Product development lifecycle

End-to-end scenario: spin up a product team, file the roadmap, build the MVP, ship the first release, all inside Team-X. The single playbook closest to the launch demo.

Updated

Status: Draft | Last Updated: 2026-05-03 | Version: 1.0

Executive Summary

This scenario demonstrates using Team-X to manage a complete product development lifecycle from initial concept through launch. It shows how an operator leverages AI employees for planning, decomposition, execution, coordination, and delivery, all within a governed, auditable framework.

Scenario Duration: 6 weeks (compressed for documentation) Team Size: 7 AI employees across 3 functions (Product, Engineering, Design) Total Tickets Created: 42 Total Spend: ~$127 (simulated at $3/month rate)

Learning Objectives:

  • How to translate a product vision into actionable work
  • Using Task Planner for intelligent decomposition
  • Managing cross-functional dependencies
  • Tracking progress through each development phase
  • Handoffs between roles and maintaining context
  • Launch preparation and go-live execution

Table of Contents

  1. Phase 0: Vision & Planning
  2. Phase 1: Requirements & Design
  3. Phase 2: Development
  4. Phase 3: Testing & QA
  5. Phase 4: Launch Preparation
  6. Phase 5: Go-Live & Stabilization
  7. Phase 6: Post-Launch Retrospective
  8. Key Takeaways
  9. Related Documentation

Phase 0: Vision & Planning

The Scenario Context

Product: “TeamFlow Analytics”, A dashboard for engineering leaders to track team productivity, sprint health, and blocker patterns.

Business Problem: Engineering leaders lack visibility into team velocity, blocked work patterns, and emerging risks. Data exists in tools (Jira, GitHub, Slack) but is fragmented and requires manual synthesis.

Product Vision: An AI-powered dashboard that aggregates signals from engineering tools, surfaces actionable insights, and recommends interventions.

Timeline: 6 weeks from concept to MVP launch Team: 1 Product Manager, 2 Engineers (Frontend + Backend), 1 Designer, 1 QA, 1 DevOps, 1 Tech Lead

Step 1: Hiring the Team

Operator Action: Open Command Palette (Ctrl+K) and hire employees.

Ctrl+K → "Hire a Senior Product Manager"
Ctrl+K → "Hire a Senior Fullstack Engineer named Priya"
Ctrl+K → "Hire a Backend Engineer named Mike"
Ctrl+K → "Hire a Product Designer named Lin"
Ctrl+K → "Hire a QA Engineer named Sarah"
Ctrl+K → "Hire a DevOps Engineer named James"
Ctrl+K → "Hire a Tech Lead named Elena"

Result: 7 employees created with curated role specifications from the catalog.

Org Structure:

Elena (Tech Lead)
├── Priya (Frontend Engineer)
├── Mike (Backend Engineer)
├── Lin (Product Designer)
├── Sarah (QA Engineer)
└── James (DevOps Engineer)

Alex (Product Manager) — reports to workspace owner

Step 2: Creating the Product Project

Operator Action: Create project to track all work.

Command Palette:

Ctrl+K → "Create project: TeamFlow Analytics MVP"

Manual via Projects Tab:

  1. Click Projects in top navigation
  2. Click Create Project
  3. Configure:
    • Name: TeamFlow Analytics MVP
    • Description: AI-powered engineering visibility dashboard
    • Lead: Elena (Tech Lead)
    • Target Date: 6 weeks from now
    • Goal: Launch MVP with core analytics and insight features

Result: Project card created in Projects Kanban, tracks all linked tickets.

Step 3: Using Task Planner for Decomposition

Operator Action: Ask Task Planner to decompose the vision into tickets.

Command Palette:

Ctrl+K → "Decompose TeamFlow Analytics MVP into tickets"

Amber Gate Appears:

→  Write-side agentic run detected

Confirm: Decompose "TeamFlow Analytics MVP" into tickets

This will create 8-12 tickets and assign work.

Estimated cost: ~$3.50

[Cancel]                                      [Confirm]

Operator confirms.

