoring

🧠 Shared Memory Contracts¶

Memory File	Writer	Reader(s)
`PipelineSnapshot.yaml`	DevOps Engineer	Release Manager, Studio
`ApprovalRecords.yaml`	Release Manager	Studio, auditors
`EnvironmentConfig.yaml`	Cloud Provisioner	DevOps Engineer, Deployment Orchestrator
`DeploymentSummary.json`	Deployment Orchestrator	Observability Agent, ReleaseAuditorAgent

All memory paths are trace- and tenant-scoped, ensuring isolation and replay safety.

🧩 Event-Based Agent Triggering¶

Examples of Event Triggers:¶

CommitReady → triggers PR creation
PullRequestCreated → triggers pipeline generation
InfraProvisioned → allows DevOps to proceed with staging
ReleaseApproved → activates deployment
DeploymentFailed → optionally triggers rollback, revalidation, or human escalation

📘 Collaboration Contract Example¶

DevOps Engineer Agent awaits:¶

PullRequestCreated
InfraProvisioned
Then generates:
- PipelineSnapshot.yaml
- CI/CD script files
- Emits PipelineReady

Release Manager Agent awaits:¶

PipelineReady
TestsPassed
ApproverSigned (optional)

🔁 Asynchronous Design Benefits¶

Benefit	Description
Loose coupling	Agents don’t call each other — they observe memory/events
Parallel execution	PR creation, provisioning, and test running can occur simultaneously
Retryable/resumable	If an agent fails mid-trace, it can resume from memory state
Multi-agent orchestration	Enables FSM coordination across traces without locking threads

✅ Summary¶

Agent collaboration in the DevOps cluster is based on:

✅ Events (PullRequestCreated, ReleaseApproved)
✅ Memory artifacts (DeploymentSummary.json, PipelineSnapshot.yaml)
✅ TraceId contract that binds them all

This decentralized, observable coordination enables the ConnectSoft AI Factory to scale DevOps automation safely across thousands of services.

🔄 Upstream and Downstream Interfaces¶

In a modular agentic platform, each cluster must have clear inputs and outputs to:

Remain decoupled
Enable parallel execution
Ensure traceable and deterministic workflows
Support integration with non-agent systems (e.g., Git, pipelines, Kubernetes)

This section defines the upstream sources that activate this cluster and the downstream agents that consume its outcomes.

🟢 Upstream Interfaces¶

These are the agents, humans, and systems that activate the DevOps cluster or feed it necessary input:

Source	Type	Interface	Description
🧠 Engineering Agents	Internal Agent	`ModuleReady`, `TestsGenerated`, `TraceInitialized`	Triggers Code Committer Agent after code is finalized
📘 Studio User	Human-in-the-loop	`StudioCommitRequest`, `StudioPRAction`, CLI flags	Triggers commits, PR creation, or manual approvals
🧠 Vision/Planning Cluster	Internal Agent	`BlueprintValidated`, `ReleaseIntentDeclared`	Informs DevOps that a release path is ready
🧪 QA Agents	Internal Agent	`TestsPassed`, `SmokeTestsReady`	Enables Release Manager Agent to evaluate approval gates
☁️ Infrastructure FSM	System/Orchestrator	`InfraProvisioned`	Indicates cloud resources (e.g., AKS, DNS) are ready

🔽 Downstream Interfaces¶

These are the agents or systems that consume events and artifacts produced by this cluster:

Target	Type	Event / Memory	Description
🔍 QA Cluster	Internal Agent	`PipelineSnapshot.yaml`, `TestsExecuted`	QA agents validate post-pipeline artifacts
📡 Observability Cluster	Internal Agent	`DeploymentCompleted`, OTEL spans	Observes service health, performance, errors
📦 Runtime SLO Cluster	Internal Agent	`ReleasePublished`, `DeploymentSummary.json`	Validates SLA conformance
📘 Studio	Human-facing UI	`Timeline Events`, `Approval Logs`, `Deploy Status`	Surfaces release readiness and deployment progress
🧑‍⚖️ Audit & Compliance Tools	External system	`ApprovalRecords.yaml`, `ReleaseHistory.json`	Used for external traceability and compliance exports

📘 File Outputs Read by Other Clusters¶

File	Read By	Purpose
`ReleaseSummary.json`	Studio, Observability, Auditors	Describes what, when, who, and how a release was approved
`DeploymentSummary.json`	Observability, QA, SLO Agents	Provides deployment health data (latency, success, rollbacks)
`PipelineSnapshot.yaml`	QA Cluster	Confirms CI/CD config matches test requirements
`ApprovalRecords.yaml`	Studio, Audit	Human sign-off trace and role enforcement validation

🔄 Orchestration Transition Points¶

Event	Origin Agent	Target FSM/Cluster
`PullRequestCreated`	PR Creator Agent	Triggers DevOps + Testing FSM
`ReleaseApproved`	Release Manager Agent	Activates Deployment FSM
`DeploymentCompleted`	Deployment Orchestrator Agent	Triggers post-deploy QA + Observability FSM
`ReleasePublished`	Final orchestrated event	Marks full CI/CD + deploy flow completion

✅ Summary¶

The DevOps, Deployment, and Delivery cluster serves as:

The execution bridge between Engineering and Runtime
The release control plane triggered by QA and Planning clusters
The deployment source of truth for Observability and SLA agents

It is both event-reactive and memory-driven, designed for safe integration in a multi-agent, multi-environment delivery graph.

🧠 Memory Model Across Agents¶

In the ConnectSoft AI Software Factory, memory enables agents to:

Work asynchronously
Store decisions and artifacts
Coordinate without direct calls
Enable traceability and replay
Provide audit and compliance visibility

The DevOps cluster uses a shared, trace-scoped, and agent-specific memory model to persist delivery-critical artifacts and support event-based workflows.

