ATVC v1.0

AI/ML Ops Roadmap
Enterprise Agentic System Validation

ATVC is a 100-step work breakdown structure for enterprise AI/ML systems. Four phases take an agentic system from shared vocabulary through production trust in a structured, auditable sequence. 211 validation documents ensure nothing is assumed, everything is verified.

Ontology

Steps 1–25 · Foundation & Discovery

Architecture

Steps 26–50 · Design & Infrastructure

Engineering

Steps 51–75 · Build & Harden

Enablement

Steps 76–100 · Operate & Sustain

Ontology Phase 1 — Foundation

Define the conceptual foundation. Align on vocabulary, entities, relationships, boundaries, and the problem space before building anything expensive.

Before writing any code or training any model, the organization must agree on what words mean. Ontology is the disciplined practice of naming things, defining their relationships, and establishing the boundaries that separate one concept from another. This phase forces alignment on vocabulary that will later become schemas, labels, and embeddings. Mistakes here propagate through the entire system.

View All 25 Steps & Validation Documents +

Executive Sponsorship & Charter

Secure executive sponsor, define charter scope, budget envelope, and kill criteria.

1.1

Charter Document

Signed project charter with scope boundaries, success criteria, budget ceiling, and executive sign-off.

1.2

Kill Criteria Memo

Explicit conditions under which the initiative is terminated, with financial thresholds.

Stakeholder Identification & Mapping

Identify all stakeholders with influence and interest ratings.

2.1

Stakeholder Register

Complete registry with names, roles, influence/interest grid, and communication preferences.

2.2

RACI Matrix v0

Initial responsibility assignment for Phase 1 decisions and deliverables.

Domain Expert Access & Interview Protocol

Identify who holds the knowledge, how deep it goes, and how to extract it.

3.1

Expert Stakeholder Map

Knowledge holders with depth assessment and availability matrix.

3.2

Interview Schedule & Protocol

Timeline with concept extraction methodologies.

3.3

Knowledge Source Priority Matrix

Ranked experts, customers, partners with access strategy.

Concept Harvesting & Terminology Extraction

Extract domain concepts from documents, interviews, observations, and existing systems.

4.1

Terminology Extraction Report

Domain concepts with frequency analysis from multiple sources.

4.2

Concept Laddering Results

Hierarchical relationships from structured interviews.

4.3

Cross-Source Consistency Analysis

Validation matrix comparing concepts across channels.

Relationship Mapping & Hierarchy Construction

Build structural relationships—taxonomies, part-whole, and associations.

5.1

Taxonomic Hierarchy Model

Is-a relationships with inheritance rules and classification logic.

5.2

Part-Whole Relationship Map

Component dependencies and composition rules.

5.3

Associative Relationship Network

Related-to connections with strength weights.

Formal Ontology Representation

Capture the ontology in formats that can be reviewed, versioned, and enforced.

6.1

Concept Glossary & Definition Framework

Definitions, synonyms, examples, and measurement criteria.

6.2

Relationship Diagram Library

Visual representations of concept connections.

6.3

Decision Rationale Documentation

Reasoning for contested concepts with evidence.

Problem Space Definition & Scoping

Define what is in scope, what is out, and what success looks like.

7.1

Boundary Definition & Scope Constraints

Hard boundaries, soft boundaries, and explicit exclusions with rationale.

7.2

Problem Statement Document

Formal articulation in both business and technical language.

Multi-Perspective Validation

Cross-stakeholder review ensuring the problem is understood from all perspectives.

8.1

Stakeholder Validation Sign-off Matrix

Each stakeholder group confirms understanding and agreement.

8.2

Assumption Register

Every assumption documented with owner, validation plan, and time-box.

Stress Testing & Edge Case Exploration

Adversarial questioning of scope boundaries and assumptions.

9.1

Edge Case Catalog

Boundary conditions, corner cases, and adversarial scenarios.

9.2

Assumption Stress Test Results

Findings from deliberate challenges to core assumptions.

ML Problem Statement Translation

Translate business needs into specific ML formulations with measurable success criteria.

10.1

ML Problem Formulation

Business objectives mapped to ML task types with metrics.

10.2

Success Criteria Specification

Quantitative thresholds for model performance, latency, cost, and business impact.

Data Availability & Quality Assessment

Inventory what data actually exists, its quality, gaps, and collection requirements.

11.1

Data Inventory Report

Complete catalog of available data sources with volume, freshness, and access methods.