Task Planner Execution:

Plan card (agent reasoning):

I’ll analyze the MVP requirements and decompose into actionable tickets:

  1. Core features: dashboard UI, data pipeline, insight engine
  2. Infrastructure: data storage, API design, deployment
  3. Cross-functional: design, QA, documentation
  4. Phased delivery: MVP → iteration

Tool calls:

query_employees({ role_contains: ["product", "design", "frontend", "backend", "qa", "devops"] })
query_projects({ name: "TeamFlow Analytics MVP" })

Answer card:

Created 9 tickets for TeamFlow Analytics MVP:

Planning (2):

  1. Requirements specification, Alex (Product Manager)
  2. UX mockups & design system, Lin (Designer)

Backend (2): 3. Data pipeline architecture, Mike (Backend Engineer) 4. Insight aggregation service, Mike (Backend Engineer)

Frontend (2): 5. Dashboard component library, Priya (Frontend Engineer) 6. Real-time analytics dashboard, Priya (Frontend Engineer)

Infrastructure (1): 7. CI/CD pipeline & deployment, James (DevOps Engineer)

QA (1): 8. Test plan & automated testing, Sarah (QA Engineer)

Integration (1): 9. API integration & end-to-end, Elena (Tech Lead)

All tickets linked to TeamFlow Analytics MVP project. Estimated completion: 6 weeks based on team capacity.

Phase 0 Checklist

ItemStatusNotes
Team hired✅ Complete7 employees with balanced roles
Project created✅ CompleteLinked tickets roll up to project progress
Work decomposed✅ Complete9 tickets created, optimally assigned
Kickoff scheduled⏳ PendingSchedule all-hands in Meetings tab

Phase 1: Requirements & Design (Week 1)

Ticket #1: Requirements Specification

Assignee: Alex (Product Manager) Status: In Progress Participants: Alex, Elena (Tech Lead)

Thread Summary:

Alex (opening comment):

I'll create a comprehensive PRD for TeamFlow Analytics MVP.

Requirements:
1. Data Sources: Jira (tickets, sprints), GitHub (PRs, commits), Slack (messages, reactions)
2. Dashboard Views: Team velocity, Sprint burndown, Blocked work, Team capacity
3. Insights: Pattern recognition (anomalies, trends, recommendations)
4. Exports: CSV, PDF reports
5. Users: Engineering leads, EMs, CTO

Success metrics:
- Dashboard loads in < 3 seconds
- Insights surface within 1 hour of data change
- Supports 10 concurrent users

Non-requirements (out of scope):
- User authentication (assumes SSO)
- Custom report builder
- Mobile app

Let me know if I should adjust scope or priorities.

Elena (Tech Lead):

Looks solid. One question: for the data pipeline, do we have preference on 
storage? Postgres + TimescaleDB for time-series, or something simpler for MVP?

Also, suggest adding a "technical requirements" section for API design, 
data retention, and privacy considerations.

Alex (with tool call):

Good catches. Let me research time-series options for MVP-scale data.

[Tool call: search_vault "time-series database comparison"]
[Tool result: Returns analysis of TimescaleDB, InfluxDB, plain Postgres]

Recommendation: Plain Postgres for MVP (simpler ops, sufficient for 
10-100 tickets/day per team). Can migrate to TimescaleDB if we hit scale.

Adding technical requirements section now.

Resolution: Alex creates PRD document, attaches to ticket. Ticket marked Done.

Ticket #2: UX Mockups & Design System

Assignee: Lin (Product Designer) Status: In Progress Participants: Lin, Alex (Product Manager), Priya (Frontend Engineer)

Thread Summary:

Lin (opening comment):

Creating design system for TeamFlow Analytics:

Design principles:
- Data-first: Charts and metrics take prominence
- Scanability: 5-second rule — can user understand team health in 5 seconds?
- Action-oriented: Every insight has a "what to do" recommendation

Deliverables:
1. Design system (colors, typography, components)
2. Dashboard mockups (3 views: Overview, Team Detail, Insights)
3. Component library (charts, cards, tables)

Brand: Strategia-X color palette with data-viz friendly variations.

Working on mockups now — will share for feedback.

Alex (comment):

Lin — can you prioritize the Overview dashboard? We want to validate the 
core concept before detailed views. Also, make sure to show the "blocked 
work" pattern prominently — that's our key differentiator.

Priya (added as participant):

Lin — once you have mockups, I can start building the component library. 
Would be helpful to have:
- Responsive breakpoints (desktop primary, tablet secondary)
- Chart component specs (libraries: Recharts vs. D3 vs. Chart.js)
- Loading/error/empty states for each component

Don't need final implementation, just enough for me to start scaffolding.

Lin (with attachments):

Uploaded initial mockups:
- overview-dashboard-v1.png (Attached)
- design-system-tokens.png (Attached)

Key decisions:
- Recharts for charting (React-native, good perf)
- 4-column grid for metrics
- Color coding: red (critical), amber (warning), green (healthy), blue (info)

Feedback welcome — especially on the insight cards layout.

Resolution: Lin finalizes design system, attaches Figma link. Ticket marked Done.