📂 Memory Architecture Model¶

Each trace (traceId) has its own memory root. Inside this scope, each agent stores files using predictable paths and naming conventions.

/memory
└── trace-2025-05-23-0012/
    ├── commits/
    │   └── CommitMetadata.json
    ├── pipelines/
    │   └── PipelineSnapshot.yaml
    ├── approvals/
    │   └── ApprovalRecords.yaml
    ├── releases/
    │   └── ReleaseSummary.json
    ├── deployments/
    │   └── DeploymentSummary.json
    ├── env/
    │   └── EnvironmentConfig.yaml

📘 Agent-Specific Memory Responsibilities¶

Agent	Memory Artifacts
Code Committer Agent	`CommitMetadata.json` — Git commit hash, traceId, moduleId, version
Pull Request Creator Agent	(Optional) `PullRequestMetadata.json` — PR URL, linked branches
DevOps Engineer Agent	`PipelineSnapshot.yaml`, `CIStages.yaml`, `Dockerfile`, `azure-pipelines.yaml`
Cloud Provisioner Agent	`EnvironmentConfig.yaml`, `DNSBindings.yaml`
Release Manager Agent	`ApprovalRecords.yaml`, `ReleaseSummary.json`, `ReleaseHistory.json`
Deployment Orchestrator Agent	`DeploymentSummary.json`, `DeploymentHistory.json`, `RollbackMap.yaml`

🔁 Memory Lifecycles¶

Lifecycle Phase	Memory Access
Pre-commit	Write: `CommitMetadata.json`
Pre-pipeline	Write: `PipelineSnapshot.yaml`, read: `EnvironmentConfig.yaml`
Pre-release	Read: `PipelineSnapshot.yaml`, `QAResults.log`
Post-release	Write: `ReleaseSummary.json`, `ApprovalRecords.yaml`
Post-deployment	Write: `DeploymentSummary.json`, read: `RollbackMap.yaml`

Each agent only reads or writes its scoped memory unless otherwise collaborating through shared contracts.

🔒 Memory Governance¶

Constraint	Description
Trace-scoped	All memory entries live under `/memory/trace-{traceId}`
Tenant-isolated	Agents are only allowed to access memory matching their `tenantId`
Audit-linked	All approvals, deployments, and rejections are timestamped and signed
Studio-readable	Studio surfaces human-friendly views of `ReleaseSummary`, `DeploymentSummary`, `ApprovalRecords`

📊 Memory for Observability + Feedback¶

Downstream agents (QA, SLO, Observability) use this memory to:

Audit why a release was approved
Reproduce a deployment locally
Replay pipeline configurations
Detect delivery anomalies
Visualize delivery flows on trace timelines

✅ Summary¶

The DevOps cluster’s memory model:

Enables modular, event-driven agent coordination
Supports Studio visualization and audit
Guarantees trace-safe, tenant-aware, environment-specific persistence
Serves as the source of truth for all delivery-critical data

This makes it a foundational building block for autonomous, resilient software delivery.

🔐 Security and RBAC Enforcement Points¶

This cluster operates at the CI/CD + runtime boundary — the most sensitive layer in the ConnectSoft Factory. It interacts with:

Code repositories
Secrets and infrastructure
Production environments
Human approvals

It must therefore enforce role-based access, environment scoping, and artifact validation at every stage.

🧠 Core Security Controls per Agent¶

Agent	Security Responsibilities
Code Committer Agent	Enforces commit message formatting, trace scoping, and repo access rights
Pull Request Creator Agent	Ensures PR targets correct branch; validates changelog format
DevOps Engineer Agent	Enforces secrets-injection only from scoped Key Vault; validates pipeline structure
Cloud Provisioner Agent	Scopes provisioning by tenant and environment; uses signed stack definitions
Release Manager Agent	Enforces approval RBAC, role requirements, justification logging
Deployment Orchestrator Agent	Runs deployments only to authorized namespaces; validates manifests; uses signed images (if required)

🧾 RBAC Enforcement Scenarios¶

Action	Enforced Role
Approve production release	Must have `ReleaseManager` or `ProductOwner`
Provision shared cloud resource	Must have `InfraAdmin` or `CloudArchitect`
Trigger rollback	Requires `Deployer`, `DevOpsEngineer`, or Studio Admin
Override change freeze	Requires `StudioApprover` with `override.freeze` flag

RBAC is enforced via:

ApprovalRecords.yaml
Studio user role claims
Agent config overlays

🔒 Secrets and Vault Access Controls¶

Mechanism	Description
`Key Vault injection`	Handled by DevOps Engineer Agent + Deployment Orchestrator Agent
`Secret scope resolution`	Tied to `tenantId`, `environment`, and `moduleId`
`Missing or invalid vault link`	Blocks deployment and triggers `ReleaseRejected`
`Runtime sidecar secrets`	Optional support for injected secrets at deploy time via init container or CSI driver

✅ Signed Artifact Support¶

Feature	Agent	Behavior
OCI image signature validation	Deployment Orchestrator Agent	Reject unsigned containers in production (configurable)
Git commit verification	Code Committer Agent	Signs commits with trace metadata and signature hash
SBOM signature verification	Release Manager Agent (future)	Optional: block release without signed SBOM metadata

📘 Policy-Driven Enforcement Example¶

securityPolicy:
  requireApprovalFor:
    - environment: production
      roles: [ProductOwner, ReleaseManager]
  requireSignedImages: true
  enforceChangeFreeze: true
  allowedApprovers:
    vetclinic:
      - user: jane.doe
        role: ProductOwner

This governs the Release Manager and Deployment Orchestrator behavior.

