Deployment & Maintenance (DM) Principles

Enterprise Architecture principles for AI operations lifecycle management.

Category Overview

flowchart LR
    Dev["Development"] --> Build["Build"] --> Test["Test"] --> Deploy["Deploy"] --> Operate["Operate"] --> Monitor["Monitor"]

    Monitor --> DM["DM PRINCIPLES"]

    subgraph DM["DM PRINCIPLES"]
        DM001["DM-001: Pipeline Integration"]
        DM002["DM-002: Observability First"]
    end

Key Concerns:

• CI/CD pipeline architecture for AI-generated code
• Quality gates with human approval points
• Monitoring AI-assisted system behavior
• Incident response for AI-related issues
• Continuous improvement feedback loops

Principles in This Category

Relationship to Other Categories

flowchart TB
    TQC["TQC: Testing &<br/>Quality Control<br/><i>Quality standards feed into gates</i>"]

    TQC --> DM

    DC["DC: Development<br/>& Coding<br/><i>Code generation practices</i>"]
    DM["DM: Deployment &<br/>Maintenance<br/><i>Pipeline + Monitor practices</i>"]
    GSC["GSC: Governance<br/>Security<br/><i>Compliance audit requirements</i>"]

    DC --> DM
    DM --> GSC

DM-001: Pipeline Integration with Quality Gates

Statement

All AI-generated code must pass through automated quality gates with mandatory human approval before production deployment.

Rationale

Implications

flowchart LR
    subgraph Pipeline["PIPELINE ARCHITECTURE"]
        Commit["Commit"] --> Build["Build"] --> Test["Test"] --> Security["Security"] --> Review["Review"] --> Stage["Stage"] --> Prod["Prod"]
    end

    Build -.-> Auto1["AUTO"]
    Test -.-> Auto2["AUTO"]
    Security -.-> Auto3["AUTO"]
    Review -.-> Human["HUMAN<br/><i>Mandatory Approval</i>"]

Quality Gates

Maturity Alignment

Governance

Compliance Measures

• All repositories have CI/CD pipeline configured
• Security scanning enabled for all code paths
• Human approval required before production deployment
• Gate failures documented and tracked
• Exception requests require documented justification

Exception Process

Related Principles

• TQC-003: Quality Assurance Framework (quality standards for gates)
• TQC-002: Security Practices (security gate requirements)
• DC-003: Code Review Practices (human review requirements)
• GSC-001: Governance Framework (compliance requirements)

DM-002: Observability First

Statement

Establish comprehensive observability (logs, metrics, traces) for AI-assisted systems before deployment to production.

Rationale

Implications

flowchart TB
    subgraph Pillars["THREE PILLARS OF OBSERVABILITY"]
        Logs["📋 LOGS<br/><i>What happened</i><br/>Errors, Warnings, Info, Debug"]
        Metrics["📊 METRICS<br/><i>How much/many</i><br/>Latency, Throughput, Error rate"]
        Traces["🔍 TRACES<br/><i>Request flow</i><br/>Span data, Context, Timing"]
    end

    subgraph Engine["CORRELATION ENGINE"]
        Unified["Unified view of system behavior and health"]
    end

    subgraph Outputs["OUTPUTS"]
        Dashboards["Dashboards"]
        Alerts["Alerts"]
        Runbooks["Runbooks"]
    end

    Logs --> Engine
    Metrics --> Engine
    Traces --> Engine
    Engine --> Outputs

Key Monitoring Areas

Monitoring Coverage Matrix

Legend: ● Required

○ Recommended

Alert Classification

Maturity Alignment

Governance

Compliance Measures

• All services have logging, metrics, and traces configured
• Critical alerts have associated runbooks
• SLOs defined and tracked for key services
• Incident response procedures documented
• Regular observability audits conducted

Incident Response Process

flowchart LR
    Detect["DETECT<br/><i>Alerts trigger response</i>"]
    Triage["TRIAGE<br/><i>Severity assessment</i>"]
    Investigate["INVESTIGATE<br/><i>Log/trace analysis</i>"]
    Resolve["RESOLVE<br/><i>Fix/deploy verification</i>"]
    Review["REVIEW<br/><i>Post-incident review</i>"]

    Detect --> Triage --> Investigate --> Resolve --> Review

AI-Specific Considerations:

• Check if AI-generated code is involved
• Review recent AI-assisted changes
• Document AI tool involvement in incident
• Feed learnings back to AI training/configuration

Related Principles

• DM-001: Pipeline Integration (deployment triggers monitoring setup)
• TQC-002: Security Practices (security monitoring requirements)
• GSC-001: Governance Framework (audit and compliance monitoring)
• DC-004: Agentic Development (autonomous agent monitoring)

Category Summary

Principle Matrix

Legend: Requirements increase with maturity level

Key Takeaways

1. Quality gates are non-negotiable - Every deployment path includes automated and human checkpoints
2. Human approval remains mandatory - Production deployments require explicit human sign-off
3. Observability enables confidence - You can’t manage what you can’t measure
4. Maturity unlocks automation - Higher levels enable AI-assisted operations
5. Feedback loops improve AI - Monitoring data informs AI tool effectiveness

Next Steps

License