11.2

Data Quality Assessment

Completeness, accuracy, consistency, and timeliness scores per source.

11.3

Gap Analysis & Collection Plan

Missing data identified with acquisition strategy.

Regulatory & Ethical Constraint Mapping

Map compliance requirements, ethical boundaries, and governance obligations.

12.1

Regulatory Constraint Map

Applicable regulations with specific requirements per system component.

12.2

Ethical Boundary Framework

Explicit ethical constraints on model behavior, data usage, and output.

12.3

Governance Obligation Matrix

Audit, reporting, and documentation requirements per regulatory body.

Feasibility Analysis & Technical Risk Assessment

Determine whether the ML approach is viable given data, constraints, and capability.

13.1

Technical Feasibility Report

Assessment of whether current state-of-art can solve the problem within constraints.

13.2

Risk Register v1

Technical, organizational, and regulatory risks ranked by probability and impact.

Build vs. Buy vs. Partner Analysis

Evaluate whether to build internally, purchase vendor solutions, or engage partners.

14.1

Build/Buy/Partner Decision Matrix

Comparison across cost, time, risk, IP ownership, and strategic alignment.

14.2

Vendor Landscape Assessment

Evaluation of available commercial solutions with capability gaps.

Data Governance & Lineage Framework

Establish data governance policies, ownership, and lineage tracking from source to model.

15.1

Data Governance Policy

Ownership, access controls, retention, and deletion rules per data category.

15.2

Data Lineage Framework

Tracking methodology from raw source through transformation to model input.

Labeling Strategy & Annotation Guidelines

Define how training data will be labeled, who labels it, and how quality is ensured.

16.1

Annotation Guidelines Document

Label definitions, examples, edge cases, and inter-annotator agreement targets.

16.2

Labeling Workflow Design

Pipeline from raw data to labeled dataset with QA checkpoints.

Bias & Representation Audit (Data)

Assess training data for demographic bias, underrepresentation, and potential harm.

17.1

Data Bias Audit Report

Demographic distribution analysis, underrepresented groups, and proxy variable identification.

17.2

Representation Gap Plan

Strategy to address identified biases through collection, augmentation, or weighting.

Privacy Impact Assessment

Evaluate privacy implications of data collection, model training, and inference.

18.1

Privacy Impact Assessment (PIA)

Formal assessment of personal data processing with risk mitigation measures.

18.2

Data Minimization Plan

Strategy to collect only necessary data and anonymize where possible.

Success Metric Hierarchy

Define the cascade from business KPIs to model metrics to operational telemetry.

19.1

Metric Hierarchy Document

Business KPIs to model metrics to operational metrics with causal linkage.

19.2

Measurement Methodology

How each metric is calculated, frequency, and responsible party.

Organizational Readiness Assessment

Evaluate whether the organization has the skills, culture, and processes to operate an AI system.

20.1

Readiness Assessment Report

Skills gap analysis, cultural readiness, process maturity evaluation.

20.2

Training Needs Analysis

Role-specific training requirements for operators, users, and leadership.

Economic Model & Unit Economics

Define the unit of AI work and establish cost-per-inference, cost-per-decision economics.

21.1

Unit Economics Model

Cost per inference, cost per decision, cost per user interaction with scaling projections.

21.2

Budget Allocation Plan

Phase-by-phase budget with contingency and kill thresholds.

Competitive & Market Intelligence

Understand how competitors and the market are approaching similar problems.

22.1

Competitive Intelligence Brief

How peers solve this problem, their tech stacks, and published results.

22.2

Market Readiness Assessment

Customer willingness, market timing, and differentiation opportunity.

Communication & Change Management Plan

Plan how the initiative will be communicated across the organization.

23.1

Communication Plan

Stakeholder-specific messaging, cadence, channels, and escalation triggers.

23.2

Change Impact Assessment

Who is affected, how their work changes, and resistance mitigation strategy.

Phase 1 Integration & Consistency Review

Cross-review all Phase 1 deliverables for internal consistency and completeness.

24.1

Phase 1 Consistency Review Report

Cross-reference check of all ontology, problem, and discovery deliverables.

24.2

Open Questions Register

Unresolved items with owners, deadlines, and escalation paths.

Phase 1 Gate Review & Exit Certification

Formal gate review: all Phase 1 contracts must be explicit, reviewed, and owned.

25.1

Phase 1 Gate Review Package

Complete deliverable inventory with sign-off status.

25.2

Phase 1 Exit Certificate

Formal certification that ontology, problem, and discovery are validated.