🚨 Security Failure Scenarios¶

Condition	Result
Deployment to unauthorized namespace	Blocked with `InvalidNamespaceAccess`
Key Vault reference invalid or missing	Blocked with `SecretsMissing`
Approval granted by wrong role	`ReleaseRejected`, reason: `UnauthorizedApprover`
Image scan failed	Blocked by `SecurityPolicyEnforcerAgent` or `Release Manager Agent`
Change freeze override without scope	Delay + escalation request to Studio approver

✅ Summary¶

Security in this cluster is:

Proactive and policy-bound
Multi-agent and environment-aware
Scoped by tenant, trace, and role
Tightly integrated with Studio and trace memory

This makes ConnectSoft’s delivery agents secure-by-default, compliant-by-design, and resilient at runtime.

🖥 Studio Integration¶

ConnectSoft Studio is the human-facing window into the AI Software Factory. Agents in the DevOps cluster must:

Expose key lifecycle milestones
Enable manual approvals (where required)
Display CI/CD and deployment status
Surface errors, failures, and rollback events
Provide traceability and compliance history

This integration ensures that Studio users can interact with, monitor, and audit every release decision.

🔄 What Agents Expose to Studio¶

Agent	Studio UI Contributions
Code Committer Agent	Displays committed modules, `traceId`, repo/branch info
Pull Request Creator Agent	Shows PR URL, title, affected modules
DevOps Engineer Agent	Injects pipeline preview, test status, build links
Cloud Provisioner Agent	Flags infra readiness, shows provisioned endpoints or stack metadata
Release Manager Agent	Manages approval prompt UI, rejection reasons, approver identity
Deployment Orchestrator Agent	Displays status: `in-progress`, `completed`, `rolled back`, `failed`, probe health

📘 Studio Timeline Updates¶

Each agent emits timeline events with traceId context:

- timestamp: 2025-05-23T10:02:00Z
  agent: release-manager
  event: ReleaseApproved
  user: jane.doe
  reason: "QA passed, security validated"
  environment: production

These appear on the trace timeline, alongside generated code events, tests, approvals, and deployment flows.

👥 Studio Approval & Interaction Flow¶

Approval Workflow¶

Agent: Release Manager Agent
Interface: Studio UI panel (Approval section)
Inputs: Approve, Reject, Add rationale, Override freeze
Outputs:
- ApprovalRecords.yaml
- Timeline entry
- Trigger to Deployment Orchestrator Agent

Rollback Trigger (Optional Future)¶

Agent: Deployment Orchestrator Agent
Studio Button: “Request rollback”
Input: Reason / error code / comments
Action: Emits RollbackTriggered, writes RollbackMap.yaml

🔎 Studio Deployment Dashboard View¶

Section	Data Source
Modules being deployed	`DeploymentSummary.json`
Live status per environment	K8s events, OTEL traces, health probes
Deployment metrics	Startup time, pod restarts, crash loops
Deployment issues	Aggregated from logs and memory snapshots

🧾 Studio Audit View¶

View	Backed by
Who approved the release	`ApprovalRecords.yaml`
Why a release was blocked	`ReleaseSummary.json → reasons[]`
When the deployment completed	`DeploymentSummary.json → deployedAt`
What failed in probes	`healthStatus[]`, OTEL span failures

🧠 Example Studio Flow: Releasing to Production¶

Studio shows PR & CI status
DevOps Agent emits PipelineReady
Release Manager triggers approval prompt
User clicks “Approve” → Studio logs signature
Deployment Orchestrator updates Studio with live rollout events
Timeline ends with “✅ DeploymentCompleted @ 10:12”

✅ Summary¶

Studio integration enables:

Visibility into every release lifecycle phase
Safe manual intervention where needed
Real-time deployment observability
Fully auditable release history per trace

This turns the DevOps cluster into a governance-aware, user-interactive release pipeline — not just a background automation process.

🏢 Environment and Tenant Scoping¶

In a multi-tenant, multi-environment factory model like ConnectSoft’s, every deployment must be:

Isolated per environment (e.g., dev, staging, prod)
Bound to a tenant (e.g., vetclinic-001, petcare-labs)
Restricted by policy, memory access, and approval chains

This prevents cross-contamination, enforces compliance, and allows independent control of hundreds of parallel delivery streams.

📦 Key Scope Dimensions¶

Scope Dimension	Description
`environment`	The deployment stage (dev, test, staging, production, etc.)
`tenantId`	The logical or commercial client (SaaS tenant, project, or region)
`moduleId`	The microservice or component under delivery
`editionId` (optional)	The SaaS edition (basic, premium, enterprise) being deployed

🔐 Scoping Applied by Each Agent¶

Agent	Scoped By
Code Committer Agent	`moduleId`, `traceId`
Pull Request Creator Agent	`tenantId`, `traceId`, `branch`
DevOps Engineer Agent	`tenantId`, `environment`, `moduleId`
Cloud Provisioner Agent	`tenantId`, `environment`, `editionId`
Release Manager Agent	`tenantId`, `environment`, `role`-based approval policy
Deployment Orchestrator Agent	`namespace = tenantId-env`, validates manifests and secrets per scope

🗂 Directory Layout Example¶

/memory/trace-2025-05-23-0012/
  ├── tenant-vetclinic-001/
  │   ├── env-staging/
  │   │   ├── invoice-service/
  │   │   │   ├── DeploymentSummary.json
  │   │   │   ├── RollbackMap.yaml
  │   │   │   ├── ReleaseSummary.json

This folder-level scoping ensures memory files never overlap between tenants or environments.

