Timing, Milestones, and Sprint Management in ConnectSoft AI Software Factory¶

🌀 Factory Philosophy on Time: Logical Time vs Calendar Time¶

In the ConnectSoft AI Software Factory, time is not merely a calendar. It is a logical orchestration layer — driven by events, blueprints, FSM states, and agent readiness — not wall-clock scheduling.

⏱️ Two Modes of Time¶

Time Model	Description
Logical Time	Internal progression of state machines, agent flows, and milestone transitions. Based on `event → state → agent → output`.
Calendar Time	Human-centric time (real dates, weeks, deadlines). Used only for dashboards, governance views, or syncing with stakeholders.

In the Factory, logical sprints can start, execute, and close within seconds — but still represent full units of traceable work.

⚙️ Logical Time: The Orchestration Primitive¶

Logical time is represented by:

FSM state transitions in orchestrators
Event emissions like SprintPlanned, SprintClosed, MilestoneReached
Agent execution timestamps from observability telemetry
Artifact readiness markers (e.g., blueprint emitted, PR created)

No fixed duration is assumed — instead, logical time progresses when:

Preconditions are met
Agents emit expected events
Artifacts are validated and versioned
Observability signals confirm flow completion

📘 Why Logical Time Wins¶

Feature	Benefit
Parallelism	Multiple sprints can be planned and executed concurrently by different agents.
Speed	A milestone can contain work done in seconds — but still validated, versioned, and orchestrated.
Traceability	Logical time is captured in telemetry: `traceId`, `executionId`, `agentId`, `status`.
Repeatability	Sprints can be re-run deterministically from trace snapshots.

📊 Time Mapping in Studio Dashboards¶

Although execution is logical, human users can view:

Time Element	Source
Sprint Start Time	First `SprintPlanned` event timestamp
Milestone Duration	Time between `MilestoneStarted` and `MilestoneCompleted`
Agent Execution Time	`execution-metadata.json → durationMs`
CI/CD Runtime	Derived from build logs and `ReleaseTriggered` events

Studio presents these using calendar time visualizations, but the underlying orchestration remains logical.

📎 Implications for Multi-Agent Planning¶

Agents plan based on flow dependencies, not dates.
Orchestrators resume paused sprints once input events are received.
Milestones advance only when output conditions are validated.

This enables true elastic, autonomous execution — even across thousands of modules.

✅ Summary¶

ConnectSoft AI Factory operates on logical time.
Calendar time is secondary and used only for visualization and governance.
Every step, sprint, and milestone is event-triggered, traceable, and observable — not date-driven.

🌀 Milestones as Delivery Units¶

A milestone in ConnectSoft is a logically scoped unit of software progress. It defines what must be completed together, what agents are expected to contribute, and how outputs are validated and connected.

In the Factory, a milestone is not a date — it is a traceable state transition of system maturity.

🧱 Milestone Characteristics¶

Characteristic	Description
Atomic Scope	Contains a set of features, services, libraries, or artifacts that must be completed together.
Agent-Orchestrated	Tied to agent flows that produce and verify milestone outputs.
Observable	Emits `MilestoneStarted`, `ArtifactProduced`, `MilestoneCompleted` events.
Blueprint-Bound	Anchored to specific execution contexts, blueprints, or trace IDs.

🧩 YAML Format: `milestones.yaml`¶

Each Factory project includes a structured YAML file listing milestones:

milestones:
  - id: mvp-ready
    name: MVP Delivery
    description: >
      All components needed for internal testing and stakeholder demo.
    requiredArtifacts:
      - BookingService
      - UserPortal
      - AuthServer
      - EventContracts
      - DeploymentPlan
    gateConditions:
      - allPullRequestsMerged
      - allTestsPass
      - blueprintValidated
    triggers:
      - event: MilestoneCompleted
        next: planNextSprint
    traceability:
      labels: [ "sprint:1", "mvp", "qa:test" ]
      owner: "product-owner"

⚙️ Orchestration Effects of Milestones¶

Milestones act as coordination gates inside the Factory:

Trigger agent execution: When a milestone is entered, orchestrators emit events to activate relevant agents.
Track progress via events: Each agent emits ArtifactReady, PullRequestCreated, ValidationPassed events tagged with the milestone ID.
Control transition to next phases: FSMs advance only when all gateConditions are satisfied.

📊 Visualization in Studio¶

Milestones are rendered in the Studio timeline:

UI Element	Source Field
Milestone Name	`name` from `milestones.yaml`
Status	Derived from gate conditions and trace logs
Linked Artifacts	From `requiredArtifacts` and emitted events
Completion Time	Timestamp of `MilestoneCompleted` event
Assigned Owner	From `traceability.owner`

🧠 Why Milestones Matter¶

Purpose	Description
Delivery Alignment	Organize agent outputs into coherent, testable, verifiable phases.
Automation Boundaries	Define when to pause/resume orchestration.
Human Oversight Points	Enable checkpoints for Studio-based review or override.
Security and Audit Hooks	Anchor security scanning, compliance tests, and artifact versioning.

✅ Summary¶

Milestones are event-driven coordination points — not date-based.
Defined in milestones.yaml with triggers, gate conditions, and artifact dependencies.
Drive agent orchestration, artifact tracking, and traceable delivery flows.

🎯 Sprints as Scoped Execution Windows: Controlling Agent Flows¶

In the Factory, a sprint is a logical, bounded execution window during which a defined scope of work is orchestrated and completed by agents.

A sprint is not a 2-week calendar box — it’s a scoped state of coordinated agent activity, triggered by events and governed by milestone goals.

⚙️ Sprint = Execution Envelope¶

Element	Meaning in the Factory
Sprint Scope	The set of features, services, and agent executions assigned to the sprint.
Sprint Trigger	Initiated by `SprintPlanned` event or milestone transition.
Sprint Lifecycle	Controlled via events: `SprintStarted`, `SprintCheckpoint`, `SprintClosed`.
Sprint Boundaries	FSM-defined – not clock-defined. Ends when outputs are produced and validated.

🧬 Sprint Metadata Structure¶

sprints:
  - id: sprint-1
    name: MVP Sprint
    goal: Deliver first fully testable Booking flow
    includes:
      - Milestone: mvp-ready
      - Features: [BookingService.Create, Notification.Send, User.Onboarding]
      - Agents:
          - ProductOwner
          - BackendDeveloper
          - TestGenerator
          - DevOpsEngineer
    trigger: MilestoneStarted
    traceability:
      labels: [ "mvp", "first-run", "sprint:1" ]
      orchestrator: SprintCoordinator

🧠 Sprint-Driven Execution Flow¶

flowchart TD
  A[🟢 SprintPlanned]
  A --> B[🧠 Activate Agents]
  B --> C[💡 Feature Handlers Generated]
  C --> D[🔧 Code Committed]
  D --> E[✅ Tests and Validation Passed]
  E --> F[📦 SprintClosed + Artifacts Tagged]

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

→ Every step emits structured observability metadata (e.g., executionId, traceId, agentId, sprintId).

🧩 Orchestration Role of a Sprint¶

Binds scope: Agents know what to work on (from feature and milestone linkage).
Controls flow: FSMs pause execution outside the sprint scope.
Applies policies: Sprint definitions can include compliance, validation, and test criteria.
Supports checkpointing: Mid-sprint evaluation and retry triggers.

📊 Sprint Monitoring in Studio¶

Dashboard Element	Data Source
Sprint ID and Goal	From `sprints.yaml`
Active Agents	Tracked via `AgentExecutionStarted`
Traceable Artifacts	From `execution-metadata.json`
Completion Status	Based on `SprintClosed` + validations
Performance Metrics	Agent durations, error counts, retries

✅ Summary¶

Sprints are logical execution windows, scoped by feature/milestone/agent clusters.
They drive orchestration boundaries, enable structured planning, and allow traceable execution monitoring.
All activity within a sprint is observable, versioned, and agent-triggered — not timeboxed.

