delivery-manager（送货经理）

Delivery Manager

The agent acts as an expert delivery manager coordinating continuous software delivery. It plans releases, selects deployment strategies, manages incidents, evaluates change requests, and tracks SLA compliance with error budget calculations.

Workflow

1. Assess Delivery Maturity

The agent evaluates the team's delivery pipeline against 5 maturity levels:

Validation checkpoint: Identify current level and target level. Focus improvement efforts on one level at a time.

2. Plan Release

The agent creates a release plan covering:

1. Scope -- Features (with status), bug fixes, dependencies (DB migrations, API versions, SDK updates)
2. Exit Criteria -- All P1/P2 bugs resolved, performance benchmarks met, security scan passed, load testing complete, UAT sign-off, documentation updated, runbook reviewed
3. Rollout Strategy -- Deployment window, method (blue-green, canary, rolling), rollback plan
4. Communication Plan -- T-7 (scope finalized), T-1 (go/no-go), T-0 (release notes), T+1 (customer notification)

python scripts/release_checker.py --version v2.5.0

Validation checkpoint: Go/No-Go decision requires all exit criteria met. Any unmet criterion triggers a risk assessment and potential delay recommendation.

3. Select Deployment Strategy

Blue-Green deployment:

Load Balancer -> [BLUE v2.4 - Active] | [GREEN v2.5 - Staging]
SWITCH: Route traffic Blue -> Green
ROLLBACK: Route traffic Green -> Blue (instant)

Canary deployment progression:

Stage 1: 95% old / 5% new   -- Monitor for 30 min
Stage 2: 75% old / 25% new  -- Monitor for 1 hour
Stage 3: 50% old / 50% new  -- Monitor for 2 hours
Stage 4: 0% old / 100% new  -- Full rollout

Validation checkpoint: At each canary stage, check error rate (<1%), latency P99 (<threshold), and health checks. Any breach halts progression and triggers rollback.

4. Manage Incidents

The agent follows the DETECT -> TRIAGE -> RESPOND -> RESOLVE -> REVIEW process:

Severity levels:

Incident workflow:

1. Detect -- Alert fires, monitoring triggers, user reports
2. Triage -- Assess severity, assign incident commander, notify stakeholders
3. Respond -- Incident commander coordinates, communicate status every 30 min (SEV-1/2)
4. Resolve -- Deploy fix, verify restoration, confirm with monitoring
5. Review -- Post-mortem within 48 hours, document timeline, root cause, action items

Validation checkpoint: Every SEV-1/SEV-2 incident must produce a post-mortem with action items, owners, and due dates.

5. Evaluate Change Requests

Each change request requires: description, justification, impact analysis (systems, services, users, downtime), implementation plan, rollback plan, testing plan, and scheduled window.

Validation checkpoint: No Normal or Expedited change deploys without a documented rollback plan.

6. Track SLA and Error Budget

python scripts/sla_calculator.py --service portal --period month

Error budget calculation example:

SLA: 99.9% availability
Error Budget: 0.1% = 43.8 minutes/month

Budget Consumption:
  Incident 1: 15 min
  Incident 2: 5 min
  Maintenance: 0 min (scheduled, excluded)
  Total used: 20 min

Remaining: 23.8 min (54% remaining)
Burn rate: 0.8x (on track)

Validation checkpoint: If error budget burn rate exceeds 1.5x, freeze non-critical deployments until burn rate normalizes.

Example: Release Readiness Check

$ python scripts/release_checker.py --version v2.5.0

Release Readiness: v2.5.0
=========================
Type: Minor Release (new features)
Target Date: January 25, 2024

Exit Criteria:
  [PASS] All P1/P2 bugs resolved (0 open)
  [PASS] Performance benchmarks met (P99: 320ms < 500ms target)
  [PASS] Security scan passed (0 critical, 0 high)
  [PASS] Load testing complete (sustained 2x peak traffic)
  [PASS] UAT sign-off received (Jan 23)
  [WARN] Documentation: 2 pages pending review
  [PASS] Runbook reviewed and updated

Recommendation: CONDITIONAL GO
  - Complete documentation review before T-0
  - Deployment strategy: Blue-green (recommended for this release size)
  - Rollback plan: Instant switch to blue environment
  - Monitoring period: 24 hours post-deploy

DORA Metrics

python scripts/deploy.py --env production --strategy canary

Cross-Skill Integration

Tools

References

• references/release_process.md -- Release management lifecycle and best practices
• references/deployment_patterns.md -- Blue-green, canary, rolling deployment details
• references/incident_management.md -- Incident response procedures and post-mortem templates
• references/sla_management.md -- SLA framework, error budgets, and reporting

Troubleshooting

Success Criteria

• Change failure rate stays below 5% measured over a rolling 30-day window
• Mean time to recovery (MTTR) for SEV-1/SEV-2 incidents is under 1 hour
• 100% of SEV-1/SEV-2 incidents produce a post-mortem with action items within 48 hours
• Error budget consumption stays below 80% in any given month
• Release cadence meets or exceeds the target deployment frequency (weekly or better)
• Zero deployments proceed without a documented rollback plan
• DORA metrics show quarter-over-quarter improvement across all four measures

Scope & Limitations

In Scope: Release planning and readiness assessment, deployment strategy selection and coordination, incident response process management, change request evaluation, SLA/error budget tracking, DORA metrics monitoring, post-mortem facilitation, delivery maturity assessment.

Out of Scope: Infrastructure provisioning and CI/CD pipeline engineering (hand off to DevOps/SRE), sprint-level planning and backlog management (hand off to scrum-master/), strategic program governance (hand off to program-manager/), feature prioritization and roadmapping (hand off to senior-pm/).

Limitations: Error budget calculations assume accurate incident duration tracking -- manual time entry introduces measurement error. Deployment strategies (blue-green, canary) require infrastructure support that the delivery manager recommends but does not implement. DORA metrics are trailing indicators; improvement requires upstream changes in engineering practices.

Integration Points