🌐 Namespace and K8s Resource Mapping¶

Each deployment is targeted to a scoped namespace, typically:

namespace = {tenantId}-{environment}
e.g. vetclinic-001-prod

This governs:

Secret injection
RBAC policies
Network policies
Resource quotas

🔐 Tenant-Specific Policy Examples¶

Policy	Enforced By
Only `ProductOwner` of `vetclinic-001` can approve production releases	Release Manager Agent
DNS entries must use tenant prefix	Cloud Provisioner Agent
Secrets injected from `KeyVault:vetclinic-prod` only	DevOps Engineer Agent
Monitoring alerts mapped to tenant SLO dashboards	Observability cluster (future)

📘 Edition-Specific Support (Optional)¶

Field	Purpose
`editionId: enterprise`	Allows conditionally generating pipelines or Helm values for feature flags
Used by:	DevOps Engineer, Cloud Provisioner, Deployment Orchestrator

✅ Summary¶

Every agent in the DevOps cluster respects strict scoping boundaries for:

Tenant
Environment
Module
Edition

This makes ConnectSoft’s software delivery safe, compliant, and multi-tenant by design, supporting autonomous delivery at massive scale.

👥 Human-in-the-Loop Escalation Paths¶

Although the ConnectSoft Factory is agentic and autonomous by default, certain delivery stages require human decisions to ensure:

Approval compliance in production
Resolution of ambiguous or high-risk conditions
Intervention during failures or policy exceptions
Compliance with governance, legal, or business rules

This section defines where manual decision gates are integrated into the otherwise automated flow.

🔁 Escalation Triggers¶

Trigger	Handled By	Escalation
Release to production requires manual sign-off	✅ Release Manager Agent	Studio UI approval prompt
Change freeze is active	✅ Release Manager Agent	Delays until lifted or overridden
Security policy partially failed (e.g., CVE warning)	✅ Release Manager Agent	Studio prompt for override
Post-deploy failure in prod with rollback disabled	✅ Deployment Orchestrator Agent	Escalates to Studio Admin for manual rollback
Pipeline build failed due to missing integration	✅ DevOps Engineer Agent (via Studio signal)	Studio troubleshooting & replan required

🖥 Studio UI Hooks¶

Action	Agent Involved	Interface
✅ Approve/Reject release	Release Manager	Timeline prompt
📝 Provide rationale for approval	Release Manager	Approval comment → `ApprovalRecords.yaml`
🧾 Override freeze window	Studio Admin + Release Manager	`override.freeze = true` in UI
🔁 Trigger manual rollback	Deployment Orchestrator Agent	Optional rollback override UI
👀 View deployment progress	Studio + Deployment Agent	Live timeline with `DeploymentCompleted`, `RollbackTriggered` markers

📘 Approval Memory Record Example¶

- releaseId: release-001293
  approvedBy: alice.jones
  role: ProductOwner
  method: Studio UI
  approvedAt: 2025-05-23T10:45:00Z
  comment: "Critical fix for outage - CVE override approved"

Stored in ApprovalRecords.yaml and used for audits.

🧠 CLI & Bot-Based Escalation (Optional)¶

Tool	Usage
`studio approve-release`	Approves release from CLI
`studio reject-release`	Rejects and provides rationale
`studio request-rollback`	Escalates rollback request for failed deployment
Slack/Teams bot (future)	Notifies stakeholders of pending approvals or failures

🔐 RBAC Enforced Escalation Roles¶

Role	Permission
`ProductOwner`	Can approve production
`ReleaseManager`	Can reject, delay, or escalate
`DevOpsEngineer`	Can comment or retry builds
`StudioAdmin`	Can override freezes or rollback restrictions

All actions are scoped per tenantId, environment, and validated before execution.

📊 Audit Trail After Escalation¶

Approval logged in ApprovalRecords.yaml
Studio shows timeline reason and actor
Event stream includes ReleaseApproved, ReleaseRejected, or ReleaseDelayed
All are tied to traceId, releaseId, and moduleId

✅ Summary¶

The DevOps cluster is designed for:

Autonomous default delivery
Deterministic error handling
Governed escalation

It includes human-in-the-loop checkpoints only when necessary, and always logs them with:

RBAC roles
Justifications
Time-stamps
Scope enforcement

This ensures the delivery process remains auditable, flexible, and compliant — even when humans intervene.

📡 Observability Hooks and Metrics¶

The DevOps, Deployment, and Delivery cluster is a critical execution layer — handling builds, releases, and production deployments.

To ensure traceability, reliability, and SLA conformance, each agent must:

Emit OpenTelemetry spans
Log structured JSON events
Write deployment and approval summaries
Expose metrics for dashboards and alerting

This enables full-stack, real-time visibility into the entire delivery process.

📊 Core Observability Dimensions¶

Signal Type	Example Tools	Usage
🔍 OpenTelemetry spans	Azure Monitor, Jaeger, Application Insights	Agent/skill-level tracing with timing and status
📈 Metrics (Prometheus)	Grafana, Azure Monitor	Rate, duration, error % of release and deploy events
📝 Structured logs	Serilog, Loki, Elastic	Human and machine-readable trace logs
📘 Studio event timeline	Studio UI	Human-facing view of major delivery milestones
📄 Memory artifacts	DeploymentSummary.json, ReleaseSummary.json	Long-term persistent observability traces

🧠 Agent-Level Span Examples¶

Agent	Span Tags
Code Committer Agent	`agentId=code-committer`, `commitId`, `traceId`
DevOps Engineer Agent	`skillId=GeneratePipeline`, `durationMs`, `status=Success`
Release Manager Agent	`event=ReleaseApproved`, `approver=j.doe`, `environment=production`
Deployment Orchestrator Agent	`startupTimeMs`, `readinessSuccess`, `rollbackTriggered=false`