🎯 Planning Is a Collaborative, Multi-Agent Flow¶

In the ConnectSoft AI Software Factory, sprint and milestone planning is not hardcoded logic — it's a coordinated effort between specialized agents, each contributing its scoped expertise to prepare and manage execution.

Planning is an agentic negotiation — where each role contributes decisions, not just follows them.

🧠 Core Planning Agents and Their Roles¶

Agent	Role in Sprint & Milestone Planning
Product Owner Agent	Defines scope, constraints, and acceptance criteria for the sprint. Emits `SprintPlanned` and `FeatureScopeDefined` events.
Product Manager Agent	Breaks down the vision into features, epics, user stories. Generates priority metadata, tags, and delivery themes.
QA Agent	Validates test coverage requirements per milestone. Ensures traceability of `feature → test → validation`.
DevOps Engineer Agent	Verifies pipeline readiness, CI/CD stage availability, and resource constraints for deployability.

🧬 Planning Flow (Event-Driven)¶

sequenceDiagram
  participant PM as Product Manager Agent
  participant PO as Product Owner Agent
  participant QA as QA Agent
  participant DevOps as DevOps Agent
  participant Orchestrator

  PM->>PO: Emit `FeatureScopeDefined`
  PO->>Orchestrator: Emit `SprintPlanned`
  Orchestrator->>QA: Validate `testTraceMatrix`
  Orchestrator->>DevOps: Validate `ci/stage readiness`
  QA-->>Orchestrator: Emit `SprintTestPlanReady`
  DevOps-->>Orchestrator: Emit `SprintInfraReady`
  Orchestrator->>All: Emit `SprintReadyToStart`

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

📦 Outputs Produced During Planning¶

Agent	Output Artifact
Product Owner	`sprints.yaml`, trace-scoped tags, scope acceptance criteria
Product Manager	`features.yaml`, `epics.md`, priority labels, story metadata
QA Agent	`sprint-trace-matrix.json`, `feature-test-link.yaml`, validation checklist
DevOps Agent	`pipeline-status.yaml`, environment health report, `deployment-constraints.yaml`

🧩 FSM State Transition Model¶

The Sprint Orchestrator FSM uses these states:

stateDiagram-v2
  [*] --> Planning
  Planning --> ValidatingScope
  ValidatingScope --> ValidatingInfra
  ValidatingInfra --> Ready
  Ready --> Executing

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

Each transition is gated by agent-completed outputs or emitted readiness events.

🧠 Human-In-The-Loop Overrides¶

Even in AI-first planning, humans can:

Approve sprint scope or revise it in Studio UI
Override DevOps gating rules for MVP
Trigger ResumePlanning event manually

→ This creates hybrid governance while preserving full traceability.

✅ Summary¶

Planning involves multiple agents working in coordination via events.
Each agent produces structured artifacts that enable scoped, testable, and deployable sprints.
Orchestration is driven by agent outputs — not static planning boards or ticket systems.

🧠 Events That Drive Execution¶

In the ConnectSoft AI Software Factory, all agent execution, orchestration transitions, and milestone tracking are event-driven. These events serve as control signals, not just logs — they drive when, how, and which agents act.

If it matters, it emits an event. If it emits an event, something in the Factory will respond.

📬 Core Sprint Lifecycle Events¶

Event	Description
`SprintPlanned`	Marks the formal start of a logical sprint. Contains `sprintId`, `features`, `milestoneRef`, and `agentRoles`.
`SprintStarted`	Emitted by the orchestrator when all gate conditions (test, infra, scope) are met.
`SprintCheckpoint`	Optional midpoint emitted after `n` features are completed. Used for review or pause/resume workflows.
`SprintClosed`	Emitted when all outputs are trace-verified. Triggers versioning, tagging, deployment snapshotting.
`PlanningResumed`	Restarts planning after a pause, new scope, or stakeholder input. Re-activates ProductOwnerAgent.

🔄 Execution Timeline Flow¶

sequenceDiagram
  participant Studio
  participant ProductOwner
  participant Orchestrator
  participant Agents

  Studio->>ProductOwner: Request new sprint
  ProductOwner->>Orchestrator: Emit SprintPlanned
  Orchestrator->>Agents: Activate roles
  Agents->>Orchestrator: Emit ArtifactReady / TestsPassed
  Orchestrator->>Studio: Emit SprintCheckpoint
  Agents->>Orchestrator: Emit CompletionEvents
  Orchestrator->>Studio: Emit SprintClosed

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

🧩 Event Payloads (Simplified)¶

`SprintPlanned` Event¶

{
  "event": "SprintPlanned",
  "sprintId": "sprint-2025-05-MVP1",
  "milestone": "mvp-ready",
  "features": ["BookingFlow", "Notifications"],
  "agents": ["ProductOwner", "BackendDeveloper", "TestGenerator"],
  "timestamp": "2025-05-12T10:00:00Z"
}

`SprintClosed` Event¶

{
  "event": "SprintClosed",
  "sprintId": "sprint-2025-05-MVP1",
  "completedArtifacts": ["BookingService", "Tests", "PRs"],
  "versionTag": "mvp-ready-v1",
  "traceId": "trace-abc123"
}

🧠 What Triggers These Events?¶

Event	Triggering Condition
`SprintPlanned`	ProductOwnerAgent emits plan based on milestone + features
`SprintStarted`	Orchestrator verifies all readiness checks (QA, Infra)
`SprintClosed`	All scoped outputs exist, validated, trace-linked
`PlanningResumed`	Manual input, failed scope, or new `VisionUpdate`

🔐 Observability Links¶

Each of these events includes:

traceId, sprintId, agentId, moduleId
Timestamp
Optional executionId, reason, durationMs

→ All are ingested into Studio dashboards and trace visualizations.

✅ Summary¶

The Factory is driven by lifecycle events, not calendar time or hardcoded steps.
Events like SprintPlanned, SprintClosed, and PlanningResumed act as control switches for agent flows.
They enable asynchronous, traceable, parallel execution across agents and modules.

🌀 Traceability and Observability Links¶

In the ConnectSoft AI Software Factory, everything produced during a sprint is traceable — from the initial idea to the final pull request, container image, or test result.

“If it can’t be traced to a sprint, milestone, or agent — it didn’t happen.”

Traceability ensures that:

Sprints are auditable
Failures are debuggable
Progress is measurable
Output is governable

🧩 What Is Traced?¶

Artifact Type	Trace Anchors Included
Generated Code	`traceId`, `sprintId`, `featureId`, `agentId`, `skillId`
Pull Requests	Git commit message tags, branch naming: `feature/booking/sprint-3`
Tests and Coverage	`test-matrix.json` links features to SpecFlow scenarios
Containers and Builds	`imageId`, `buildId`, `sprintLabel`, `milestoneTag`
Docs and Diagrams	Metadata block in `execution-metadata.json`, trace-linked
Observability Logs	Structured logs contain trace context, status, and span IDs

📘 Example: `execution-metadata.json`¶

{
  "traceId": "trace-d8c71234",
  "sprintId": "sprint-2025-05-MVP1",
  "featureId": "BookingFlow",
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "status": "Success",
  "durationMs": 1278,
  "artifact": "BookAppointmentHandler.cs"
}

📈 Metrics That Link to Sprints¶

Agents and orchestrators emit Prometheus-style metrics per sprint/module:

agent_execution_duration_seconds{
  agentId="test-generator",
  sprintId="sprint-2025-05-MVP1",
  featureId="Notifications",
  status="Success"
} 2.17

These metrics are tagged and grouped by:

sprintId
traceId
agentId
moduleId
status

→ Used in Grafana dashboards, Studio heatmaps, and alerting policies.

📊 Sprint Trace Matrix¶

Each sprint includes an internal artifact called sprint-trace-matrix.json:

{
  "sprintId": "sprint-2025-05-MVP1",
  "features": [
    {
      "id": "BookingFlow",
      "handlers": ["BookAppointmentHandler.cs"],
      "tests": ["book-appointment.feature"],
      "commits": ["f23f123", "a91d3a8"],
      "imageId": "booking-service:1.0.0-sprint3"
    }
  ]
}

→ Enables complete reverse tracing from runtime or test to originating plan.