Phase 1 Completion

Mission Control View:

┌─────────────────────────────────────────────────────────────────────┐
│ TeamFlow Analytics MVP — Project Progress                           │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│ Completion: 22% (2/9 tickets done)                                  │
│                                                                   │
│ DONE (2):                                                          │
│ ✅ Requirements specification                                       │
│ ✅ UX mockups & design system                                       │
│                                                                   │
│ IN PROGRESS (1):                                                   │
│ 🔄 Dashboard component library — Priya (Frontend Engineer)           │
│                                                                   │
│ OPEN (6):                                                          │
│ ⬜ Data pipeline architecture — Mike (Backend)                       │
│ ⬜ Insight aggregation service — Mike (Backend)                      │
│ ⬜ Real-time analytics dashboard — Priya (Frontend)                  │
│ ⬜ CI/CD pipeline & deployment — James (DevOps)                       │
│ ⬜ Test plan & automated testing — Sarah (QA)                        │
│ ⬜ API integration & end-to-end — Elena (Tech Lead)                  │
│                                                                   │
│ Blockers: 0                                                        │
│ Velocity: On track (2 tickets in Week 1, target: 2)                 │
└─────────────────────────────────────────────────────────────────────┘

Phase 2: Development (Weeks 2-4)

Ticket #3: Data Pipeline Architecture

Assignee: Mike (Backend Engineer) Status: In Progress Participants: Mike, Elena (Tech Lead), James (DevOps)

Thread Summary:

Mike (opening comment):

Designing the data pipeline for TeamFlow Analytics:

Data flow:
Jira/GitHub/Slack → Ingestion Service → Postgres → API → Dashboard

Architecture decisions:
1. Ingestion: Python async workers (historical sync) + webhooks (real-time)
2. Storage: Postgres with partitioned tables (by team_id, date)
3. Caching: Redis for dashboard queries (5-min TTL)
4. API: FastAPI with async endpoints

Schema:
- teams (team metadata)
- tickets (Jira tickets)
- commits (GitHub commits)
- events (Slack messages, PR comments)
- metrics_snapshot (hourly aggregations)

Working on detailed architecture document.

Elena (Tech Lead):

Mike — a few questions:

1. Webhook reliability: What if Jira/GitHub webhooks fail? Do we have 
   retry logic or fall back to polling?

2. Data freshness: "Real-time" in the PRD means < 1 hour. Can we achieve 
   that with webhooks only, or do we need incremental polling?

3. Storage partitioning: Are we planning time-based partitioning for 
   efficient queries? What's the retention policy?

4. Privacy: Do we store raw Slack messages, or just aggregates? This matters 
   for GDPR if we expand to EU teams.

Mike (with tool call):

Good questions. Let me address each:

[Tool call: query_tickets({ project: "TeamFlow Analytics MVP" })]
[Tool result: Returns all tickets for context]

1. Webhook reliability: Implementing with:
   - Exponential backoff retry (max 5 attempts)
   - Dead letter queue for failed events
   - Hourly reconciliation job to catch gaps
   - Monitoring for webhook health (Copilot integration)

2. Freshness: Hybrid approach:
   - Webhooks for real-time (target: < 5 min)
   - 15-min incremental polling for backup
   - Can tune based on cost/freshness tradeoff

3. Partitioning: Yes, (team_id, date) composite partition.
   Retention: 90 days raw data, 1 year aggregated metrics.

4. Privacy: Storing aggregates only for Slack:
   - Message counts per channel/user
   - Sentiment scores (high-level)
   - No raw message content (GDPR-safe)

Updating architecture doc with these decisions.

James (DevOps, added as participant):

Mike — once architecture is finalized, I'll need:
1. Docker Compose for local development
2. Kubernetes manifests for production (eventually)
3. Environment variable config guide
4. Migration scripts for schema changes

Can we sync on deployment strategy? I'm thinking:
- Week 2: Local dev environment
- Week 3: Staging environment (with sample data)
- Week 4: Production-ready (with actual data pipettes)

Mike:

Sounds good. I'll include deployment guide in my deliverable.

One more thing: Should we use a message queue (RabbitMQ/SQS) for ingestion, 
or is direct DB write fine for MVP scale?

Thinking: Direct write is simpler. Message queue adds complexity but gives 
us replayability and rate limiting.

My recommendation: MVP = direct write. If we hit > 100 events/sec, 
reconsider.

Elena:

Agreed with direct write for MVP. We can add a queue if needed.

Arch looks solid. Proceed to implementation phase.

Resolution: Mike creates architecture document, database schema, and ingestion service specification. Ticket marked Done.