25.3

Go/No-Go Decision Record

Decision with rationale, conditions, and dissent documentation.

Architecture Phase 2 — Design

Design the end-to-end system. Reduce ambiguity so teams stop arguing and start shipping.

Architecture is where conceptual clarity becomes structural commitment. This phase translates the ontology and problem definition into system design decisions: what gets built, how it connects, where it runs, and who owns what. Every decision here is a bet on how the system will behave under production load, regulatory scrutiny, and organizational change.

View All 25 Steps & Validation Documents +

End-to-End Pipeline Architecture Design

Design the complete ML pipeline: ingestion, feature engineering, training, evaluation, serving, monitoring.

26.1

Pipeline Architecture Diagram

End-to-end flow with component specifications, data contracts, and failure modes.

26.2

Architecture Decision Records (ADRs)

Documented decisions with context, options considered, and rationale.

Serving Pattern Selection

Choose batch vs. real-time vs. streaming with latency, cost, and complexity tradeoffs.

27.1

Serving Pattern Analysis

Comparison matrix with latency, cost, complexity, and scaling characteristics.

27.2

Inference Architecture Design

Detailed design with load balancing, autoscaling, and failover.

27.3

Performance Requirements Spec

SLA definitions, throughput targets, latency p50/p95/p99 requirements.

Cloud Provider & Compute Strategy

Select compute infrastructure with GPU/TPU selection, multi-cloud vs. single-cloud analysis.

28.1

Cloud Strategy Document

Provider selection with cost modeling, lock-in analysis, and migration path.

28.2

Compute Sizing & Cost Model

GPU/TPU selection with performance benchmarks and cost projections.

Infrastructure as Code (IaC) Foundation

Establish reproducible, version-controlled infrastructure using Terraform, Helm, or Pulumi.

29.1

IaC Module Library

Terraform/Pulumi modules for all infrastructure components with documentation.

29.2

Environment Promotion Strategy

Dev to staging to production pipeline with drift detection.

Security Architecture & Compliance Posture

Design VPC, IAM policies, data residency, encryption—non-negotiable foundations.

30.1

Security Architecture Document

Network topology, IAM policies, encryption strategy, and threat model.

30.2

Compliance Posture Assessment

Mapping of security controls to regulatory requirements with gap analysis.

Schema Registry & Data Contracts

Define versioned schemas with backward/forward compatibility rules.

31.1

Schema Registry Design

Schema evolution strategy with compatibility rules and validation.

31.2

Data Contract Specifications

Producer-consumer contracts with SLAs, quality guarantees, and breach procedures.

Feature Store Design & Implementation

Design online/offline feature serving with consistency guarantees.

32.1

Feature Store Architecture

Online/offline serving topology with consistency model.

32.2

Feature Catalog

Registry of all features with definitions, owners, lineage, and freshness SLAs.

Model Versioning & Artifact Management

Configure MLflow/DVC, artifact storage, and lineage tracking.

33.1

Model Registry Design

Versioning strategy, artifact storage, promotion workflow, and rollback procedures.

33.2

Lineage Tracking Specification

End-to-end traceability from data version to model version to deployment.

ETL/ELT Pipeline Design

Design data extraction, transformation, and loading pipelines with idempotency.

34.1

ETL Pipeline Specification

Extraction sources, transformation logic, loading targets with error handling.

34.2

Data Quality Gates

Automated checks at each pipeline stage with failure modes and alerts.

Training Pipeline Specification

Design the model training workflow including hyperparameter tuning and distributed training.

35.1

Training Pipeline Design

Workflow orchestration, compute allocation, checkpointing, and resumption.

35.2

Hyperparameter Tuning Strategy

Search methodology, resource budget, and early stopping criteria.

Evaluation Pipeline & Metric Framework

Build automated evaluation infrastructure for continuous model assessment.

36.1

Evaluation Pipeline Design

Automated eval runs with metric computation, slicing, and regression detection.

36.2

Metric Catalog

All metrics with definitions, computation methods, thresholds, and owners.

Orchestration Runtime Design

Design the runtime layer that coordinates multi-agent workflows and tool execution.

37.1

Orchestration Architecture

Agent coordination patterns, task routing, tool execution, and state management.

37.2

Agent Communication Protocol

Message formats, handoff procedures, and error propagation between agents.

Platform Infrastructure Blueprint

Design shared platform services: container orchestration, service mesh, secrets management, CI/CD.