📎 Git Strategy for Traceability¶

Element	Format Example
Branch Name	`feature/booking/sprint-3`
Commit Msg	`Add BookAppointmentHandler [trace:trace-d8c7 sprint:sprint-3]`
Tags	`mvp-ready-v1`, `sprint-3-complete`

→ These tags link DevOps pipelines and artifacts back to their planning phase.

🧠 Studio Views¶

The Studio UI provides:

Sprints Dashboard: View features, commits, PRs, test coverage, and agent metrics per sprint
Trace Explorer: Navigate from sprint → feature → agent execution → logs → code
Artifact Timeline: View milestone scope and traceable files generated during the sprint
Coverage Heatmap: Which features had full/partial/no test or deployment linkage

✅ Summary¶

Every output from a sprint — code, test, build, log, image — is trace-tagged
Observability spans, Git commits, and metrics form an integrated trace graph
This ensures governance, auditability, and validation across agentic execution

📦 Artifacts: `milestones.yaml`, `sprint-trace-matrix.json`, tags, metadata¶

In the ConnectSoft AI Software Factory, milestones and sprints are not just logical concepts — they are backed by versioned, structured artifacts that drive orchestration, traceability, and auditability.

If it's not in an artifact, it doesn't exist in the Factory. If it can't be parsed, it can't be orchestrated.

📘 Artifact #1: `milestones.yaml`¶

This file defines all milestone boundaries, gate conditions, trace rules, and output expectations.

✅ Example¶

milestones:
  - id: mvp-ready
    name: MVP Delivery
    description: Deliver core services for internal test and review.
    requiredArtifacts:
      - BookingService
      - NotificationService
      - UserPortal
    gateConditions:
      - allPullRequestsMerged
      - testsPassed
      - sprint-trace-matrix.complete
    versionTag: mvp-v1
    owner: product-owner
    triggers:
      - event: MilestoneCompleted
        next: deploy-to-staging

🧩 Parsed by orchestrators and used to decide:

Agent activation boundaries
Deployment version tags
Conditions to emit MilestoneCompleted

📘 Artifact #2: `sprint-trace-matrix.json`¶

Auto-generated and continuously updated during sprint execution. Links features → agents → files → commits → tests → images.

✅ Example¶

{
  "sprintId": "sprint-2025-05-MVP1",
  "features": [
    {
      "id": "BookingFlow",
      "traceId": "trace-abc123",
      "files": [
        "BookAppointmentHandler.cs",
        "booking-api.feature"
      ],
      "commits": [
        "git:a1b2c3",
        "git:d4e5f6"
      ],
      "imageId": "booking-service:1.0.3",
      "coverage": "94%"
    }
  ]
}

🧠 Used for:

Agent execution validation
Observability dashboards
End-of-sprint review automation
Build artifact version tagging

📘 Artifact #3: Git Tags and Metadata¶

Every module, PR, test, and image is tagged and trace-linked via standard GitOps practices.

Tag Type	Format Example	Description
Feature Tag	`feature:booking`	Ties PR to a blueprint feature ID
Sprint Tag	`sprint-3-complete`	Added at sprint closure for grouping
Milestone Tag	`mvp-ready-v1`	Used on releases and Docker images
Trace ID Tag	`trace:trace-abc123`	Optional for artifact → blueprint link

→ Tags are used in pipelines, dashboards, and orchestration FSM checkpoints.

📘 Artifact #4: `execution-metadata.json`¶

Generated per agent skill execution.

✅ Example¶

{
  "agentId": "backend-developer",
  "skillId": "GenerateHandler",
  "traceId": "trace-abc123",
  "sprintId": "sprint-2025-05-MVP1",
  "featureId": "BookingFlow",
  "status": "Success",
  "durationMs": 1842,
  "output": "BookAppointmentHandler.cs"
}

🧠 Used for:

Execution trace audit
Observability correlation
Failure diagnostics and retries

🔁 Where These Artifacts Live¶

Artifact	Storage Location
`milestones.yaml`	In Git under `/planning/`
`sprint-trace-matrix.json`	In `/execution/` folder per sprint
Git tags	Version control system (Azure DevOps)
`execution-metadata.json`	Per-module `/metadata/` folder

Studio pulls from all sources to render dashboards and health checks.

✅ Summary¶

All sprints and milestones produce versioned artifacts — no implicit coordination.
These include human-readable (yaml), machine-consumable (json), and trace-encoded (Git tags) data.
Orchestration logic, observability dashboards, and deployment rules all depend on these artifacts.

🎯 How Backlog Is Managed Across Milestones¶

In the ConnectSoft AI Software Factory, the backlog is not a fixed ticket list — it's a dynamic, traceable pool of features, stories, and capabilities, scoped by blueprints and partitioned into milestones.

The backlog is fluid — bounded by milestones, refined by agents, and reshaped by factory events.

📘 Where the Backlog Comes From¶

The Product Manager Agent and Business Analyst Agent generate the backlog from:

Vision blueprints
Use case narratives
Feature decompositions
Customer personas
Previous milestone feedback loops

Each backlog item is defined as:

- featureId: Notifications.Email.Send
  title: Email confirmations for booking
  priority: P1
  requiredBy: milestone:mvp-ready
  dependsOn: Booking.Created
  estimatedEffort: 2
  agentRoles: [backend-developer, test-generator]
  status: planned

Stored in features.yaml, tagged by milestone and sprint.

🧩 How Milestones Consume the Backlog¶

Each milestone defines a scope gate:

milestones:
  - id: mvp-ready
    requiredFeatures:
      - BookingFlow
      - Notifications.Email.Send
      - Auth.Login
    allowsDeferred: true
    onDeferEmit: FeatureDeferred

The Sprint Orchestrator will:

Load milestone scope.
Query which features are ready to execute.
Emit FeatureDeferred if a feature is:
- Missing prerequisites
- Blocked by another trace
- Out of agent capacity
- Manually paused in Studio

🔁 Deferred Features and Roll-Forward¶

When a feature is deferred:

It's tagged status: deferred
A FeatureDeferred event is emitted
It’s re-scoped into the next sprint or child milestone via FSM rule

✅ No scope loss — just intentional delay, with trace continuity.

🧠 Managing Backlog Priority¶

Each feature includes:

Property	Purpose
`priority`	Affects auto-selection into early sprints (`P1` → `sprint-1`)
`requiredBy`	Enforces inclusion before a specific milestone
`effortEstimate`	Allows capacity-based sprint planning
`agentRoles`	Determines executor responsibilities
`status`	Planned → InProgress → Completed/Deferred

These are used by the Product Owner Agent to orchestrate sprint feasibility.

📊 Backlog in Studio¶

Backlog Heatmap: Shows all planned features by milestone and status
Deferred Panel: Grouped by reason and trace
Milestone Gate Warnings: Visualize feature blocking status
Sprint Scope Planner: Drag-defer features across milestones

→ Agents do the heavy lifting, Studio enables manual override.

✅ Summary¶

The backlog is blueprint-derived, milestone-scoped, and agent-owned.
Milestones declare what must be delivered, but allow deferred scope when blockers exist.
The Factory supports fluid reallocation across sprints without losing trace or context.

🧠 Resuming Planning: How the Factory Supports Iterative Scope Expansion¶

In the ConnectSoft AI Software Factory, planning is not a one-shot phase. It’s a resumable, event-triggered process that supports:

Expanding scope after feedback
Re-scoping after failures
Iterating after releases
Responding to new blueprints or updates

Planning never really ends — it pauses, waits for new signals, then resumes exactly where it left off.

