Execute inspect and validate Vertex AI Agent Engine deployments including Code Execution Sandbox, Memory Bank, A2A protocol compliance, and security posture. Generates production readiness scores. Use when asked to "inspect agent engine" or "validate depl... Trigger with relevant phrases based on skill purpose.
Inspect and validate Vertex AI Agent Engine deployments across seven categories: runtime configuration, Code Execution Sandbox, Memory Bank, A2A protocol compliance, security posture, performance metrics, and monitoring observability. This skill generates weighted production-readiness scores (0-100%) with actionable recommendations for each deployment.
gcloud CLI authenticated (for IAM and monitoring queries — not for Agent Engine CRUD)
IAM roles: roles/aiplatform.user and roles/monitoring.viewer granted on the target project
Access to the target Google Cloud project hosting the Agent Engine deployment
curl for A2A protocol endpoint testing (AgentCard, Task API, Status API)
Cloud Monitoring API enabled for performance metrics retrieval
Familiarity with Vertex AI Agent Engine concepts: Code Execution Sandbox, Memory Bank, Model Armor
Important: There is no gcloud CLI surface for Agent Engine (no , , or commands exist). All Agent Engine operations use the Python SDK via or .
gcloud ai agents
gcloud ai reasoning-engines
gcloud alpha ai agent-engines
vertexai.Client()
vertexai.preview.reasoning_engines
Instructions
Connect to the Agent Engine deployment by retrieving agent metadata via the Python SDK (client.agent_engines.get(name=...))
Parse the runtime configuration: model selection (Gemini 2.5 Pro/Flash), tools enabled, VPC settings, and scaling policies
Validate Code Execution Sandbox settings: confirm state TTL is 7-14 days, sandbox type is SECURE_ISOLATED, and IAM permissions are scoped to required GCP services only
Check Memory Bank configuration: verify enabled status, retention policy (min 100 memories), Firestore encryption, indexing enabled, and auto-cleanup active
Test A2A protocol compliance by probing /.well-known/agent-card, POST /v1/tasks:send, and GET /v1/tasks/<task-id> endpoints for correct responses
Audit security posture: validate IAM least-privilege roles, VPC Service Controls perimeter, Model Armor activation, encryption at rest and in transit, and absence of hardcoded credentials
Query Cloud Monitoring for performance metrics: request count, error rate (target < 5%), latency percentiles (p50/p95/p99), token usage, and cost estimates over the last 24 hours
Assess monitoring and observability: confirm Cloud Monitoring dashboards, alerting policies, structured logging, OpenTelemetry tracing, and Cloud Error Reporting are configured
Calculate weighted scores across all categories and determine overall production readiness status
Generate a prioritized list of recommendations with estimated score improvement per remediation
See ${CLAUDE_SKILL_DIR}/references/inspection-workflow.md for the phased inspection process and ${CLAUDE_SKILL_DIR}/references/inspection-categories.md for detailed check criteria.
Output
Inspection report in YAML format with per-category scores and overall readiness percentage
Prioritized recommendations with estimated score improvement per item
See ${CLAUDE_SKILL_DIR}/references/example-inspection-report.md for a complete sample report.
Error Handling
Error
Cause
Solution
Agent metadata not accessible
Insufficient IAM permissions or incorrect agent ID
Verify roles/aiplatform.user granted; confirm agent ID with client.agent_engines.list() via Python SDK
A2A AgentCard endpoint 404
Agent not configured for A2A protocol or endpoint path incorrect
Check agent configuration for A2A enablement; verify /.well-known/agent-card path
Cloud Monitoring metrics empty
Monitoring API not enabled or no recent traffic
Run gcloud services enable monitoring.googleapis.com; generate test traffic first
VPC-SC perimeter blocking access
Inspector running outside VPC Service Controls perimeter
Add inspector service account to access level; use VPC-SC bridge or access policy
Code Execution TTL out of range
State TTL set below 1 day or above 14 days
Adjust TTL to 7-14 days for production; values above 14 days are rejected by Agent Engine
See ${CLAUDE_SKILL_DIR}/references/errors.md for additional error scenarios.
Examples
Scenario 1: Pre-Production Readiness Check -- Inspect a newly deployed ADK agent before production launch. Run all 28 checklist items across security, performance, monitoring, compliance, and reliability. Target: overall score above 85% before approving production traffic.
Scenario 2: Security Audit After IAM Change -- Re-inspect security posture after modifying service account roles. Validate that least-privilege is maintained (target: IAM score 95%+), VPC-SC perimeter is intact, and Model Armor remains active.
Scenario 3: Performance Degradation Investigation -- Inspect an agent showing elevated error rates. Query 24-hour performance metrics, identify latency spikes at p95/p99, check auto-scaling behavior, and correlate with token usage patterns to isolate the root cause.
Resources
Vertex AI Agent Engine Documentation -- deployment and configuration