Ticket #5: Dashboard Component Library

Assignee: Priya (Frontend Engineer) Status: In Progress Participants: Priya, Lin (Designer), Elena (Tech Lead)

Thread Summary:

Priya (opening comment):

Building the component library based on Lin's design system:

Component list:
1. MetricCard — displays single KPI with trend indicator
2. ChartContainer — responsive wrapper for all charts
3. InsightCard — Copilot-style insight with severity stripe
4. DataTable — sortable, filterable data table
5. StatusBadge — Open/In Progress/Blocked/Done badges
6. Avatar — user avatar with online status

Tech stack:
- React 19 (with new compiler optimizations)
- Tailwind CSS for styling
- Recharts for visualizations
- Framer Motion for micro-animations

Starting with MetricCard and ChartContainer.

Lin (Designer):

Priya — quick question on MetricCard:

In the mockups, the metric card has a "trend" indicator (arrow up/down 
with percentage). Should this be:
a) Calculated automatically (comparison to previous period)
b) Manually specified (passed as prop)

I'm leaning toward (a) for better UX, but it means more backend work.

Priya (with code):

Good question. Let me implement (a) with a fallback:

```tsx
interface MetricCardProps {
  title: string;
  value: number | string;
  trend?: {
    value: number; // percentage change
    period: string; // "vs. last week", "vs. last sprint"
  };
  trendSource?: 'auto' | 'manual';
}

// If trendSource='auto', we calculate from API data
// If trendSource='manual', we use provided trend prop

This gives us flexibility. For MVP, we can start with manual trends (hardcoded in API) and move to auto-calculation in iteration.

Lin, does this approach work for you?


**Lin:**

Yes, that’s perfect. Manual for MVP is fine.

One more thing: Can you add a “loading” skeleton state? The card should show a pulsing placeholder while data loads. This is important for the < 3-second load requirement.


**Priya:**

Absolutely. I’ll add:

  1. Skeleton component (shimmer effect)
  2. Loading prop on MetricCard
  3. Error state with retry button

Also implementing “optimistic UI”, cards show cached data immediately then refresh in background.

Attaching component preview for review.


**[Attachment: metric-card-demo.mp4]**

**Elena (Tech Lead):**

Priya, component looks great. A few code review notes:

  1. Consider using React.memo() for MetricCard to prevent unnecessary re-renders when parent updates.

  2. The trend calculation logic should be extracted to a utility function so we can unit test it independently.

  3. For the skeleton, can we use a consistent height? The card should not jump when content loads.

  4. Error handling: If the API fails, should we show the last known good value, or an error state? I’d prefer “last known” with a small “stale” indicator.

Overall: Ship it. These are nice-to-haves for iteration.


**Priya:**

Thanks Elena! I’ll address:

  1. ✅ Adding React.memo
  2. ✅ Extracting to utils (calcTrend.test.ts)
  3. ✅ Fixed height skeleton
  4. ✅ Last known good + stale indicator for errors

Component library is ready for integration. Marking ticket Done.


**Resolution:** Component library completed with 6 reusable components, TypeScript types, and Storybook documentation. Ticket marked Done.

---

### Dependency Coordination: Frontend Waits for Backend

**Situation:** Ticket #6 (Real-time Analytics Dashboard) is blocked because Mike's backend API isn't ready.

**Operator Action (via Mission Control):**

1. **Detect blocker:** Mission Control shows "Blocked Work: 1"
2. **Investigate:** Click blocked ticket, see block reason: "Waiting for API endpoints from Ticket #3"
3. **Coordinate:** Add Priya to Ticket #3 as participant

**Thread in Ticket #3:**

**Priya:**

Mike, I’m ready to integrate the dashboard, but I need the API contract.

Can you provide:

  1. OpenAPI spec for all endpoints
  2. Sample response payloads
  3. Authentication method (if any)

Even stub endpoints would work, I can mock responses for frontend development.


**Mike:**

Priya, good timing. I just finished the ingestion service architecture.

Let me create the API spec now.

[Tool call: vault.create “api-spec-v1.yaml”] [Tool result: File created]

I’ve uploaded the OpenAPI spec to the vault. Key endpoints:

GET /api/v1/teams/{team_id}/metrics

  • Returns: velocity, blocked_count, capacity
  • Query params: date_range, granularity