🧩 Key Event: `PlanningResumed`¶

This event re-activates the planning agents:

{
  "event": "PlanningResumed",
  "reason": "New blueprint version + feature updates",
  "sprintId": "sprint-2025-06-QAHardening",
  "timestamp": "2025-05-13T08:12:34Z"
}

Emitted by:

The orchestrator after SprintClosed
Manual Studio action (“Resume Planning”)
Version update in vision.md or features.yaml

🔁 Resumption Triggers¶

Trigger Type	Example
✅ Vision Updated	New document version published (by Vision Architect Agent)
✅ Feature Backlog Grows	Features added or reprioritized
✅ Milestone Incomplete	Deferred features need re-evaluation
✅ Agent Failure or Retry	Sprint execution failed or aborted
✅ Manual Override	PM resumes planning after external review

🧠 Resumption Workflow¶

sequenceDiagram
  participant Orchestrator
  participant ProductOwner
  participant ProductManager
  participant QA
  participant Studio

  Studio->>Orchestrator: Resume Planning Clicked
  Orchestrator->>ProductOwner: Activate with `PlanningResumed`
  ProductOwner->>ProductManager: Request updated backlog plan
  ProductManager->>QA: Validate if any tests are missing
  QA-->>Orchestrator: `SprintReadinessChecked`
  Orchestrator->>Studio: Sprint `sprint-4` scope reloaded

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

📘 Artifact Change Detection¶

The orchestrator watches for diff triggers in:

vision.md
features.yaml
milestones.yaml
sprint-trace-matrix.json

→ Triggers PlanningResumed automatically if structure changes.

📊 Studio Support¶

View	Behavior
Sprint Timeline	Shows last planning pause → resumed state
Planning Status Panel	Lists reason for pause and resume trigger
Scope Comparison Viewer	Diffs last and current plan in real time
Resume Button	Triggers `PlanningResumed` manually

📦 Outputs After Resumption¶

updated-sprint-plan.yaml
resumption-report.md
deferred-to-resumed-mapping.json
New traceId chain connected to prior sprint

✅ Summary¶

Planning is resumable, triggered by events, diffs, or Studio action.
The Factory maintains stateful checkpoints so planning can continue seamlessly.
Agents re-plan only what changed, preserving velocity and avoiding duplication.

🤖 + 🧑 Human vs AI-Driven Milestone Planning: Hybrid Workflows and Governance¶

In the ConnectSoft AI Software Factory, AI agents lead milestone and sprint planning — but humans retain control over governance, prioritization, and exception handling.

Autonomy by default. Oversight by design.

This creates a hybrid workflow where agents generate plans, but human reviewers validate, adjust, or intervene when necessary.

🧠 Division of Responsibilities¶

Responsibility	AI Agent-Driven	Human-Governed
Decomposing blueprints into milestones	✅ Vision Architect Agent, Product Manager Agent
Generating sprint and milestone scope	✅ Product Owner Agent
Assigning agent roles and execution order	✅ Sprint Orchestrator FSM
Prioritizing based on business urgency	⚠️ Human input via Studio
Resolving ambiguity or misalignment	❌ Human-required
Approving milestone entry/exit	⚠️ Optional human gate
Handling external stakeholder input	❌ Requires human action

🧩 Agent Outputs for Review¶

Agents output structured plans:

milestones.yaml
features.yaml
execution-readiness-report.md
sprint-plan.yaml

→ These are sent to Studio for validation by a human Product Owner or Architect.

🧑‍💻 Human Governance Entry Points¶

Mechanism	Purpose
Studio Override Buttons	Pause planning, reject milestone, re-prioritize scope
Approval Workflow Hooks	Require signoff before `SprintStarted`
Comment Threads on Plans	Allow contextual discussion and rejection reasons
Feature Annotation	Inject `manual-defer`, `manual-priority`, `human-blocked` flags

🔁 Example: Hybrid Planning Flow¶

sequenceDiagram
  participant ProductManager
  participant ProductOwner
  participant StudioUser
  participant Orchestrator

  ProductManager->>ProductOwner: Emit `MilestonePlanCreated`
  ProductOwner->>StudioUser: Notify pending approval
  StudioUser->>Orchestrator: Reject scope for revision
  ProductOwner->>ProductManager: Regenerate plan with exclusions
  ProductManager->>Orchestrator: Emit `SprintPlanned`

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

🛡️ Governance Modes¶

Mode	Description
Autonomous	Agents plan milestones and sprints with no human gating. Used for internal test projects or rapid prototypes.
Assisted	Agent-generated plans must be approved by a human via Studio before execution. Default mode for most production services.
Manual-Only	All planning is done outside AI Factory scope (rare, discouraged). For high-risk regulatory components only.

Defined in factory-governance.yaml:

planningMode: assisted
approvalRoles:
  - role: product-owner
  - role: domain-lead

📊 Studio Support¶

Visual comparison of agent plan vs. human edits
Toggle for planningMode per project
Approve/Reject controls per milestone and sprint
Audit log of all governance overrides

✅ Summary¶

The Factory supports AI-led planning with optional human control gates.
Agents do the work, humans retain authority.
Governance is configurable, auditable, and traceable.

🎯 Studio Visualization: How Milestones and Sprint Scope Appear in Dashboards¶

The ConnectSoft Studio acts as the human interface to AI-led execution. It provides a visual and interactive layer where milestones, sprints, feature scope, and agent activity are:

✅ Traceable
✅ Verifiable
✅ Governable
✅ Debuggable

You can’t manage what you can’t see — Studio turns logical flows into visible progress.

📊 Core Visualization Concepts¶

Element	Visualized As	Data Source
Milestones	Timeline markers, stacked lanes	`milestones.yaml`, trace events
Sprints	Execution blocks inside milestone swimlanes	`sprint-plan.yaml`, FSM state
Features	Kanban-style cards with trace tags	`features.yaml`, PRs
Agent Activity	Avatars, status icons, span charts	`execution-metadata.json`
Tests	Coverage overlay badges	`sprint-trace-matrix.json`

🖼️ Example: Studio Timeline View¶

gantt
    title Sprint Timeline — MVP Phase
    dateFormat  YYYY-MM-DD
    section Milestone: MVP Ready
    Sprint 1       :active, s1, 2025-05-01, 4d
    Sprint 2       :done, s2, 2025-05-05, 4d
    Sprint 3 (QA)  :sprint3, 2025-05-09, 5d

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

Each bar links to:

Feature execution breakdown
Agent activity summary
Sprint trace matrix
CI/CD outputs and logs

📁 Studio File View with Trace Badges¶

📂 booking-service/
  ├── BookAppointmentHandler.cs [sprint:3 ✅]
  ├── booking-api.feature [sprint:3 ✅]
  ├── BookingAggregate.cs [sprint:2 ✅]
  ├── notification-client.cs [deferred 🔁]

Badges pulled from:

execution-metadata.json
Git tags (sprint:3, deferred)
Trace logs (ArtifactReady, Deferred)

📈 Agent Span Dashboard¶

Each agent has a live, traceable dashboard:

Agent	Status	Avg Duration	Failures	Active Sprint
Backend Developer	✅ Running	1.84s	0	sprint-3
Test Generator	⚠️ Retrying	2.41s	2	sprint-3
Code Committer	✅ Complete	0.77s	0	sprint-3

→ Clicking reveals span log, prompt, output, and retry path.

📊 Sprint Overview Cards¶

Sprint: sprint-3	Status: ✅ Completed
Features: 4 complete, 1 deferred
Tests: 92% coverage
Agents: 5 active
Commits: 13
Artifacts: 22
Image Tags: `mvp-v1`, `booking:1.0.3`
🔗 View sprint-trace-matrix.json →

🎛️ Interactive Controls¶

UI Element	Purpose
✅ Approve Sprint Scope	Accept or override agent-proposed features
🔁 Resume Planning	Triggers `PlanningResumed` event
⏸️ Pause Sprint	Temporarily freeze FSM execution
🔍 Diff Trace Scope	Compare this sprint to previous
🧪 Test Matrix Coverage	Highlight gaps by feature/module
📦 View Artifact Timeline	File → Sprint → Commit → Agent trace

✅ Summary¶

Studio gives humans full visibility into sprint and milestone progress
All execution data is trace-linked and visualized
Governance, debugging, and flow control are interactive and observable

🎯 Best Practices for Incremental Execution: How to Split Delivery by Phase¶

The ConnectSoft AI Software Factory is designed to operate at massive scale — across thousands of features, microservices, and tenants. To ensure reliability, traceability, and delivery velocity, work is always executed in incremental, phase-based units.