38.1

Platform Blueprint

Kubernetes configuration, service mesh topology, and shared services catalog.

38.2

CI/CD Pipeline Design

Build, test, deploy pipeline with gates, approvals, and rollback automation.

API Design & Contract-First Development

Design all system APIs with OpenAPI specs, versioning, and backward compatibility.

39.1

API Specification Library

OpenAPI/gRPC specs for all internal and external interfaces.

39.2

API Versioning & Deprecation Policy

Version lifecycle, sunset timelines, and migration support.

Reproducible Build Environment

Configure deterministic builds—Docker, requirements pinning, conda environments.

40.1

Build Reproducibility Spec

Docker images, dependency pinning, and deterministic build verification.

40.2

Development Environment Setup Guide

One-command developer onboarding with verified parity to CI/CD.

Baseline Model & Error Analysis

Build the simplest viable model to establish performance floor and error taxonomy.

41.1

Baseline Model Report

Simplest model with documented performance, error analysis, and improvement hypotheses.

41.2

Error Taxonomy

Classification of model errors by type, severity, and root cause.

Telemetry & Instrumentation Design

Design telemetry for latency, drift, bias, and cost—if you cannot measure it, you cannot manage it.

42.1

Telemetry Architecture

What to measure, where to measure it, collection pipeline, and storage.

42.2

Dashboard Specification

Layout, metrics, refresh cadence, and alert integration for each persona.

Cost Allocation & Chargeback Model

Design cost tracking at the team, project, and inference level.

43.1

Cost Allocation Framework

Tagging strategy, allocation rules, and chargeback/showback model.

43.2

Cost Dashboard Specification

Real-time cost visibility per team, model, and environment.

Disaster Recovery & Business Continuity

Design recovery procedures for infrastructure failure, data corruption, and model degradation.

44.1

DR/BC Plan

Recovery time objectives (RTO), recovery point objectives (RPO), and failover procedures.

44.2

Backup & Restore Specification

Data backup strategy, model artifact backup, and restore verification.

Multi-Tenancy & Isolation Design

Design tenant isolation, resource quotas, and data separation.

45.1

Multi-Tenancy Architecture

Isolation model, resource quotas, data separation, and noisy-neighbor prevention.

45.2

Tenant Onboarding Specification

Automated provisioning, configuration, and validation for new tenants.

Integration Architecture & System Boundaries

Define how the ML system integrates with existing enterprise systems.

46.1

Integration Architecture Diagram

All system touchpoints with protocols, authentication, and failure modes.

46.2

System Boundary Document

What the ML system owns vs. consumes vs. produces.

Capacity Planning & Scaling Strategy

Project resource requirements and design autoscaling policies.

47.1

Capacity Planning Model

Resource projections for 6/12/24 months with scaling triggers.

47.2

Autoscaling Policy Document

Scaling rules, cooldown periods, and cost guardrails for each component.

Network & Data Flow Security

Design network segmentation, data flow controls, and zero-trust architecture.

48.1

Network Security Design

VPC layout, security groups, network policies, and data flow diagrams.

48.2

Zero-Trust Architecture Spec

Identity-based access, mutual TLS, and least-privilege enforcement.

Phase 2 Architecture Review

Formal architecture review with cross-functional stakeholders.

49.1

Architecture Review Board Minutes

Findings, concerns, required changes, and conditional approvals.

49.2

Technical Debt Register

Known compromises with remediation plans and deadlines.

Phase 2 Gate Review & Exit Certification

Formal gate review: architecture validated, baseline established, infrastructure proven.

50.1

Phase 2 Gate Review Package

Complete architecture deliverable inventory with review status.

50.2

Phase 2 Exit Certificate

Formal certification that architecture is validated and ready for engineering.

50.3

Go/No-Go Decision Record

Decision with rationale, conditions, and risk acceptance documentation.

Engineering Phase 3 — Build & Harden

Build with guardrails. Validation, red-teaming, risk controls, pre-production hardening, and hypercare.

Engineering is where design meets reality. This phase transforms architectural plans into hardened, validated systems that can withstand adversarial inputs, distribution shifts, and the entropy of production. The goal is not perfection—it is managed imperfection with explicit bounds, fast detection, and safe degradation.

View All 25 Steps & Validation Documents +

Evaluation Suite Design & Implementation

Build evaluation infrastructure that tells you whether the system works—not just on benchmarks.

51.1

Evaluation Suite Specification

Task-specific metrics, slice-based analysis, regression test sets with CI/CD integration.

