Validator Expert
Overview
Validate production readiness of Vertex AI Agent Engine deployments by executing weighted checks across five categories: security (30 points), monitoring (20 points), performance (25 points), compliance (15 points), and reliability (10 points). This skill produces a 0-100% composite score with pass/fail per check and prioritized remediation recommendations.
Prerequisites
gcloud CLI authenticated with roles/aiplatform.viewer, roles/iam.securityReviewer, and roles/monitoring.viewer
- Access to the target Google Cloud project and Vertex AI Agent Engine deployment
- Cloud Monitoring API and Cloud Logging API enabled in the project
- Knowledge of the deployment's expected SLOs (latency targets, error rate thresholds)
- Read-only access to IAM policies, VPC-SC configurations, and service account bindings
Instructions
- Retrieve the deployment configuration using
gcloud ai agents describe and parse model, scaling, and feature settings
- Run the security validation suite:
- Verify IAM roles follow least-privilege by auditing service account bindings against required permissions
- Confirm VPC Service Controls perimeter is active and correctly scoped
- Check encryption at rest (CMEK or Google-managed) and in-transit (TLS 1.3)
- Scan configuration files and environment variables for hardcoded secrets
- Validate service account key rotation policy (max 90 days)
- Confirm Model Armor is enabled for ADK-based agents
- Run the monitoring validation suite:
- Verify Cloud Monitoring dashboards exist with required panels (request count, error rate, latency)
- Confirm alerting policies cover error rate spikes, latency SLO breaches, and cost thresholds
- Check token usage tracking is enabled with per-model granularity
- Validate structured logging with severity levels and correlation IDs
- Confirm latency SLOs are defined with p95 and p99 targets
- Run the performance validation suite:
- Verify auto-scaling is configured with appropriate min/max instance counts
- Check resource limits (CPU, memory) match expected workload profile
- Confirm caching strategy is implemented for repeated prompts or embeddings
- Validate Code Execution Sandbox TTL is set between 7-14 days
- Check Memory Bank retention policy (min 100 memories, auto-cleanup enabled)
- Run the compliance validation suite:
- Confirm audit logging is enabled for all admin and data access operations
- Verify data residency meets regional requirements
- Check privacy policies and data retention schedules
- Validate backup and disaster recovery configuration
- Calculate weighted scores per category and compute the overall production readiness percentage
- Generate a prioritized recommendation list sorted by score impact per remediation effort
Output
- Production readiness score: 0-100% with status (READY >= 85%, NEEDS WORK 70-84%, NOT READY < 70%)
- Per-category breakdown: security (x/30), monitoring (x/20), performance (x/25), compliance (x/15), reliability (x/10)
- Pass/fail table for each individual check with evidence notes
- Prioritized remediation plan: action items ranked by score improvement per effort
- Comparison to previous validation run (if available) showing score delta
Error Handling
| Error |
Cause |
Solution |
| Insufficient IAM permissions |
Viewer roles not granted on target project |
Request roles/aiplatform.viewer and roles/iam.securityReviewer from project admin |
| Agent deployment not found |
Incorrect agent ID or deployment deleted |
Verify agent ID with gcloud ai agents list; confirm deployment region |
| Monitoring API returns no data |
API not enabled or agent has zero traffic |
Enable Monitoring API; generate synthetic traffic to populate baseline metrics |
| VPC-SC configuration inaccessible |
Organization policy restricts VPC-SC reads |
Request roles/accesscontextmanager.policyReader at organization level |
| Compliance check inconclusive |
Audit logs not enabled or retention too short |
Enable Data Access audit logs; set log retention to minimum 365 days |
Examples
Scenario 1: Pre-Launch Validation -- Validate a new ADK agent before production launch. Run all five validation categories. Target score: 85%+ overall, with security score at 28/30 minimum. Generate remediation plan for any failing checks.
Scenario 2: Post-Incident Security Audit -- After a permission escalation incident, re-validate security posture. Focus on IAM least-privilege, service account bindings, and VPC-SC perimeter integrity. Compare scores against the last passing validation.
Scenario 3: Quarterly Compliance Review -- Execute compliance and monitoring validation suites for SOC 2 audit preparation. Verify audit logging coverage, data residency compliance, and backup/DR configuration. Export results as evidence artifacts.