All are tagged with: traceId, releaseId, moduleId, tenantId, agentId

📈 Metrics Emitted by Cluster¶

Metric Name	Description
`release_approved_total`	Count of approvals (by env/tenant)
`release_rejected_total`	Count of rejected release attempts
`pipeline_generation_duration_ms`	Time to generate CI/CD config
`deployment_duration_seconds`	Time from deploy trigger to readiness
`rollback_triggered_total`	How many deployments required rollback
`approval_wait_time_seconds`	Time between `ReleaseIntent` and human sign-off

📝 Structured Log Example (Serilog JSON)¶

{
  "timestamp": "2025-05-23T10:44:21Z",
  "agent": "deployment-orchestrator",
  "event": "DeploymentCompleted",
  "traceId": "trace-2025-05-23-0012",
  "releaseId": "release-001731",
  "durationMs": 84213,
  "restartCount": 0,
  "status": "Success"
}

These logs feed into:

DeploymentHistory.json
ReleaseHistory.json
Studio deployment dashboards

📘 Example: Grafana Panel Metrics¶

Panel	Metric
"Deploy Latency by Module"	`deployment_duration_seconds{module="booking-service"}`
"Release Failure Rate"	`release_rejected_total / (release_approved_total + release_rejected_total)`
"Rollback Heatmap"	`rollback_triggered_total{env="production"}`
"Pending Approvals"	Derived from Studio API + memory reads

🔄 Feedback Loop for Agents¶

Observability data is also used by:

Observability Agent: To detect post-deploy regressions
ReleaseAuditorAgent: To audit deployment health vs. release approvals
SLO Validator: To assess if recent releases degrade metrics

✅ Summary¶

The DevOps cluster emits rich observability hooks at every stage:

Commits, pipelines, releases, deployments
Approvals, rollbacks, restarts, failures

This powers Studio timelines, Grafana dashboards, Studio alerts, and postmortems, making delivery safe, transparent, and measurable.

🔁 Rollback and Recovery Flows¶

Failures during release or deployment are inevitable. The DevOps cluster must provide automated rollback paths, deterministic retry logic, and manual recovery hooks to ensure:

Service reliability
SLA protection
Zero human babysitting
Full trace visibility on recovery actions

🚨 When Recovery Is Triggered¶

Condition	Response
Health checks fail (readiness, liveness, probes)	Trigger rollback (automated or policy-based)
Startup timeout expires	Retry or rollback
Manual rollback requested from Studio	Escalates to human recovery flow
Policy or approval invalidates release post-factum	Revert to last known good state
Unexpected crash loop after deploy	Record failure, initiate rollback, emit observability signal

⚙️ Rollback Flow Orchestrated by Deployment Orchestrator Agent¶

flowchart TD
    A[DeploymentStarted] --> B[Apply Manifests]
    B --> C[Monitor Health Probes]
    C --> D{All Probes Pass?}
    D -- Yes --> E[Emit DeploymentCompleted]
    D -- No --> F[Retry if allowed]
    F --> C
    F --> G{Retry Limit Reached?}
    G -- Yes --> H[Trigger Rollback]
    H --> I[Emit RollbackTriggered → Studio]
    I --> J[Emit DeploymentFailed (Recoverable)]

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

🔁 Rollback Types Supported¶

Type	Description
Helm rollback	Roll back to previous Helm release revision
kubectl rollout undo	K8s-native undo on deployment
Pulumi stack restore	Revert cloud infrastructure state
Manual PR revert	Triggered by Code Committer Agent (optional future)
Studio-triggered rollback	Manual recovery for high-risk rollouts

🧠 Memory Support for Recovery¶

1. `RollbackMap.yaml`¶

module: invoice-service
previousVersion: 1.3.8
currentAttempt: 1.3.9
lastKnownGood: 1.3.8
strategy: helm
retryCount: 2
rollbackAllowed: true

2. `DeploymentHistory.json`¶

Tracks all failed, rolled back, or retried deployments per module and tenant.

👥 Manual Recovery via Studio¶

Action	Trigger
“Rollback Now”	Calls rollback skill manually
“Approve Hotfix”	Allows redeploy of emergency version
“Delay Next Attempt”	Schedules retry with cooldown
“View Failure Details”	Shows `DeploymentSummary.json` + logs + metrics

📊 Metrics and Logging for Recovery¶

Metric	Description
`rollback_triggered_total`	Number of times a rollback was required
`recovery_duration_seconds`	Time from failure to stable redeployment
`deployment_failed_total`	Failed deployments (pre-rollback)
`studio_manual_rollback_total`	User-triggered rollback via UI/CLI

🔄 Retry Policy Example¶

retryPolicy:
  maxAttempts: 3
  backoff: [30s, 60s, 120s]
  allowRollback: true
  skipAfter: 3 failures

Set by Deployment Orchestrator Agent or inferred from PipelineSnapshot.yaml.

✅ Summary¶

Rollback and recovery flows in the DevOps cluster are:

Agent-driven and policy-aware
Traceable and memory-persistent
Escalatable via Studio
Integrated with observability, metrics, and Studio UI

This ensures ConnectSoft can deploy at scale with confidence, knowing every failure has a safe, explainable path to recovery.

⚡️ Typical Agent Activation Triggers¶

Agents in the DevOps cluster do not run continuously — they are activated based on declarative events, trace state transitions, or upstream task completions. This makes them:

Efficient
Coordinated
Reentrant (support retries)
FSM-compatible

This section outlines what activates each agent, ensuring a fully event-driven, trace-guided execution model.