51.2

Golden Dataset

Curated, versioned evaluation dataset with known-good labels and edge cases.

Red Team Protocol & Adversarial Testing

Design and execute adversarial testing—jailbreaks, prompt injection, data poisoning, evasion.

52.1

Red Team Protocol

Attack surface inventory, adversarial playbook, and engagement rules.

52.2

Red Team Results Report

Findings with severity ratings, reproduction steps, and mitigation evidence.

Bias & Fairness Audit

Demographic parity analysis, disparate impact testing, and remediation with human sign-off.

53.1

Bias & Fairness Audit Report

Demographic parity, equalized odds, and disparate impact analysis per protected class.

53.2

Remediation Plan

Specific actions to address identified biases with timeline and verification.

Prompt Engineering & Guard Rails (LLM)

For LLM-based systems: design system prompts, output guardrails, and content filtering.

54.1

Prompt Engineering Guide

System prompts, few-shot examples, chain-of-thought templates with versioning.

54.2

Output Guardrail Specification

Toxicity filters, PII scrubbing, hallucination detection, citation verification.

Model Optimization & Compression

Quantization, pruning, distillation, and ONNX conversion for production-grade performance.

55.1

Optimization Report

Techniques applied, accuracy/latency tradeoffs, and final model specifications.

55.2

Model Card

Standardized documentation—capabilities, limitations, intended use, ethical considerations.

Drift Detection & Alerting Pipeline

Implement statistical tests for data drift, concept drift, and prediction drift.

56.1

Drift Detection Specification

Statistical methods, monitoring frequency, threshold calibration, and alert routing.

56.2

Drift Response Playbook

Actions when drift is detected—investigation, retraining triggers, rollback criteria.

Circuit Breaker & Fallback Configuration

Automated fallback to simpler models or cached responses when primary system degrades.

57.1

Circuit Breaker Design

Trigger conditions, fallback hierarchy, and recovery procedures.

57.2

Graceful Degradation Matrix

What happens when each component fails—user experience and data integrity guarantees.

Cost Kill Switch & Rate Limiting

Automated spend caps, per-user rate limits, and cost anomaly detection.

58.1

Cost Control Specification

Spend caps, rate limits, anomaly detection rules, and auto-throttling configuration.

58.2

Cost Anomaly Response Playbook

Investigation steps, communication, and service restoration procedures.

Load Testing & Performance Benchmarks

Test throughput, latency, and degradation behavior under sustained and burst load.

59.1

Load Test Results

Throughput curves, latency distributions (p50/p95/p99), and breaking points.

59.2

Performance Benchmark Report

Comparison against requirements with gap analysis and optimization plan.

Canary & Shadow Deployment Configuration

Progressive rollout strategy with traffic splitting, rollback triggers, and comparison dashboards.

60.1

Deployment Strategy Document

Canary percentage ramps, shadow mode configuration, and success criteria.

60.2

Rollback Trigger Specification

Automated and manual rollback conditions with restoration time targets.

A/B Testing Framework

Design experiment infrastructure for controlled comparison of model versions.

61.1

A/B Test Framework Design

Randomization, sample sizing, metric collection, and statistical analysis pipeline.

61.2

Experiment Governance Policy

Approval process, ethical review, user consent, and result publication rules.

Data Validation Pipeline

Implement automated data validation at ingestion with schema enforcement.

62.1

Data Validation Rules

Schema checks, range validation, distribution tests, and freshness requirements.

62.2

Data Quarantine Procedures

What happens when invalid data is detected—isolation, alerting, remediation.

Model Explainability & Interpretability

Implement explanation methods appropriate to the model type and use case.

63.1

Explainability Specification

Methods (SHAP, LIME, attention, counterfactuals) selected per use case.

63.2

Explanation Validation Report

Human evaluation of explanation quality and faithfulness.

Eval & Governance Framework

Build the governance layer—model review boards, approval workflows, audit trails.

64.1

Model Governance Framework

Review board composition, approval workflows, and veto procedures.

64.2

Audit Trail Specification

What gets logged, retention policy, and tamper-evidence guarantees.

Failure Mode & Effects Analysis (FMEA)

Systematic identification of failure modes across the entire system.

65.1

FMEA Register

Every failure mode with severity, occurrence probability, detection capability, and RPN.

65.2

Critical Failure Mitigation Plan

Specific mitigations for high-RPN failure modes with verification evidence.