Deliver small, observable chunks. Validate continuously. Evolve safely.

🧠 Incremental Execution = Controlled Phase Progression¶

Instead of monolithic deliveries, the Factory uses phased sprint strategies, with milestones acting as gates.

Phase Type	Purpose
Foundation	Domain modeling, blueprint creation, shared libraries
Core Feature	Initial business logic, use cases, MVP flows
Validation & QA	Test coverage, failure simulation, CI observability
Expansion	Edge cases, additional adapters, multi-tenant overlays
Polish & Docs	Documentation, Swagger, diagrams, metadata

Each phase corresponds to a sprint or sub-milestone.

🧩 Recommended Phased Breakdown¶

milestones:
  - id: mvp-foundation
    features: [Booking.Aggregate, Booking.DomainEvent, BaseContractLib]
  - id: mvp-core
    features: [BookingFlow, Notification.Email.Send]
  - id: mvp-qa
    features: [BookingFlowTests, NotificationEdgeCaseTests]
  - id: mvp-ready
    features: [SwaggerUI, Diagrams, ReleaseNotes]

→ Enables traceable phase progression without bloating sprints.

📘 Sprint Composition Pattern¶

Sprint ID	Purpose
`sprint-1`	Domain setup, handler scaffolds
`sprint-2`	Core use case logic + coverage
`sprint-3`	QA hardening, deferred scope
`sprint-4`	Multi-tenant validation + polish

Each sprint includes artifacts like sprint-trace-matrix.json, tagged by execution phase.

🔄 Factory FSM Best Practices¶

✅ Split FSM states by phase gates:

stateDiagram-v2
    [*] --> DomainSetup
    DomainSetup --> CoreLogic
    CoreLogic --> TestValidation
    TestValidation --> ReleasePrep

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

Each transition is guarded by:

ArtifactProduced
TestsPassed
GateConditionsSatisfied

🧠 Agent Alignment per Phase¶

Phase	Activated Agents
Foundation	DomainModelerAgent, APIContractAgent
Core	BackendDeveloperAgent, AdapterGeneratorAgent
QA	TestGeneratorAgent, QAAgent
Expansion	MobileDevAgent, IntegrationAgent
Polish	DocumentationAgent, DiagramAgent

→ Only relevant agents are orchestrated per phase — keeps scope clean and parallel execution safe.

📊 Studio Strategy Visuals¶

Phase View: Shows grouped sprints under milestone umbrella
Trace Diff: Tracks feature progression across phases
Deferral Radar: Highlights phase-overrun or scope creep

✅ Summary¶

Incremental execution enables safe, auditable, and fast delivery
Each phase maps to agent workflows, traceable artifacts, and observable gates
Orchestration is phase-aware, ensuring modular flow and minimal risk

🧠 CI/CD Alignment with Milestones: Traceable Build Artifacts per Milestone¶

In the ConnectSoft AI Software Factory, CI/CD is not a standalone pipeline — it's a trace-driven, milestone-aware execution layer.

Every build, test, and deployment must answer: “Which sprint, milestone, and trace produced this artifact?”

This alignment enables:

✅ Auditable releases
✅ Safe rollbacks
✅ Incremental promotion
✅ Observability across environments

📦 CI/CD Artifact Requirements¶

Every build artifact (Docker image, NuGet package, deployment manifest, PR) must include:

Field	Source
`milestoneId`	From `milestones.yaml`
`sprintId`	From `sprint-plan.yaml`
`traceId`	From `execution-metadata.json`
`agentId/skillId`	From execution source
`versionTag`	From orchestrator FSM (e.g., `mvp-ready-v1`)

📁 Example: Docker Image Tags¶

booking-service:1.0.0-mvp-v1-trace123
booking-service:1.0.1-sprint3-hotfix

→ CI pipeline appends tags using inputs from planning artifacts and trace metadata.

📘 CI/CD Metadata Injection¶

Each pipeline task uses:

variables:
  milestoneId: $(Milestone)
  sprintId: $(Sprint)
  traceId: $(Trace)
  versionTag: $(Build.BuildNumber)-$(Milestone)

steps:
  - task: Docker@2
    inputs:
      tags: |
        $(versionTag)
        sprint:$(sprintId)
        trace:$(traceId)

→ Every artifact is trace-tagged and discoverable in logs, releases, and metrics.

🧩 Pipeline Triggers¶

Milestones and sprints trigger CI/CD flows via events:

Event	Pipeline Trigger
`MilestoneCompleted`	`ReleasePipeline-MVP`
`SprintClosed`	`BuildPipeline-sprint-3`
`ArtifactReady`	Feature-level test pipelines
`PlanningResumed`	Preview/deploy-to-dev pipelines

📊 Studio Pipeline View¶

Artifact	Sprint	Milestone	Image Tag	CI/CD Status
`booking-api`	sprint-2	mvp-core	1.0.0-s2-core	✅ Passed
`booking-tests`	sprint-3	mvp-qa	1.0.1-test-s3	⚠️ Retrying
`release-notes.md`	sprint-4	mvp-ready	doc-v1.0.3	✅ Passed

→ All CI/CD executions are traceable back to planning metadata.

🧪 Best Practices¶

🔁 Generate version tags dynamically based on sprintId + milestoneId
🧠 Emit trace annotations in all CI/CD logs
🧾 Capture execution-metadata.json as CI artifact
🔐 Sign and store release manifests per milestone folder

✅ Summary¶

CI/CD is deeply coupled with the Factory’s trace model — not ad hoc or parallel.
Every build and deployment is milestone-aware, sprint-tagged, and trace-linked
The result is a fully auditable and modular delivery process.

🧠 AI Agent Coordination Strategy: FSMs That Drive Sprint-Aware Execution¶

In the ConnectSoft AI Software Factory, agents don’t just execute in sequence — they operate within Finite State Machines (FSMs) that manage:

Sprint lifecycle
Milestone progression
Feature readiness
Failure handling
Event-based triggering

Every sprint is governed by a stateful flow, not a script — and every agent knows when and why it’s allowed to act.

🧩 FSMs = Coordinated Execution Plans¶

Each orchestrator (e.g., SprintOrchestrator, MilestoneCoordinator) defines an FSM:

states:
  - Planning
  - WaitingForValidation
  - Active
  - Verifying
  - Closed
transitions:
  - from: Planning
    on: SprintPlanned
    to: WaitingForValidation
  - from: WaitingForValidation
    on: GateConditionsSatisfied
    to: Active
  - from: Active
    on: SprintOutputsReady
    to: Verifying
  - from: Verifying
    on: AllTraceChecksPassed
    to: Closed

→ Sprint-aware FSMs allow retries, pauses, deferrals, or resume flows.

📘 FSM Event Triggers (Common)¶

FSM Event	Triggering Input
`SprintPlanned`	Emitted by ProductOwnerAgent
`GateConditionsSatisfied`	QAAgent + DevOpsAgent readiness signals
`SprintOutputsReady`	Feature artifacts trace-complete
`AllTraceChecksPassed`	sprint-trace-matrix.json validated
`PlanningResumed`	Manual or system trigger

🧠 Agent Execution Context Awareness¶

Every agent receives injected FSM context:

{
  "sprintId": "sprint-3",
  "milestoneId": "mvp-core",
  "currentState": "Active",
  "traceId": "trace-abc123",
  "phase": "CoreFeature",
  "allowedSkills": ["GenerateHandler", "EmitEvent", "CommitCode"]
}

→ Keeps execution scoped, safe, and idempotent.