🟢 Common Trigger Types¶

Type	Description
🧠 Trace FSM Event	Triggered by the planner or orchestrator FSM reaching a release or delivery state
📨 Agent Emitted Event	Triggered when another agent emits an event like `CommitReady`, `PipelineReady`, `ReleaseApproved`
📝 Memory State Change	Triggered when a required file (e.g., `PipelineSnapshot.yaml`) is written to memory
👥 Human Studio Action	Triggered when a Studio user approves, rejects, or requests a rollback
⏱ Scheduled/Timed Retry	Triggered after a delay or backoff in retry window (e.g., post-deploy cooldown)

📘 Trigger Table per Agent¶

Agent	Trigger(s)
Code Committer Agent	`TraceInitialized`, `ModuleGenerated`, `StudioCommitRequest`
Pull Request Creator Agent	`CommitReady`, Studio PR request
DevOps Engineer Agent	`PullRequestCreated`, `InfraProvisioned`
Cloud Provisioner Agent	`ReleaseIntentDeclared`, blueprint with `needsInfrastructure = true`
Release Manager Agent	`PipelineReady`, `TestsPassed`, `ReleaseIntentDeclared`
Deployment Orchestrator Agent	`ReleaseApproved`, `InfraProvisioned`

🧭 Orchestrator FSM Integration (YAML Snippet)¶

states:
  module_ready:
    on:
      CommitReady:
        target: pr_created

  pr_created:
    invoke:
      agent: devops-engineer

  pipeline_ready:
    on:
      TestsPassed:
        target: release_evaluation

  release_evaluation:
    invoke:
      agent: release-manager

  release_approved:
    invoke:
      agent: deployment-orchestrator

Each agent is invoked in response to an FSM state or an upstream event.

🔁 Retryable Trigger Conditions¶

Agent	Retry Logic
DevOps Engineer	Retry on invalid pipeline with fix suggestion
Release Manager	Retry if freeze expires or approval arrives
Deployment Orchestrator	Retry deployment if health probe fails and retry policy allows

Each retry re-invokes the agent with updated memory and trace context.

📘 Studio Manual Triggers (Optional)¶

“Deploy Now” → triggers Deployment Orchestrator Agent
“Rebuild Pipeline” → triggers DevOps Engineer Agent
“Approve Release” → activates Release Manager Agent with status = ready_for_approval
“Provision Environment” → activates Cloud Provisioner Agent for custom environments

✅ Summary¶

Agents in this cluster are activated via trace-based events and orchestrated state transitions, ensuring:

Modular execution
Policy-bound automation
Manual override paths
Full observability on what triggered what and when

This supports ConnectSoft’s vision of safe, traceable, on-demand delivery automation.

🧠 FSM and Orchestrator Integration¶

In the ConnectSoft AI Software Factory, finite state machines (FSMs) act as the orchestration backbone for trace execution. Each agent in the DevOps cluster:

Is invoked as part of a state transition
Emits events that advance the release/deploy FSM
Writes memory that triggers new states or agents
Ensures delivery is traceable, deterministic, and restartable

🔄 FSM = Agent Activation Logic + Control Flow¶

FSMs define when an agent should be invoked, based on:

Memory state
Upstream events
TraceId phase
Retry logic
Human approval

They also react to agent results, transitioning between stages like release_ready, release_approved, deploy_completed, etc.

📘 Example FSM Fragment: Delivery Flow¶

states:
  trace_initialized:
    on:
      ModuleGenerated:
        target: commit_ready

  commit_ready:
    invoke:
      agent: code-committer
    onDone:
      target: pull_request_created

  pull_request_created:
    invoke:
      agent: pull-request-creator
    onDone:
      target: pipeline_ready

  pipeline_ready:
    invoke:
      agent: devops-engineer
    onDone:
      target: release_evaluation

  release_evaluation:
    invoke:
      agent: release-manager
    onDone:
      target: release_approved
    onError:
      target: release_rejected

  release_approved:
    invoke:
      agent: deployment-orchestrator
    onDone:
      target: deployment_completed

🔁 Orchestrator Loop With Memory Watch¶

FSM transitions are often triggered by memory updates:

PipelineSnapshot.yaml written → triggers Release Manager Agent
ReleaseApproved event emitted → triggers Deployment Orchestrator Agent
DeploymentSummary.json finalized → triggers Observability Agent

FSMs observe these files and events per traceId.

🎯 Benefits of FSM Coordination¶

Benefit	Result
✅ Deterministic flows	Releases can't skip required steps
✅ Idempotent execution	Agent retries won’t double-trigger downstreams
✅ Replayable flows	Restart from any state (e.g., `release_ready → release_approved`)
✅ Parallel execution	FSMs allow multiple traces to progress independently
✅ Memory-driven routing	No hardcoded APIs — everything flows via trace state and memory artifacts

🧩 FSM → Agent Invocation Metadata¶

Each agent receives injected metadata:

{
  "traceId": "trace-2025-05-23-0012",
  "fsmState": "release_evaluation",
  "step": 5,
  "retries": 1,
  "tenantId": "vetclinic-001",
  "moduleId": "booking-service"
}

This ensures scoped, contextual execution per invocation.

📦 Transition Events Generated by Agents¶

Agent	Event	Consumed By
Code Committer	`CommitReady`	PR FSM
PR Creator	`PullRequestCreated`	CI/CD FSM
DevOps Engineer	`PipelineReady`	Release FSM
Release Manager	`ReleaseApproved` or `ReleaseRejected`	Deployment FSM
Deployment Orchestrator	`DeploymentCompleted`, `RollbackTriggered`	Observability FSM

✅ Summary¶

The DevOps cluster is FSM-native by design. Every agent:

Acts when its FSM state is reached
Writes output or events that progress the FSM
Is memory- and trace-aware
Supports retries, replays, and rollback coordination

This ensures ConnectSoft’s delivery pipeline is modular, recoverable, and orchestrated with zero ambiguity.