Promotion Gate Design

Define the gates between environments—what must pass before a model moves to production.

66.1

Promotion Gate Specification

Required checks per gate: accuracy, latency, cost, bias, security, and approval.

66.2

Gate Automation Configuration

CI/CD pipeline implementing promotion gates with automated and manual checks.

Security Hardening & Penetration Testing

Harden the system against security threats—pen testing, dependency scanning, vulnerability remediation.

67.1

Penetration Test Report

Findings with severity, reproduction, and remediation evidence.

67.2

Security Hardening Checklist

Verified hardening measures across all system components.

Compliance Validation & Audit Readiness

Verify all regulatory requirements are met with evidence packages for auditors.

68.1

Compliance Evidence Package

Mapped controls to regulatory requirements with proof artifacts.

68.2

Audit Readiness Assessment

Gap analysis against audit standards with remediation timeline.

Human-in-the-Loop Design

Design where human judgment is required—escalation, override, and approval workflows.

69.1

HITL Workflow Design

Escalation triggers, queue management, SLA for human review, and feedback routing.

69.2

Override & Appeal Process

How end-users or operators can challenge system decisions.

Incident Response Protocol

Design incident classification, on-call rotation, communication templates, and postmortem process.

70.1

Incident Response Plan

Severity classification, response procedures per level, and communication templates.

70.2

On-Call Rotation Schedule

Primary/secondary rotation with escalation and handoff procedures.

Hypercare Runbook

The first 30 days of production require dedicated engineering attention.

71.1

Hypercare Runbook

Day-by-day procedures, escalation triggers, and rollback playbooks.

71.2

Known Issues Register

Pre-identified issues with workarounds and resolution timelines.

Early Signal Dashboard

Real-time monitoring of key metrics during hypercare.

72.1

Hypercare Dashboard Specification

Metrics, refresh rates, alert thresholds, and persona-specific views.

72.2

Signal-to-Action Mapping

What each signal means and the specific action to take when triggered.

End-to-End Integration Testing

Test the complete system end-to-end with realistic data, load, and failure scenarios.

73.1

Integration Test Plan

Test scenarios covering happy paths, error paths, and failure injection.

73.2

Integration Test Results

Pass/fail per scenario with root cause for failures and remediation.

Pre-Production Readiness Checklist

Final verification that every system component meets production standards.

74.1

Production Readiness Checklist

Verified items across security, performance, monitoring, documentation, and ownership.

74.2

Outstanding Risk Acceptance

Risks accepted by named owners with review dates and mitigation plans.

Phase 3 Gate Review & Exit Certification

Formal gate review: system hardened, validated, and ready for production traffic.

75.1

Phase 3 Gate Review Package

Complete engineering deliverable inventory with validation evidence.

75.2

Phase 3 Exit Certificate

Formal certification that the system is production-ready.

75.3

Go/No-Go Decision Record

Final production decision with conditions, risk acceptance, and dissent.

Enablement Phase 4 — Operate & Sustain

Make the system survivable after handoff. Production operations, monitoring, runbooks, change management, and ROI validation.

Enablement is what separates a demo from an institution. Most AI systems die not from technical failure but from organizational neglect. This phase builds the operational, organizational, and economic scaffolding that keeps the system alive, trusted, and improving after the founding engineers are gone.

View All 25 Steps & Validation Documents +

Production Monitoring Dashboard

Real-time visibility into latency, error rates, throughput, model performance, and cost.

76.1

Production Dashboard Specification

Metrics, layout, refresh cadence, alert integration, and persona-specific views.

76.2

Alert Routing Configuration

Who gets paged, when, via what channel, with escalation rules.

Operational Runbook Library

Step-by-step procedures for common incidents, maintenance tasks, and recovery scenarios.

77.1

Operational Runbook Library

Indexed runbooks for every known failure mode and maintenance procedure.

77.2

Runbook Verification Log

Evidence that each runbook has been tested and verified by operations.

SLA & SLO Definitions

Service level objectives with error budgets, measurement methodology, and consequence policies.

78.1

SLA/SLO Document

Targets, measurement, error budgets, and consequences for breach.

78.2

Error Budget Policy

How error budget is tracked, reported, and what happens when exhausted.

Observability Model Implementation

Structured observability: logs, metrics, traces unified with correlation IDs and context.

79.1

Observability Architecture

Logging standards, metric collection, distributed tracing, and correlation strategy.

79.2

Observability Maturity Assessment

Current state vs. target with improvement roadmap.

