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:

Level Name Characteristics

1 Manual Delivery Manual builds, manual testing, manual deploys, reactive monitoring

2 Automated Build/Test CI pipeline, automated unit tests, manual deploys, basic monitoring

3 Continuous Delivery Full CI/CD, automated testing, push-button deploys, comprehensive monitoring

4 Continuous Deployment Automated deploys, feature flags, canary releases, self-healing systems

5 DevOps Excellence Zero-downtime deploys, automated rollbacks, chaos engineering, full observability

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:

• Scope -- Features (with status), bug fixes, dependencies (DB migrations, API versions, SDK updates)

• Exit Criteria -- All P1/P2 bugs resolved, performance benchmarks met, security scan passed, load testing complete, UAT sign-off, documentation updated, runbook reviewed

• Rollout Strategy -- Deployment window, method (blue-green, canary, rolling), rollback plan

• 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

Strategy When to Use Rollback Speed Risk Level

Blue-Green Need instant rollback, have 2x infrastructure Instant (switch traffic) Low

Canary Want to validate with subset of users first Fast (stop traffic shift) Low-Medium

Rolling Cost-constrained, can tolerate mixed versions Moderate (re-deploy old) Medium

Big Bang Small app, low traffic, maintenance window OK Slow (full redeploy) High

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:

Severity Criteria Response Time Resolution Target

SEV-1 Complete outage or data loss 15 minutes 4 hours

SEV-2 Major feature unavailable 30 minutes 8 hours

SEV-3 Minor feature impact, workaround available 2 hours 24 hours

SEV-4 Cosmetic, no customer impact 8 hours 5 days

Incident workflow:

• Detect -- Alert fires, monitoring triggers, user reports

• Triage -- Assess severity, assign incident commander, notify stakeholders

• Respond -- Incident commander coordinates, communicate status every 30 min (SEV-1/2)

• Resolve -- Deploy fix, verify restoration, confirm with monitoring

• 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

Change Type Approval Required Lead Time

Standard None (pre-approved, low risk) 0

Normal CAB (Change Advisory Board) 5 days

Expedited Manager approval 24 hours

Emergency On-call approval 0

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

Metric Definition Elite Target

Deployment Frequency Deploys per day/week Multiple per day

Lead Time for Changes Commit to production <1 hour

Change Failure Rate Failed deployments % <5%

MTTR Mean time to recovery <1 hour

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

Cross-Skill Integration

Activity Primary Skill Delivery Manager Contribution

Release notes execution/release-notes/

Provides ticket list, timeline, deployment details

Stakeholder notification senior-pm/

Aligns communication plan with release calendar

Sprint demo coordination scrum-master/

Confirms demo-ready state matches release scope

Launch risk assessment discovery/pre-mortem/

Supplies deployment risk data for Tiger classification

Tools

Tool Purpose Command

release_checker.py

Check release readiness against exit criteria python scripts/release_checker.py --version v2.5.0

deploy.py

Coordinate deployment with selected strategy python scripts/deploy.py --env production --strategy canary

sla_calculator.py

Calculate SLA compliance and error budget python scripts/sla_calculator.py --service portal --period month

incident_report.py

Generate incident report from timeline data python scripts/incident_report.py --id INC-2024-0125

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