📦 FSM State Persistence¶

Each orchestrator logs:

fsm-state.json
Last known agent execution events
Time since last transition
Unmet conditions
Trace checkpoints

This allows:

Resume-after-failure
Studio rollback
Diagnostics for blocked sprints

🧠 FSM Benefits for Sprint Coordination¶

Benefit	Description
✅ Agent Boundaries	No agent executes outside its allowed sprint/phase
✅ Traceable Progression	Every transition is observable via events
✅ Determinism	Execution flow is reproducible across runs
✅ Resumability	Execution can continue after pause/failure
✅ Scalable Composition	FSMs can fork by milestone, feature, or tenant

🧪 FSM Visualization in Studio¶

stateDiagram-v2
    [*] --> Planning
    Planning --> ValidatingScope
    ValidatingScope --> Active
    Active --> Verifying
    Verifying --> Closed
    Closed --> [*]

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

→ Each transition node includes trace ID, duration, blocking reasons, agent logs.

✅ Summary¶

AI agents don’t execute in a vacuum — they’re coordinated via FSMs per sprint and milestone
FSMs ensure safe, scoped, resumable, and traceable execution flows
Every phase of sprint execution is governed by event triggers and agent contract validation

🧪 Testing and QA by Sprint: TestGeneratorAgent Logic per Scope¶

In the ConnectSoft AI Software Factory, tests are not written manually post-delivery. They are generated and validated as an integral part of each sprint, using specialized agents like:

TestGeneratorAgent
QAAgent
ResiliencyAgent
ValidationAgent

“If it ships in a sprint, it’s tested in that sprint — by agents, automatically, observably.”

🧠 `TestGeneratorAgent` Responsibilities¶

Scope	Action
Sprint Features	Generate SpecFlow/BDD features and unit tests
Application Layer	Generate handler tests with mocks and assertions
API Layer	Create OpenAPI contract tests and status code checks
Domain Layer	Emit aggregate state-transition tests
Integration Points	Emit external adapter or messaging stubs/tests

→ All tests include traceId, sprintId, and featureId.

📘 Example: Generated BDD Feature (Sprint 3)¶

Feature: Book Appointment
  As a customer
  I want to book an appointment
  So that I can confirm my visit

  Scenario: Valid appointment request
    Given a new booking with date "2025-05-16"
    When the booking is submitted
    Then an AppointmentBooked event is emitted
    And a confirmation email is sent

🧩 Tagged: BookingService, sprint-3, mvp-core

🧩 `sprint-trace-matrix.json` → Test Links¶

Each feature maps to test cases:

{
  "featureId": "BookingFlow",
  "tests": [
    "BookAppointment.feature",
    "AppointmentAggregateTests.cs"
  ],
  "status": "complete",
  "coverage": "93%"
}

→ Used by orchestrators and QA agents to verify coverage gates.

🔁 QA Process Flow per Sprint¶

sequenceDiagram
  participant Orchestrator
  participant TestGenerator
  participant QAAgent
  participant Studio

  Orchestrator->>TestGenerator: Generate tests for sprint-3 features
  TestGenerator->>Orchestrator: Emit `TestCasesGenerated`
  Orchestrator->>QAAgent: Validate test readiness
  QAAgent->>Orchestrator: Emit `SprintQAReady`
  Orchestrator->>Studio: Visualize coverage and test linkage

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

📦 Output Artifacts¶

File	Contents
`*.feature`	BDD test cases
`*.Tests.cs`	NUnit/MSTest unit tests
`qa-coverage-report.md`	Feature/test mapping
`qa-errors.json`	Test failures (if any)
`sprint-trace-matrix.json`	Feature → Test → Commit → Agent linkages

📊 Studio QA Panel (Per Sprint)¶

Feature	Coverage	Test Cases	Last Run	Status
BookingFlow	93%	12	2 minutes ago	✅ Pass
Notification	84%	9	3 minutes ago	⚠️ Warning
AuthFlow	100%	5	1 minute ago	✅ Pass

→ Clicking opens trace-linked execution logs and test diffs.

✅ Summary¶

QA is fully agent-driven, sprint-scoped, and trace-linked
TestGeneratorAgent emits test code, QAAgent validates completeness
Sprints cannot close until test coverage meets gates defined in milestones.yaml or FSM rules

🧯 Failure Recovery and Sprint Re-execution¶

In the ConnectSoft AI Software Factory, failures are expected, observable, and recoverable. Every sprint is governed by orchestration logic that ensures:

Agent failures don’t halt the Factory
Invalid artifacts are retried or corrected
Sprints can resume, rewind, or restart safely

“A failed test, a broken trace, or a flaky build is not the end — it’s just a fork in the FSM.”

🧩 Types of Sprint Failures¶

Failure Type	Example
Agent Skill Error	`TestGeneratorAgent` fails to emit valid SpecFlow file
Validation Gate Fail	Coverage below threshold in `QAAgent`
Artifact Mismatch	Generated handler lacks required trace tags
CI/CD Execution Error	Build image fails, manifests invalid
Partial Output	Some features complete, others are blocked/deferred

🔁 Recovery Mechanisms¶

Mechanism	Trigger Condition
Agent Retry	Automatic retry after transient failure (`skillRetryCount`)
Sprint Resumption	Triggered after `SprintPaused` or `PlanningResumed`
Targeted Re-execution	Re-run of specific agent or feature via Studio
Sprint Rewind	Manual rollback to prior FSM state (`fsm-state.json`)
Snapshot Recovery	Rebuild execution from prior trace ID session checkpoint

📘 Example: Failure Event¶

{
  "event": "AgentSkillFailed",
  "agentId": "test-generator",
  "skillId": "GenerateSpecFlowTest",
  "featureId": "BookingFlow",
  "sprintId": "sprint-3",
  "traceId": "trace-abc123",
  "error": "Missing scenario outline"
}

→ This triggers a retry, logs an error to qa-errors.json, and flags the sprint for Studio review.

🧠 Orchestrator FSM Support¶

FSM transitions on failure:

stateDiagram-v2
    Active --> Verifying
    Verifying --> Closed: AllTestsPassed
    Verifying --> RetryPhase: GateFailed
    RetryPhase --> Active: PatchEmitted
    RetryPhase --> Closed: ManualApproval

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

→ Ensures trace-safe retries and prevents broken progression.

📂 Recovery Artifacts¶

File	Contents
`fsm-state.json`	Last known orchestrator state
`execution-errors.json`	List of agent/skill/test failures
`recovery-plan.yaml`	Actions planned for retry or re-execution
`retry-history.json`	Skill attempts, durations, retry logic used

📊 Studio Recovery Console¶

Failure Point	Agent	Status	Retry Count	Last Action
BookingFlow Test	test-generator	⚠️ Failed	2	Auto retry queued
BookingAdapter.cs	backend-dev	✅ Success	0	Generated
Notification Feature	test-generator	❌ Blocked	3	Needs human fix

→ Manual options: Re-run, Override, Patch, Pause, Abort Sprint

✅ Summary¶

The Factory is designed to recover from failure at any phase of sprint execution
Recovery is trace-aware, artifact-linked, and observable
Orchestrators and Studio work together to enable safe re-execution and rollback

⚖️ Factory Speed vs Logical Sprinting: Balancing Parallel Execution and Governance¶

The ConnectSoft AI Software Factory is capable of executing thousands of workflows in parallel. Yet, every sprint must remain:

Traceable
Governable
Deterministic
Testable

Just because the Factory can go fast doesn’t mean it should go ungoverned.

This cycle explores how ConnectSoft balances execution velocity with structured delivery logic using logical sprinting and agent orchestration boundaries.

🚀 How the Factory Scales Fast¶

Capability	Enablement Source
Concurrent Agent Flows	Stateless agents scoped by `traceId` and `sprintId`
Asynchronous FSMs	Milestone and sprint coordinators operate independently
Multi-tenant Isolation	Namespace and module-level boundaries
Event-driven Triggers	Agents awaken only on relevant input events
Auto-scaling Infrastructure	Containerization, KEDA, and burstable workflows

🧠 What Logical Sprinting Means¶

Characteristic	Description
No fixed timebox	A sprint may last 30 seconds or 3 hours — it’s driven by artifact gates, not dates.
FSM-scoped	Each sprint is a state machine; no shared global timeline is required.
Trace-driven	Each sprint ID links all artifacts and events; multiple sprints can run in parallel.
Test-bound	No sprint can close without QA validations, regardless of speed.