Retraining Pipeline & Cadence

Automated retraining triggers with validation gates.

80.1

Retraining Pipeline Specification

Trigger conditions, data selection, training configuration, and validation gates.

80.2

Retraining Cadence Policy

Scheduled vs. triggered retraining with resource allocation and approval workflow.

Model Performance Decay Monitoring

Continuous monitoring of model performance with automated degradation detection.

81.1

Performance Decay Detection Specification

Metrics, baselines, decay thresholds, and alert configuration.

81.2

Performance Recovery Playbook

Investigation, root cause analysis, and remediation procedures.

Chaos Engineering & Resilience Testing

Scheduled failure injection to validate graceful degradation and recovery.

82.1

Chaos Engineering Plan

Failure scenarios, injection methods, blast radius, and success criteria.

82.2

Resilience Test Results

Findings from each chaos experiment with recovery times and improvement actions.

User Feedback Loop Integration

Structured collection of end-user feedback routed into model improvement.

83.1

Feedback Collection Design

Channels, formats, routing rules, and response SLAs.

83.2

Feedback-to-Improvement Pipeline

How feedback becomes labels, retraining data, or product changes.

Training & Enablement Materials

Role-specific training for operators, end-users, and leadership.

84.1

Training Curriculum

Role-specific modules with learning objectives, materials, and assessments.

84.2

Quick Start Guide

30-minute onboarding for new users with key workflows and troubleshooting.

Adoption Metrics & Health Dashboard

Track usage, satisfaction, feature adoption funnels, and time-to-value.

85.1

Adoption Dashboard Specification

Metrics: DAU/MAU, feature adoption, satisfaction scores, and churn indicators.

85.2

Adoption Target & Milestone Plan

Adoption targets by persona with timeline and intervention triggers.

Champion Network & Internal Advocacy

Build a network of internal advocates who drive adoption within their teams.

86.1

Champion Program Design

Selection criteria, responsibilities, recognition, and communication cadence.

86.2

Champion Playbook

Talking points, demo scripts, FAQ responses, and escalation procedures.

Governance Council & Decision Framework

Cross-functional governance for ongoing decisions about the system.

87.1

Governance Council Charter

Composition, meeting cadence, decision authority, and escalation to executive sponsor.

87.2

Decision Framework

How model changes, policy updates, and resource allocation are decided.

Developer Enablement & Self-Service

Build self-service tools, documentation, and APIs for other teams to consume the ML system.

88.1

Developer Documentation

API docs, SDK guides, code samples, and integration patterns.

88.2

Self-Service Portal Specification

Dashboard for developers to register, test, and monitor their integrations.

Cost Governance & Optimization

Ongoing cost monitoring, optimization recommendations, and budget adherence reporting.

89.1

Cost Governance Dashboard

Real-time cost tracking with trend analysis and anomaly detection.

89.2

Cost Optimization Playbook

Recurring review process with optimization techniques and ROI tracking.

Model Deprecation & Sunset Policy

Define how models are retired, users notified, and functionality preserved or migrated.

90.1

Deprecation Policy

Notice periods, migration paths, backward compatibility windows, and data retention.

90.2

Sunset Checklist

Verification steps for clean model retirement with no orphaned dependencies.

Knowledge Transfer & Documentation Audit

Ensure all system knowledge is documented and transferable.

91.1

Knowledge Transfer Plan

Sessions, recordings, documentation, and verification tests for each knowledge area.

91.2

Documentation Completeness Audit

Inventory of all required documentation with quality scores and gap remediation.

Postmortem & Continuous Learning Process

Establish blameless postmortem culture and systematic learning from incidents.

92.1

Postmortem Template & Process

Standard template, timeline expectations, follow-up tracking, and publication policy.

92.2

Lessons Learned Repository

Indexed, searchable archive of incidents and their insights.

Regulatory Reporting & Compliance Maintenance

Ongoing compliance monitoring, reporting automation, and regulatory change tracking.

93.1

Regulatory Reporting Schedule

Required reports, deadlines, responsible parties, and automation status.

93.2

Regulatory Change Monitoring Plan

How regulatory changes are detected, assessed, and implemented.

Vendor & Dependency Management

Track external dependencies, vendor health, and migration plans.

94.1

Vendor Risk Assessment

Critical vendors with concentration risk, alternatives, and migration playbooks.

94.2

Dependency Update Policy

Cadence for dependency updates, security patch SLAs, and testing requirements.