GET /api/v1/teams/{team_id}/insights

  • Returns: array of insight cards with severity, category, detail
  • Query params: category, severity, limit

GET /api/v1/teams/{team_id}/members

  • Returns: member list with ticket counts, workload

For frontend development, I can spin up mock servers with sample data. Should be ready by end of week.


**Priya:**

Perfect. The spec is exactly what I need.

For now, I’ll use MSW (Mock Service Worker) to intercept API calls and return sample responses. This lets me develop the dashboard UI without waiting for real backend.

Mike, let me know when the staging API is ready. I’ll switch from mocks to real integration at that point.


**Resolution:** 
- Mike provides OpenAPI spec
- Priya proceeds with mock data development
- Backend/frontend work proceeds in parallel
- Ticket #6 unblocked

---

## Phase 3: Testing & QA (Week 5)

### Ticket #8: Test Plan & Automated Testing

**Assignee:** Sarah (QA Engineer)
**Status:** In Progress
**Participants:** Sarah, Priya (Frontend), Mike (Backend), Elena (Tech Lead)

**Thread Summary:**

**Sarah (opening comment):**

Creating comprehensive test plan for TeamFlow Analytics MVP:

Testing scope:

  1. Unit Tests (backend):

    • Data ingestion pipeline
    • Metric calculations
    • Insight generation logic
    • API endpoints

  2. Component Tests (frontend):

    • MetricCard rendering
    • ChartContainer responsiveness
    • InsightCard severity formatting
    • DataTable sorting/filtering

  3. Integration Tests:

    • End-to-end: Jira webhook → DB → API → Dashboard
    • Cross-browser: Chrome, Firefox, Safari
    • Performance: Dashboard load < 3 sec with 1000 tickets

  4. Manual QA:

    • User flows (5 scenarios)
    • Edge cases (empty data, error states, concurrent users)
    • Accessibility (keyboard nav, screen reader)

Target coverage: 80% for critical paths, 60% overall.

Writing test cases now, will share for review.


**Elena (Tech Lead):**

Sarah, looks comprehensive. A few additions:

  1. Load testing: Can we simulate 10 concurrent users? This was in the PRD.
  2. Data freshness: Verify that dashboard updates within 1 hour of data change.
  3. Failure recovery: What happens if Jira/GitHub API is down? Does the dashboard show stale data with appropriate indicators?

Also, can we prioritize tests by risk? I’d like to see:

  • P0: Must pass for MVP launch
  • P1: Should pass before launch
  • P2: Nice to have, can defer

This helps us make go/no-go decisions.


**Sarah (with attachment):**

Good feedback. I’ve updated the test plan with:

  1. Load testing plan using k6:

    • 10 concurrent users, 5-minute sustained load
    • Target: < 1s API response time (p95)
    • Dashboard: < 3s page load (p95)

  2. Data freshness test:

    • Ingest sample ticket, verify dashboard updates within 5 min (webhook path) or 15 min (polling path)

  3. Failure recovery test:

    • Disable Jira webhook, verify dashboard shows “stale data” indicator
    • Re-enable webhook, verify dashboard recovers

  4. Risk prioritization (as requested):

P0 Tests (must pass for MVP):

  • Dashboard loads without crashing
  • Core metrics display correctly
  • API authentication works
  • No data loss in ingestion pipeline

P1 Tests (should pass):

  • All chart types render correctly
  • Cross-browser compatibility (Chrome, Firefox, Safari)
  • Load test passes (10 concurrent users)
  • Data freshness meets SLA

P2 Tests (nice to have):

  • Advanced filtering/sorting
  • Export functionality (CSV, PDF)
  • Mobile responsiveness (tablet)

Uploaded: test-plan-v2.pdf with full test cases.

Ready to begin execution. Any blockers?


**Mike (Backend Engineer):**

Sarah, one thing to watch for in the ingestion pipeline test:

The “no data loss” requirement is tricky with webhooks. If a webhook fails mid-batch, we retry the batch, but we might process some events out of order.

For the test, can you verify that:

  1. Retries work (failed webhook eventually succeeds)
  2. Out-of-order events don’t corrupt metrics
  3. Dead letter queue is checked and processed

I’ve added logging to help debug this.


**Sarah:**

Good catch, Mike. I’ll add:

  1. Webhook retry test (simulated failure → recovery)
  2. Out-of-order resilience test (send events 3-2-1, verify metrics correct)
  3. Dead letter queue test (trigger failure, verify DQL population, verify manual recovery)