🧑‍💻 Developer Experience and Autonomy¶

The ConnectSoft Factory is not just about automation — it's about enabling developers to ship with zero friction. The DevOps, Deployment, and Delivery cluster is designed to:

Eliminate manual CI/CD setup
Auto-generate deployable infrastructure
Empower self-service deployments via Studio or CLI
Prevent release confusion with traceable approvals

This section covers how developers interact with, benefit from, and optionally control this cluster.

🛠 Developer Autonomy Features¶

Feature	Enabled By
Auto-generated CI/CD pipelines	DevOps Engineer Agent creates `azure-pipelines.yaml`, `Dockerfile`, and test harnesses
Zero manual infra scripting	Cloud Provisioner Agent sets up environments, namespaces, vaults, DNS
Studio-triggered commit/PR	Developers can request trace commits and PRs without leaving Studio
Role-based approval	Product Owners approve via Studio or CLI; no DevOps bottleneck
Preview deployment plans	`PipelineSnapshot.yaml`, `RolloutPlan.yaml` visible in Studio
Self-service rollback (optional)	Developers may trigger rollback if authorized (future path)

👨‍💻 Sample Developer Flow¶

Developer uses Studio to generate a new service trace
ConnectSoft:
- Commits generated code (Code Committer Agent)
- Opens PR with changelog (Pull Request Creator Agent)
- Generates pipelines (DevOps Engineer Agent)
- Provisions AKS + Key Vault (Cloud Provisioner Agent)
Developer sees pipeline preview in Studio
Once approved, deploys to staging via Release Manager Agent
Can view health, rollbacks, and logs post-deploy

All of this is automated, trace-bound, and permission-controlled.

🧠 Studio UI: Developer Touchpoints¶

Action	Result
“Generate & Commit”	Triggers Code Committer Agent
“Open PR”	Triggers Pull Request Creator Agent
“Build + Deploy to Staging”	Triggers DevOps + Release Manager + Deployer
“Approve Production” (if role allows)	Authorizes release
“View Pipeline Plan”	Opens `PipelineSnapshot.yaml` preview
“Download Deployment Summary”	Exports `DeploymentSummary.json` post-release

🧪 Local Dev Options (Future Extensions)¶

Capability	Description
`connectsoft dev preview`	Preview generated Dockerfile, K8s manifests, pipeline file
`connectsoft run test`	Run generated tests locally with emulated infra
`connectsoft diff deploy`	Show what would change on next deploy
Studio integration with Codespaces	Future: One-click dev environment with trace context pre-loaded

✅ Summary¶

The DevOps cluster transforms the developer experience by:

Automating every delivery artifact
Exposing deployment logic in Studio
Enabling approvals and deploys as self-service
Making every step observable, reproducible, and auditable

It enables a 10x developer experience, turning ideas into production safely, consistently, and without operational dependency.

🛡 Cluster Responsibilities in Compliance Mode¶

In regulated environments (e.g., healthcare, finance, government, or enterprise SaaS), the factory must operate under stricter constraints:

Approval policies
Artifact signatures
Rollback guarantees
Immutable audit trails
Freeze and escalation windows

The DevOps, Deployment, and Delivery cluster adapts automatically when a project, tenant, or environment is flagged as compliance-critical.

🔐 Compliance Mode Trigger Conditions¶

Trigger Type	Examples
Tenant Classification	`tenantId = vetclinic-001` is marked as `regulated = true`
Environment Policy	`environment = production` in a regulated trace
Blueprint Flag	`complianceMode: true` in trace or module definition
Studio Toggle	Studio admin enables compliance for project/trace

🔁 Changes to Agent Behavior¶

Agent	Compliance Adaptation
Code Committer Agent	Signs commits with GPG or hash stamp; enforces commit message policy
Pull Request Creator Agent	Validates branch policy (e.g., must target `main`)
DevOps Engineer Agent	Generates signed pipeline YAML; blocks CI if unsigned artifacts found
Cloud Provisioner Agent	Enforces naming policies, vault access scoping, encryption
Release Manager Agent	Requires 2+ approvers, time delay validation, justification logging
Deployment Orchestrator Agent	Requires signed images, SBOM validation, and rollback plan injection

📘 Example: Release Policy in Compliance Mode¶

releasePolicy:
  environments:
    production:
      requiresApproval: true
      roles: ["ReleaseManager", "SecurityReviewer"]
      minDelayBeforeDeploy: 10m
      requireSignedArtifacts: true
      requireJustification: true

🧾 Required Artifacts in Compliance Traces¶

File	Purpose
`ApprovalRecords.yaml`	Logs approver identity, role, timestamp, and rationale
`ReleaseSummary.json`	Includes validation hashes, policy version, trace lineage
`PipelineSnapshot.yaml`	Records CI/CD stages, sign-offs, artifact signatures
`DeploymentSummary.json`	Captures probes, failures, restarts, rollback actions
`SBOM.json` (optional future)	Lists open-source packages and licenses

⏳ Enforced Controls¶

Control	Result
Change freeze	Prevents deploys during blackout windows
Delay window	Minimum wait after approval before deployment starts
Justification required	Approval UI prompts user to write reason for production release
Immutable logs	Release + deployment logs are locked after deploy
Studio audit export	Timeline + artifacts can be bundled for compliance report archive

🔒 Compliance Logging Strategy¶

Channel	Logging Target
Studio Timeline	Event annotations with compliance labels
JSON Logs	Structured with `compliance=true`, `approver`, `policyVersion`
Prometheus	`release_compliance_mode_total`, `approval_justification_missing_total`
Memory	Permanent records under `compliance/trace-xxx/` folders

✅ Summary¶

When compliance mode is enabled, the DevOps cluster:

Hardens every release path
Requires multi-role approvals
Emits tamper-proof logs
Validates every artifact and signature
Generates a machine-verifiable trace of policy adherence

It transforms the software factory from a fast shipper into a trustworthy, regulator-ready delivery pipeline.