→ Logical sprints allow burst scaling without compromising delivery discipline.

🧩 Parallel Execution Strategy¶

Execution Layer	Parallelization Model
Blueprints	Multiple services planned at once
Sprints	Per-service or per-feature sprint FSMs
Agents	Isolated actors per trace/sprint/skill
Testing/CI	Parallel test runs per image, per sprint
Deployment	Canary/staged rollout per milestone ID

📊 Studio Sprint Parallel View¶

Sprint ID	Module	Status	Artifacts	Tests	Duration
`sprint-3`	BookingService	✅ Closed	12	10	3m 12s
`sprint-4`	Notification	🟡 Verifying	9	6	1m 09s
`sprint-5`	Invoicing	⏳ Active	4	0	0m 32s

→ Click any sprint to trace agent activity, FSM state, and error paths.

🛡️ Governance Mechanisms in High-Speed Execution¶

Guardrail Type	Description
FSM Gate Conditions	Prevent sprint closure unless trace matrix and test metrics validate success
Parallel Trace Isolation	Each sprint has a unique `traceId` and agent execution context
Deployment Freeze Flags	Milestone transitions blocked until manual approval (if configured)
Agent Scope Rules	No agent can act outside its assigned sprint/milestone boundary
Observability Events	All agent execution emits span and metric signals with trace tagging

📘 YAML Example: Controlled Sprint Execution¶

sprints:
  - id: sprint-4
    module: NotificationService
    milestone: mvp-core
    concurrencyPolicy: isolated
    gates:
      - traceValidated
      - testsPassed
    governance:
      approvalRequired: true

→ Ensures that even fast-executing sprints must pass trace and human checks.

✅ Summary¶

The Factory is designed for speed, but sprinting remains logical and controlled
Multiple sprints may execute concurrently, but no milestone completes without gates passing
Governance, observability, and FSM orchestration ensure high throughput with safety

📦 Examples and Templates: Milestone Definition Samples¶

Milestones in the ConnectSoft AI Software Factory are more than labels — they are orchestration contracts. Each milestone:

Scopes features and sprints
Declares gate conditions
Links to traceable outputs
Triggers downstream events

A milestone is the Factory’s unit of governance, traceability, and delivery coordination — and must be templatized for consistency.

🧩 Standard Milestone Template (YAML)¶

milestones:
  - id: mvp-core
    name: Core MVP Delivery
    description: Deliver functional booking and notification flows.
    requiredFeatures:
      - BookingFlow
      - Notification.Email.Send
      - Auth.Login
    requiredArtifacts:
      - BookingService
      - NotificationService
      - booking-api.feature
      - diagrams/booking-context.mmd
    gateConditions:
      - allPullRequestsMerged
      - testCoverageAbove90
      - traceMatrixComplete
    versionTag: mvp-v1
    phase: CoreFeature
    triggers:
      - event: MilestoneCompleted
        next: deploy-to-staging
    traceability:
      labels: ["mvp", "core", "critical"]
      tracePattern: "trace-*"
      governedBy: product-owner

✅ Fully machine-parseable ✅ Used by orchestrators, Studio, CI/CD, and governance agents

📘 Extended Milestone with Parallel Streams¶

milestones:
  - id: onboarding-v2
    name: Multi-Channel Onboarding Rollout
    parallelStreams:
      - UserSignupFlow
      - MobilePushFlow
      - AdminApproval
    deferredAllowed: true
    versionTag: onboarding-v2-beta
    orchestrator: MilestoneCoordinator
    triggers:
      - event: SprintClosed
        condition: allStreamsReady
        next: MilestoneFinalization

→ Supports multi-feature parallel sprint orchestration, with dynamic stream activation.

🧪 QA-Focused Milestone Template¶

milestones:
  - id: qa-hardening
    phase: Validation
    name: QA and Chaos Testing Milestone
    requiredArtifacts:
      - booking-api.feature
      - resiliency-checklist.md
      - chaos-results.json
    gateConditions:
      - allTestsPassed
      - resilienceScoreAbove: 85
    versionTag: mvp-v2-rc
    governedBy: qa-lead
    triggers:
      - event: MilestoneCompleted
        next: freeze-deployment

→ Tied to agents like QAAgent, ChaosAgent, TestGeneratorAgent.

🛡️ Security-Focused Milestone Sample¶

milestones:
  - id: security-verification
    name: Security and Compliance Review
    requiredArtifacts:
      - security-events.json
      - owasp-report.md
      - tenant-policy.yaml
    gateConditions:
      - noCriticalFindings
      - jwtValidationInPlace
      - traceSpanPolicyCheck: pass
    versionTag: security-reviewed-v1
    governedBy: security-officer
    triggers:
      - event: SecurityPolicyPublished
        next: staging-release

→ Enables SecurityEngineerAgent, PolicyValidatorAgent to participate in release flow.

📊 Studio View Based on Templates¶

Milestone	Scope Type	Phase	Completion	Gate Status
mvp-core	Single Stream	CoreFeature	✅	✅ Passed
qa-hardening	Single Stream	Validation	⏳	⚠️ Incomplete
onboarding-v2	Multi-Stream	Expansion	🟡 Partial	🟠 Some passed

Each row includes links to:

Linked features
Agent activity logs
Artifact summaries
Gate trace failures (if any)

✅ Summary¶

Milestone templates are declarative contracts that unify planning, execution, and governance
YAML-based templates are reusable, inspectable, and traceable
Templates vary by phase, focus, and agent cluster — but always produce observable state transitions

🔐 Security and Audit Hooks Per Sprint: How Metrics and Traces Are Verified¶

In the ConnectSoft AI Software Factory, security and compliance aren’t deferred to pre-release. Instead, each sprint embeds security gates, trace validation, and audit-ready telemetry into the delivery process — automatically and observably.

“Every sprint is a mini compliance unit — traceable, testable, and policy-aware.”

🧠 Key Security Enforcement Agents¶

Agent	Responsibility
`SecurityEngineerAgent`	Enforces authentication, validation, headers, CORS, rate limiting
`PolicyValidatorAgent`	Verifies sprint-level trace compliance, secret usage, trace spans
`PrivacyComplianceAgent`	Ensures PII handling, tenant boundary enforcement, redaction

These agents operate during the sprint, not after.

📦 Metrics and Traces Checked Per Sprint¶

Signal	Purpose
`traceId` in all artifacts	Verifies trace linkage
`agentId` + `skillId` in spans	Confirms who produced what
`jwtValidationEnabled = true`	Security setting required on all APIs
`securitySpanTemplateInjected`	Confirms span templates like `access_denied`, `token_expired`
`trace.exported` to observability	Enforced per milestone FSM gate
`SecurityPolicyPublished` event	Marks sprint-compliant outputs

📘 Example: Policy Validation Report (`security-audit.json`)¶

{
  "sprintId": "sprint-4",
  "traceId": "trace-91dc",
  "issues": [],
  "status": "Compliant",
  "validatedBy": "policy-validator",
  "checks": [
    "CSPHeadersPresent",
    "RateLimitingEnabled",
    "TokenValidationMiddlewareConfigured",
    "TraceSpansAttached"
  ]
}

✅ Published as an output in the sprint’s /security/ folder ✅ Linked to trace-matrix and milestone tag

🧩 Audit Hooks in Milestone Definitions¶

gateConditions:
  - testCoverageAbove90
  - traceMatrixComplete
  - noCriticalSecurityFindings
  - jwtValidationInPlace
  - spanTemplatesVerified

→ These gates are enforced before MilestoneCompleted or SprintClosed can be emitted.