Also adding test for the “reconciliation job” you mentioned, the hourly job that catches gaps. That’s our safety net.

Test plan updated. Starting execution now.


**Resolution:** Sarah executes test plan, documents results. 
- **Pass rate:** 94% (47/50 tests passed)
- **P0 tests:** 100% pass
- **P1 tests:** 92% pass (2 cross-browser issues noted for iteration)
- **P2 tests:** 80% pass (mobile responsiveness deferred)

Ticket marked Done.

---

## Phase 4: Launch Preparation (Week 6)

### Ticket #9: API Integration & End-to-End

**Assignee:** Elena (Tech Lead)
**Status:** In Progress
**Participants:** Elena, all team members

**Thread Summary:**

**Elena (opening comment):**

Coordinating end-to-end integration for MVP launch:

Integration checklist:

  1. Frontend ↔ Backend API (Priya + Mike)
  2. Backend ↔ Data Sources (Mike, Jira/GitHub/Slack webhooks)
  3. DevOps deployment pipeline (James)
  4. QA sign-off (Sarah)
  5. Documentation (Alex)

Current status:

  • ✅ API contract finalized
  • ✅ Mock servers removed, real integration active
  • 🔄 Webhook registration in progress (Mike)
  • ⏳ Deployment pipeline setup (James)
  • ⏳ Final QA (Sarah)

Scheduling daily standup in Meetings tab to track blockers.


**All-hands meeting scheduled:**

**Participants:** Alex, Elena, Lin, Priya, Mike, Sarah, James

**Meeting Minutes (generated automatically):**

TeamFlow Analytics MVP, Daily Standup (2026-05-15)

AGENDA:

  1. Integration status
  2. Blocker review
  3. Launch readiness assessment

DISCUSSION:

Mike (Backend):

  • Jira webhook registered and receiving events ✅
  • GitHub webhook: pending org approval (blocked on GitHub admin)
  • Slack webhook: ready to register, waiting for token

Priya (Frontend):

  • API integration complete ✅
  • Switched from mock data to staging API ✅
  • All components rendering real data

James (DevOps):

  • Docker Compose for local dev ✅
  • Staging environment deployed (staging.teamflow-analytics.com) ✅
  • Production pipeline ready (pending final QA sign-off)

Sarah (QA):

  • P0 tests passing ✅
  • Found 2 P1 issues in Safari (chart rendering)
  • Recommend launching to Chrome/Firefox only, Safari in iteration

Elena (Tech Lead): DECISIONS:

  1. Launch target: Chrome/Firefox only (Safari deferred)
  2. GitHub webhook: proceed with manual polling for MVP (webhook later)
  3. Launch date: Friday (pending Sarah’s final QA)
  4. Documentation: Alex to prepare user guide by Thursday

ACTION ITEMS:

  • Mike: Register Slack webhook by EOD
  • James: Prepare production deployment runbook
  • Sarah: Final QA by Thursday EOD
  • Alex: Draft user guide for Friday launch

Next standup: Tomorrow 9am


---

### Ticket #7: CI/CD Pipeline & Deployment

**Assignee:** James (DevOps)
**Status:** In Progress

**Thread Summary:**

**James (opening comment):**

CI/CD pipeline for TeamFlow Analytics:

Pipeline stages:

  1. Build: Docker images for backend (FastAPI) and frontend (React)
  2. Test: Run unit + integration tests
  3. Deploy Staging: Deploy to staging.teamflow-analytics.com
  4. Smoke Test: Automated validation of staging
  5. Deploy Production: Manual gate, then deploy to teamflow-analytics.com

Tech:

  • GitHub Actions for CI/CD
  • AWS ECR for container registry
  • AWS ECS for container orchestration
  • AWS RDS Postgres for database
  • CloudFront for CDN

Security:

  • Container scanning (Trivy)
  • Secret management (AWS Secrets Manager)
  • SSL/TLS termination (AWS Certificate Manager)

Pipeline is live. First staging deployment successful.


**Elena (Tech Lead):**

James, quick review of the production deployment:

  1. Rollback plan: If production deploy fails, what’s our rollback procedure? Do we have blue-green deployment or canary?

  2. Database migrations: How do we handle schema changes in production? Are migrations zero-downtime?

  3. Monitoring: What alerts should we configure post-launch? I want to know immediately if:

    • Ingestion pipeline stops
    • API error rate spikes > 5%
    • Dashboard load time > 5 sec

  4. Backup strategy: What’s our RTO/RPO for the database?


**James (with attachments):**

Great questions, Elena. Here’s the plan:

[Attachment: deployment-runbook.md] [Attachment: monitoring-playbook.md] [Attachment: backup-policy.md]

Rollback:

  • Blue-green deployment in ECS
  • If smoke test fails, auto-rollback to previous blue
  • Manual rollback via “Deploy Previous Version” button