Scaling & Capacity Review

Periodic review of capacity utilization, scaling effectiveness, and resource right-sizing.

95.1

Capacity Review Report

Utilization trends, scaling events, right-sizing recommendations, and cost impact.

95.2

Growth Projection Update

Revised demand forecasts and infrastructure investment requirements.

ROI Analysis & Business Impact Report

Quantify value delivery against the original business case.

96.1

ROI Analysis Report

Value delivered vs. projected with methodology, confidence intervals, and attribution.

96.2

Business Impact Dashboard

Ongoing tracking of business metrics attributed to the ML system.

Total Cost of Ownership Model

Project ongoing compute, maintenance, retraining, and human oversight costs forward 12-24 months.

97.1

TCO Model

All-in cost projection including compute, people, maintenance, compliance, and opportunity cost.

97.2

Investment Decision Memo

Recommendation for continued, expanded, or reduced investment with evidence.

System Health Scorecard

Composite health score across reliability, performance, cost, adoption, and compliance.

98.1

System Health Scorecard

Weighted composite score with drill-down per dimension and trend analysis.

98.2

Health Score Action Triggers

Automated and manual actions triggered by score changes.

Certification & Handoff Documentation

Complete system documentation package suitable for audit, handoff, or regulatory review.

99.1

System Documentation Package

Complete technical, operational, and governance documentation in auditable format.

99.2

Handoff Acceptance Checklist

Receiving team verifies they can operate, troubleshoot, and improve the system.

100

ATVC Certification & Final Gate Review

Final certification that the system is ontologically grounded, architecturally sound, rigorously engineered, and operationally durable.

100.1

ATVC Certification Report

Summary of all phase gate reviews, outstanding risks, and certification decision.

100.2

Final Gate Review Package

Complete deliverable inventory across all 100 steps with sign-off status.

100.3

ATVC Certificate

Formal Agentic Trust Validation Certification with conditions and review date.

Access Required

AI/ML Ops RoadmapEnterprise Agentic System Validation

Ontology

Architecture

Engineering

Enablement

Ontology Phase 1 — Foundation

Executive Sponsorship & Charter

Charter Document

Kill Criteria Memo

Stakeholder Identification & Mapping

Stakeholder Register

RACI Matrix v0

Domain Expert Access & Interview Protocol

Expert Stakeholder Map

Interview Schedule & Protocol

Knowledge Source Priority Matrix

Concept Harvesting & Terminology Extraction

Terminology Extraction Report

Concept Laddering Results

Cross-Source Consistency Analysis

Relationship Mapping & Hierarchy Construction

Taxonomic Hierarchy Model

Part-Whole Relationship Map

Associative Relationship Network

Formal Ontology Representation

Concept Glossary & Definition Framework

Relationship Diagram Library

Decision Rationale Documentation

Problem Space Definition & Scoping

Boundary Definition & Scope Constraints

Problem Statement Document

Multi-Perspective Validation

Stakeholder Validation Sign-off Matrix

Assumption Register

Stress Testing & Edge Case Exploration

Edge Case Catalog

Assumption Stress Test Results

ML Problem Statement Translation

ML Problem Formulation

Success Criteria Specification

Data Availability & Quality Assessment

Data Inventory Report

Data Quality Assessment

Gap Analysis & Collection Plan

Regulatory & Ethical Constraint Mapping

Regulatory Constraint Map

Ethical Boundary Framework

Governance Obligation Matrix

Feasibility Analysis & Technical Risk Assessment

Technical Feasibility Report

Risk Register v1

Build vs. Buy vs. Partner Analysis

Build/Buy/Partner Decision Matrix

Vendor Landscape Assessment

Data Governance & Lineage Framework

Data Governance Policy

Data Lineage Framework

Labeling Strategy & Annotation Guidelines

Annotation Guidelines Document

Labeling Workflow Design

Bias & Representation Audit (Data)

Data Bias Audit Report

Representation Gap Plan

Privacy Impact Assessment

Privacy Impact Assessment (PIA)

Data Minimization Plan

Success Metric Hierarchy

Metric Hierarchy Document

Measurement Methodology

Organizational Readiness Assessment

Readiness Assessment Report

Training Needs Analysis

Economic Model & Unit Economics

Unit Economics Model

Budget Allocation Plan

Competitive & Market Intelligence

Competitive Intelligence Brief

Market Readiness Assessment

Communication & Change Management Plan

AI/ML Ops Roadmap
Enterprise Agentic System Validation