📈 Diagram: Cluster Lifecycle¶

This cluster-level diagram provides a bird’s-eye view of how all six DevOps-related agents:

Collaborate in sequence
Pass control via events and memory
Enforce gates
Activate downstreams

It ties together the commit → PR → pipeline → release → deploy lifecycle.

🌐 Mermaid Diagram – DevOps Cluster Flow¶

flowchart TD
    subgraph CODE
        A1[Code Committer Agent]
        A2[Pull Request Creator Agent]
    end

    subgraph PIPELINE
        B1[DevOps Engineer Agent]
        B2[Cloud Provisioner Agent]
    end

    subgraph RELEASE
        C1[Release Manager Agent]
    end

    subgraph DEPLOY
        D1[Deployment Orchestrator Agent]
    end

    A1 -->|Emit: CommitReady| A2
    A2 -->|Emit: PullRequestCreated| B1
    B2 -->|Emit: InfraProvisioned| B1
    B1 -->|Emit: PipelineReady| C1
    C1 -->|Emit: ReleaseApproved| D1
    D1 -->|Emit: DeploymentCompleted| E1[Observability Cluster]

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

🔁 Flow Breakdown by Phase¶

Phase	Description
Code	Code Committer and PR Creator agents manage Git and change tracking.
Pipeline	DevOps Engineer builds pipeline logic; Cloud Provisioner ensures infra is ready.
Release	Release Manager validates readiness, approvals, policies.
Deploy	Deployment Orchestrator performs rollout and health checks.
Post-Deploy	Observability cluster validates behavior post-deployment.

🧠 Memory Write Points¶

PipelineSnapshot.yaml (by DevOps Engineer Agent)
EnvironmentConfig.yaml (by Cloud Provisioner Agent)
ReleaseSummary.json and ApprovalRecords.yaml (by Release Manager Agent)
DeploymentSummary.json (by Deployment Orchestrator Agent)

These artifacts are persisted under memory/trace-{traceId}/.

🎯 Event Transitions¶

Event	Triggers
`CommitReady`	→ Pull Request Creator Agent
`PullRequestCreated`	→ DevOps Engineer Agent
`InfraProvisioned`	→ DevOps Engineer Agent
`PipelineReady`	→ Release Manager Agent
`ReleaseApproved`	→ Deployment Orchestrator Agent
`DeploymentCompleted`	→ Observability Agent / Studio UI updates

🧩 Studio + FSM Alignment¶

Studio visualizes each phase with:

Status icons (🟢 approved, 🟡 delayed, 🔴 failed)
Linked memory artifacts
Approver and reviewer annotations
Timeline synchronization per traceId

✅ Summary¶

This diagram illustrates how DevOps agents function as a coordinated, event-driven delivery system, with:

Modular agent roles
Trace-linked memory contracts
Event-driven sequencing
Studio + observability alignment

It embodies the ConnectSoft principle of autonomous, policy-safe, traceable delivery at scale.

✅ Summary & Future Evolution¶

The DevOps, Deployment, and Delivery Agents form the execution backbone of the ConnectSoft AI Software Factory. They:

Transform committed code into production-grade deployments
Govern CI/CD, infrastructure, release approval, and deployment
Coordinate through events, memory, FSMs, and Studio
Operate per trace, per tenant, per environment
Enforce observability, rollback, and compliance safety

🧠 Cluster Capabilities at a Glance¶

Capability	Owned By
Commit + PR creation	Code Committer, PR Creator
CI/CD pipeline generation	DevOps Engineer Agent
Infrastructure provisioning	Cloud Provisioner Agent
Release gating and approvals	Release Manager Agent
Deployment execution	Deployment Orchestrator Agent
Studio status and rollback UI	Shared via all agents

🧩 Key Attributes¶

🧠 Trace-driven — all memory, logs, and flows scoped per traceId
🔐 RBAC-governed — approvals and actions restricted by role, tenant, and environment
📡 Fully observable — emits OTEL spans, Prometheus metrics, timeline events
♻️ Retry + rollback safe — all agents are reentrant, with structured recovery
🧾 Compliant by design — supports multi-approver workflows, signed artifacts, change freezes
🧩 FSM-integrated — all actions invoked declaratively via orchestrated state machines

🔭 Future Evolution Opportunities¶

1. 🧪 Test-Orchestrated Deployment Pipelines¶

Add intermediate QA validation before promotion (e.g., canary tests, chaos tests)

2. 🧾 Declarative Release Plans¶

Introduce ReleasePlan.yaml authored or generated during planning stage
Parsed by Release Manager and Deployer for smarter gating + routing

3. 📉 AI-Assisted Deployment Risk Scoring¶

Feed past failure rates, metrics, and service volatility into approval logic
Block or escalate based on calculated risk index

4. ☁️ Multi-Cloud Provisioning Support¶

Extend Cloud Provisioner to support GCP, AWS in addition to Azure
Use cloud profiles per tenant or edition

5. 🔐 SBOM and License Policy Enforcement¶

Release Manager validates open source package list before approving to prod
Deployment Orchestrator blocks releases with high-risk licenses

6. 🧭 Release Simulation Mode¶

Run dry-runs of entire delivery process per trace
Visualize outcomes, approval paths, risk gates, and deployment shape before execution

🎯 Final Statement¶

The DevOps cluster doesn’t just automate — it governs delivery, scales execution, and makes production predictable.

It is where software becomes a real product, shipped with confidence, auditability, and autonomy — every time, for every tenant, at scale.