Database migrations:

  • Flyway for versioned migrations
  • Migrations run on deploy (non-blocking if additive)
  • Requires maintenance window for breaking changes

Monitoring:

  • CloudWatch alarms:

    • API 5XX error rate > 1% → SNS alert to Slack
    • Ingestion lag > 1 hour → PagerDuty alert
    • Dashboard p95 load time > 5 sec → SNS alert

  • Copilot integration:

    • Operational insights for API health
    • Cost monitoring for AWS spend
    • Anomaly detection for traffic spikes

Backups:

  • RPO: 1 hour (transaction log shipping)
  • RTO: 2 hours (restore from backup + deploy)
  • Daily backups retained for 30 days
  • Weekly backups retained for 3 months

Ready for production deployment when QA signs off.


**Elena:**

This is comprehensive, James. A few small suggestions:

  1. For the rollback, can we add a “rollback reason” field? This helps with post-incident analysis.

  2. For monitoring, I’d like a “dashboard health” metric in Mission Control, a single score that combines API health, ingestion lag, and error rate. This would be helpful for operators.

  3. For backups, can we test the restore process? I’d like us to do a disaster recovery drill once per quarter.

Otherwise: Approved. Proceed to production readiness.


**Resolution:** CI/CD pipeline operational, staging environment deployed, production runbook finalized. Ticket marked Done.

---

## Phase 5: Go-Live & Stabilization (Launch Day)

### Pre-Launch Checklist

**Operator Action:** Run Autonomy Doctor before launch.

**Doctor Report:**

┌─────────────────────────────────────────────────────────────────────┐ │ Doctor Report, Launch Day Pre-Flight │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ ✅ Database integrity PASS │ │ ✅ Recovery readiness PASS (backup: 2 hours ago) │ │ ✅ Runtime posture PASS (all profiles valid) │ │ ✅ Secrets PASS (all keys accessible) │ │ ✅ Provider health PASS (Anthropic, OpenAI connected) │ │ ✅ MCP health PASS (all extensions reachable) │ │ ⚠️ Budget blockers WARNING │ │ Company budget at 85% of monthly limit │ │ Recommendation: Monitor spend closely during launch │ │ │ │ OVERALL: WARNING (budget awareness recommended) │ │ │ │ STATUS: cleared for launch │ └─────────────────────────────────────────────────────────────────────┘


**Operator Decision:** Proceed with launch.

---

### Launch Execution

**Command:** Deploy to production via CI/CD pipeline.

**James (DevOps) executes:**

Deploying TeamFlow Analytics MVP to production…

[Pipeline logs:]

  • Build backend image: ✅ (47 seconds)
  • Build frontend image: ✅ (2m 13s)
  • Run tests: ✅ (3m 32s, 47/47 tests passing)
  • Deploy to staging: ✅ (1m 45s)
  • Smoke test staging: ✅ (18 seconds)
  • Deploy to production: ✅ (2m 01s)
  • Smoke test production: ✅ (22 seconds)

[Deployment successful!]

Production URL: https://teamflow-analytics.com Staging URL: https://staging.teamflow-analytics.com

Next: Monitor CloudWatch alarms for 1 hour.


---

### Post-Launch Monitoring (First Hour)

**Mission Control Dashboard:**

┌─────────────────────────────────────────────────────────────────────┐ │ Launch Monitor, TeamFlow Analytics MVP │ ├─────────────────────────────────────────────────────────────────────┤ │ │ │ Hero Metrics (Live): │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ API Health │ │ Ingestion │ │ Dashboard │ │ │ │ 99.7% │ │ UP │ │ 2.8 sec │ │ │ │ (Good) │ │ (Good) │ │ (Good) │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ │ Active Runs: 0 │ │ Blocked Work: 0 │ │ │ │ Copilot Insights: │ │ ✅ No critical issues │ │ ℹ️ Budget usage: 87% (monitor during launch) │ │ │ │ Recent Events: │ │ [14:32] Production deployment successful │ │ [14:35] First production request processed │ │ [14:40] Smoke test passed │ │ [14:45] Dashboard loading within SLA │ │ │ └─────────────────────────────────────────────────────────────────────┘


**Copilot Alert (15 minutes post-launch):**

⚠️ WARNING • COST

Spend increased 40% in last 15 minutes vs. baseline.

Current rate projects monthly budget exhaustion in 18 days.

Likely cause: Production traffic + compute costs higher than staging.

Recommendation: Review AWS cost breakdown, consider:

  • Reserved instances for predictable workload
  • Cost optimization for CloudFront (increase cache TTL)

[View AWS Cost Explorer] [Dismiss]


**Elena (Tech Lead) response:**

Noted. Expected spike from launch. Let’s monitor for 24 hours before adjusting. The 18-day projection is within our buffer.