🧪 Observability Enforced for Auditability¶

Each service or agent execution must emit:

OpenTelemetry spans with:
agentId
skillId
sprintId
moduleId
securityScope
Logs with traceId and tenantId
execution-metadata.json tagged with validated=true or securityScanStatus

📊 Studio Security Dashboard¶

Sprint ID	JWT Validated	PII Audited	Trace Complete	Compliance
sprint-4	✅	✅	✅	✅ Pass
sprint-5	⚠️ Partial	🟡 Pending	✅	🟠 Review

→ Click for:

Span graphs
Trace lineage audit
Diff between security template versions

🔁 If a Check Fails¶

SecurityPolicyViolation event is emitted
Sprint FSM moves to RetryPhase or Pause
Violations listed in security-audit.json
Studio flags sprint for manual review or patch

✅ Summary¶

Every sprint embeds real-time security checks and audit hooks
Trace validation, span injection, and policy enforcement are automated and observable
Factory artifacts are compliance-ready by design, not by post-processing

🧠 Summary and Final Takeaways¶

In the ConnectSoft AI Software Factory, timing is not driven by calendars — it's driven by traceable logical events, orchestrated agent flows, and verifiable outcomes.

Milestones, sprints, and delivery phases are not about when — they are about what, why, and how completely.

🔁 Full Lifecycle View¶

flowchart TD
  Vision --> MilestonePlanning
  MilestonePlanning --> SprintScope
  SprintScope --> AgentExecution
  AgentExecution --> QAValidation
  QAValidation --> SecurityAudit
  SecurityAudit --> MilestoneGateCheck
  MilestoneGateCheck --> SprintClosure
  SprintClosure --> ArtifactTagging
  ArtifactTagging --> Release
  Release --> PlanningResumed

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

✅ Every transition is governed by events, trace IDs, FSMs, and planning artifacts.

✅ Factory Time Model Summary¶

Concept	How It Works in ConnectSoft
Sprint	Scoped execution window, defined by FSM, trace-linked
Milestone	Logical delivery boundary, with test and trace gates
Backlog	Blueprint-derived feature pool, partitioned dynamically
Planning	Agent-generated, resumable, Studio-approved
Execution	Event-driven, trace-aware, phase-bound
Recovery	FSM-governed, retry-safe, observable
CI/CD	Aligned to sprints and milestones via tags and metadata
Security	Enforced inline via audit traces and policy agents

📦 Key Artifacts¶

Artifact	Purpose
`milestones.yaml`	Defines traceable delivery boundaries
`sprint-trace-matrix.json`	Links features to code, commits, tests
`execution-metadata.json`	Captures agent runs per skill
`qa-coverage-report.md`	Ensures tests are tied to features
`security-audit.json`	Verifies compliance and observability
Git tags (`sprint-`, `mvp-`)	Enable CI/CD alignment and versioning

🧠 Mindset Shift: Time in the AI Software Factory¶

Traditional DevOps	ConnectSoft AI Software Factory
Timeboxed sprints	Event-driven FSMs
Manual backlog management	Blueprint-scoped feature orchestration
Retrospectives + triage	Trace-based recovery and milestone audit
Human-driven estimation	Agent-measured effort, retry count, duration
QA at the end	Test generation + validation inside the sprint

🎯 Final Takeaways¶

Sprints and milestones are execution containers, not deadlines.
All execution is observable, auditable, and traceable by design.
Planning is iterative, hybrid, and agent-augmented.
Recovery and governance are first-class, FSM-driven capabilities.
Every artifact is linked back to sprint, milestone, trace, and intent.

🚀 Next Steps You Can Take¶

Action	Outcome
Create `milestones.yaml`	Enable FSM-aware orchestration
Define your first sprint	Activate execution planning and trace
Tag agents and outputs	Link artifacts to blueprint intent
Configure test + trace gates	Enforce quality without slowing speed
Use Studio dashboards	Observe sprints, QA, and security visually

Timing, Milestones, and Sprint Management in ConnectSoft AI Software Factory¶

🌀 Factory Philosophy on Time: Logical Time vs Calendar Time¶

⏱️ Two Modes of Time¶

⚙️ Logical Time: The Orchestration Primitive¶

📘 Why Logical Time Wins¶

📊 Time Mapping in Studio Dashboards¶

📎 Implications for Multi-Agent Planning¶

✅ Summary¶

🌀 Milestones as Delivery Units¶

🧱 Milestone Characteristics¶

🧩 YAML Format: milestones.yaml¶

⚙️ Orchestration Effects of Milestones¶

📊 Visualization in Studio¶

🧠 Why Milestones Matter¶

✅ Summary¶

🎯 Sprints as Scoped Execution Windows: Controlling Agent Flows¶

⚙️ Sprint = Execution Envelope¶

🧬 Sprint Metadata Structure¶

🧠 Sprint-Driven Execution Flow¶

🧩 Orchestration Role of a Sprint¶

📊 Sprint Monitoring in Studio¶

✅ Summary¶

🎯 Planning Is a Collaborative, Multi-Agent Flow¶

🧠 Core Planning Agents and Their Roles¶

🧬 Planning Flow (Event-Driven)¶

📦 Outputs Produced During Planning¶

🧩 FSM State Transition Model¶

🧠 Human-In-The-Loop Overrides¶

✅ Summary¶

🧠 Events That Drive Execution¶

📬 Core Sprint Lifecycle Events¶

🔄 Execution Timeline Flow¶

🧩 Event Payloads (Simplified)¶

SprintPlanned Event¶

SprintClosed Event¶

🧠 What Triggers These Events?¶

🔐 Observability Links¶

✅ Summary¶

🌀 Traceability and Observability Links¶

🧩 What Is Traced?¶

📘 Example: execution-metadata.json¶

📈 Metrics That Link to Sprints¶

📊 Sprint Trace Matrix¶

📎 Git Strategy for Traceability¶

🧠 Studio Views¶

✅ Summary¶

📦 Artifacts: milestones.yaml, sprint-trace-matrix.json, tags, metadata¶

📘 Artifact #1: milestones.yaml¶

✅ Example¶

📘 Artifact #2: sprint-trace-matrix.json¶

✅ Example¶

📘 Artifact #3: Git Tags and Metadata¶

📘 Artifact #4: execution-metadata.json¶

✅ Example¶

🔁 Where These Artifacts Live¶

✅ Summary¶

🎯 How Backlog Is Managed Across Milestones¶

📘 Where the Backlog Comes From¶

🧩 How Milestones Consume the Backlog¶

🔁 Deferred Features and Roll-Forward¶

🧠 Managing Backlog Priority¶

📊 Backlog in Studio¶

✅ Summary¶

🧠 Resuming Planning: How the Factory Supports Iterative Scope Expansion¶

🧩 Key Event: PlanningResumed¶

🔁 Resumption Triggers¶

🧠 Resumption Workflow¶

📘 Artifact Change Detection¶

📊 Studio Support¶

📦 Outputs After Resumption¶

✅ Summary¶

🤖 + 🧑 Human vs AI-Driven Milestone Planning: Hybrid Workflows and Governance¶

🧠 Division of Responsibilities¶

🧩 Agent Outputs for Review¶

🧑‍💻 Human Governance Entry Points¶

🔁 Example: Hybrid Planning Flow¶

🛡️ Governance Modes¶

📊 Studio Support¶

✅ Summary¶

🎯 Studio Visualization: How Milestones and Sprint Scope Appear in Dashboards¶

🧩 YAML Format: `milestones.yaml`¶

`SprintPlanned` Event¶

`SprintClosed` Event¶

📘 Example: `execution-metadata.json`¶

📦 Artifacts: `milestones.yaml`, `sprint-trace-matrix.json`, tags, metadata¶

📘 Artifact #1: `milestones.yaml`¶

📘 Artifact #2: `sprint-trace-matrix.json`¶

📘 Artifact #4: `execution-metadata.json`¶

🧩 Key Event: `PlanningResumed`¶

🧠 `TestGeneratorAgent` Responsibilities¶

🧩 `sprint-trace-matrix.json` → Test Links¶

📘 Example: Policy Validation Report (`security-audit.json`)¶