Resources
1---2name: validator-expert3description: Validate production readiness of Vertex AI Agent Engine deployments across security, monitoring, performance, compliance, and best practices. Generates weighted scores (0-100%) with actionable recommendations. Use when asked to "validate deploymen... Trigger with phrases like 'validate', 'check', or 'verify'.4license: MIT5---6# Validator Expert78## Overview910Validate production readiness of Vertex AI Agent Engine deployments by executing weighted checks across five categories: security (30 points), monitoring (20 points), performance (25 points), compliance (15 points), and reliability (10 points). This skill produces a 0-100% composite score with pass/fail per check and prioritized remediation recommendations.1112## Prerequisites1314- `gcloud` CLI authenticated with `roles/aiplatform.viewer`, `roles/iam.securityReviewer`, and `roles/monitoring.viewer`15- Access to the target Google Cloud project and Vertex AI Agent Engine deployment16- Cloud Monitoring API and Cloud Logging API enabled in the project17- Knowledge of the deployment's expected SLOs (latency targets, error rate thresholds)18- Read-only access to IAM policies, VPC-SC configurations, and service account bindings1920## Instructions21221. Retrieve the deployment configuration using `gcloud ai agents describe` and parse model, scaling, and feature settings232. Run the security validation suite:24 - Verify IAM roles follow least-privilege by auditing service account bindings against required permissions25 - Confirm VPC Service Controls perimeter is active and correctly scoped26 - Check encryption at rest (CMEK or Google-managed) and in-transit (TLS 1.3)27 - Scan configuration files and environment variables for hardcoded secrets28 - Validate service account key rotation policy (max 90 days)29 - Confirm Model Armor is enabled for ADK-based agents303. Run the monitoring validation suite:31 - Verify Cloud Monitoring dashboards exist with required panels (request count, error rate, latency)32 - Confirm alerting policies cover error rate spikes, latency SLO breaches, and cost thresholds33 - Check token usage tracking is enabled with per-model granularity34 - Validate structured logging with severity levels and correlation IDs35 - Confirm latency SLOs are defined with p95 and p99 targets364. Run the performance validation suite:37 - Verify auto-scaling is configured with appropriate min/max instance counts38 - Check resource limits (CPU, memory) match expected workload profile39 - Confirm caching strategy is implemented for repeated prompts or embeddings40 - Validate Code Execution Sandbox TTL is set between 7-14 days41 - Check Memory Bank retention policy (min 100 memories, auto-cleanup enabled)425. Run the compliance validation suite:43 - Confirm audit logging is enabled for all admin and data access operations44 - Verify data residency meets regional requirements45 - Check privacy policies and data retention schedules46 - Validate backup and disaster recovery configuration476. Calculate weighted scores per category and compute the overall production readiness percentage487. Generate a prioritized recommendation list sorted by score impact per remediation effort4950## Output5152- Production readiness score: 0-100% with status (READY >= 85%, NEEDS WORK 70-84%, NOT READY < 70%)53- Per-category breakdown: security (x/30), monitoring (x/20), performance (x/25), compliance (x/15), reliability (x/10)54- Pass/fail table for each individual check with evidence notes55- Prioritized remediation plan: action items ranked by score improvement per effort56- Comparison to previous validation run (if available) showing score delta5758## Error Handling5960| Error | Cause | Solution |61|-------|-------|----------|62| Insufficient IAM permissions | Viewer roles not granted on target project | Request `roles/aiplatform.viewer` and `roles/iam.securityReviewer` from project admin |63| Agent deployment not found | Incorrect agent ID or deployment deleted | Verify agent ID with `gcloud ai agents list`; confirm deployment region |64| Monitoring API returns no data | API not enabled or agent has zero traffic | Enable Monitoring API; generate synthetic traffic to populate baseline metrics |65| VPC-SC configuration inaccessible | Organization policy restricts VPC-SC reads | Request `roles/accesscontextmanager.policyReader` at organization level |66| Compliance check inconclusive | Audit logs not enabled or retention too short | Enable Data Access audit logs; set log retention to minimum 365 days |6768## Examples6970**Scenario 1: Pre-Launch Validation** -- Validate a new ADK agent before production launch. Run all five validation categories. Target score: 85%+ overall, with security score at 28/30 minimum. Generate remediation plan for any failing checks.7172**Scenario 2: Post-Incident Security Audit** -- After a permission escalation incident, re-validate security posture. Focus on IAM least-privilege, service account bindings, and VPC-SC perimeter integrity. Compare scores against the last passing validation.7374**Scenario 3: Quarterly Compliance Review** -- Execute compliance and monitoring validation suites for SOC 2 audit preparation. Verify audit logging coverage, data residency compliance, and backup/DR configuration. Export results as evidence artifacts.7576## Resources7778- [Vertex AI Security Best Practices](https://cloud.google.com/vertex-ai/docs/security) -- IAM, encryption, VPC-SC79- [Cloud Monitoring Alerting](https://cloud.google.com/monitoring/alerts) -- policy configuration80- [VPC Service Controls](https://cloud.google.com/vpc-service-controls/docs) -- perimeter setup81- [Model Armor Documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-armor) -- prompt injection protection82- [Cloud Audit Logs](https://cloud.google.com/logging/docs/audit) -- admin and data access logging