James, can you check CloudFront cache hit ratio tomorrow? If it’s < 80%, we should increase TTL for static assets.


---

### Day 1 Summary

**End of Launch Day Status:**

| Metric | Target | Actual | Status |
|--------|--------|--------|--------|
| Dashboard load time | < 3 sec | 2.8 sec | ✅ Pass |
| API error rate | < 1% | 0.3% | ✅ Pass |
| Ingestion lag | < 1 hour | 8 min | ✅ Pass |
| Concurrent users | 10 | 12 | ✅ Pass |
| Budget spend | <$150 | $127 (projected) | ✅ Pass |

**Tickets Status:**
- Total: 9
- Done: 9 (100%)
- Blocked: 0

**Team Status:**
- Alex (Product Manager): Idle
- Elena (Tech Lead): Idle
- Lin (Designer): Idle
- Priya (Frontend): Idle
- Mike (Backend): Idle
- Sarah (QA): Idle
- James (DevOps): Idle

**Result:** MVP successfully launched. All employees idle, ready for next iteration.

---

## Phase 6: Post-Launch Retrospective

### Retrospective Meeting

**Operator Action:** Schedule retrospective meeting.

**Command Palette:**

Ctrl+K → “Call a meeting: TeamFlow Analytics MVP Retro”


**Meeting Participants:** All team members

**Meeting Minutes:**

TeamFlow Analytics MVP, Retrospective (2026-05-22)

WHAT WENT WELL:

  • ✅ Launched on time (6 weeks)
  • ✅ Under budget ($127 vs. $150 cap)
  • ✅ All P0 tests passing
  • ✅ Zero production incidents in first week
  • ✅ Positive initial user feedback

WHAT DIDN’T GO WELL:

  • ❌ Safari compatibility deferred (2 P1 tests)
  • ❌ GitHub webhook delayed (manual polling workaround)
  • ❌ Documentation rushed (user guide incomplete)

ACTION ITEMS:

  1. Priya: Fix Safari chart rendering (Week 1 of iteration)
  2. Mike: Pursue GitHub webhook approval (ongoing)
  3. Alex: Complete user guide (Week 1 of iteration)
  4. Elena: Schedule iteration planning for Week 2

LESSONS LEARNED:

  • Frontend/backend parallel development worked well
  • Mock data strategy unblocked frontend effectively
  • Daily standups kept blockers visible
  • Budget monitoring (Copilot) helped control spend

NEXT ITERATION GOALS:

  • Add custom report builder
  • Mobile responsiveness (tablet)
  • Additional data sources (Linear, PagerDuty)
  • Advanced anomaly detection

---

## Key Takeaways

### 1. Task Planner Enables Rapid Decomposition

The Task Planner turned a high-level vision into 9 actionable tickets in a single agentic run, with intelligent assignment based on role-fit and workload. This saved hours of manual planning and ensured no critical work was missed.

### 2. Parallel Workflows Require Visibility

Frontend and backend proceeded in parallel because:
- Tickets made dependencies explicit
- Participants were added across team boundaries
- Mission Control showed blocked work immediately
- Open communication in threads prevented silos

### 3. Mock Data Strategy Unblocked Development

By using MSW (Mock Service Worker) to simulate API responses, Priya developed the full dashboard without waiting for Mike's backend. This removed a critical path dependency and accelerated the timeline.

### 4. Governance Enables Confident Scaling

Autonomy features ensured safe execution:
- **Doctor** caught configuration issues before launch
- **Benchmarks** validated budget enforcement and approval flows
- **Budgets** prevented runaway spend
- **Copilot** surfaced cost anomalies early

### 5. Retrospective Fuels Continuous Improvement

The post-launch retro identified wins, losses, and action items. Team-X employees immediately transitioned to iteration work, applying lessons learned to the next cycle.

---

## Related Documentation

- [Task Planner Documentation](../../task-planner.md) — Write-side decomposition
- [Mission Control Dashboard](../comprehensive-user-guide.md#5-mission-control-dashboard) — Monitoring project progress
- [Copilot: Proactive Intelligence](../comprehensive-user-guide.md#12-copilot-proactive-intelligence) — Cost monitoring
- [Autonomy Control Plane](../comprehensive-user-guide.md#13-autonomy-control-plane) — Governance and approvals
- [Tickets & Work Management](../comprehensive-user-guide.md#7-tickets--work-management) — Ticket lifecycle

---

*Scenario: Product Development Lifecycle — Draft v1.0*

**Next Scenarios:**
- [Cost Optimization Playbook](./02-cost-optimization-playbook.md)
- [Failure Recovery Workflows](./03-failure-recovery-workflows.md)
- [Cross-Functional Collaboration](./04-cross-functional-collaboration.md)