Vertex Engine Inspector
Overview
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.
Prerequisites
google-cloud-aiplatform[agent_engines]>=1.120.0 Python SDK installedgcloud 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 gcloud ai agents, gcloud ai reasoning-engines, or gcloud alpha ai agent-engines commands exist). All Agent Engine operations use the Python SDK via vertexai.Client() or 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
- Runtime configuration summary: model, tools, VPC, scaling settings
- A2A protocol compliance matrix: pass/fail for AgentCard, Task API, Status API
- Security posture score with breakdown: IAM, VPC-SC, Model Armor, encryption, secrets
- Performance metrics dashboard: error rate, latency percentiles, token usage, daily cost estimate
- 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
1---2name: vertex-engine-inspector3description: 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, validate, or audit an Agent Engine deployment. Trigger with "inspect agent engine", "validate agent engine deployment", "check agent engine config", "audit agent engine security", "agent engine readiness check", "vertex engine health", or "reasoning engine status".4license: MIT5---6# Vertex Engine Inspector
7
8## Overview
9
10Inspect 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.
11
12## Prerequisites
13
14- `google-cloud-aiplatform[agent_engines]>=1.120.0` Python SDK installed
15- `gcloud` CLI authenticated (for IAM and monitoring queries — **not** for Agent Engine CRUD)
16- IAM roles: `roles/aiplatform.user` and `roles/monitoring.viewer` granted on the target project
17- Access to the target Google Cloud project hosting the Agent Engine deployment
18- `curl` for A2A protocol endpoint testing (AgentCard, Task API, Status API)
19- Cloud Monitoring API enabled for performance metrics retrieval
20- Familiarity with Vertex AI Agent Engine concepts: Code Execution Sandbox, Memory Bank, Model Armor
21
22**Important**: There is no `gcloud` CLI surface for Agent Engine (no `gcloud ai agents`, `gcloud ai reasoning-engines`, or `gcloud alpha ai agent-engines` commands exist). All Agent Engine operations use the Python SDK via `vertexai.Client()` or `vertexai.preview.reasoning_engines`.
23
24## Instructions
25
261. Connect to the Agent Engine deployment by retrieving agent metadata via the Python SDK (`client.agent_engines.get(name=...)`)
272. Parse the runtime configuration: model selection (Gemini 2.5 Pro/Flash), tools enabled, VPC settings, and scaling policies
283. 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
294. Check Memory Bank configuration: verify enabled status, retention policy (min 100 memories), Firestore encryption, indexing enabled, and auto-cleanup active
305. Test A2A protocol compliance by probing `/.well-known/agent-card`, `POST /v1/tasks:send`, and `GET /v1/tasks/<task-id>` endpoints for correct responses
316. 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
327. 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
338. Assess monitoring and observability: confirm Cloud Monitoring dashboards, alerting policies, structured logging, OpenTelemetry tracing, and Cloud Error Reporting are configured
349. Calculate weighted scores across all categories and determine overall production readiness status
3510. Generate a prioritized list of recommendations with estimated score improvement per remediation
36
37See `${CLAUDE_SKILL_DIR}/references/inspection-workflow.md` for the phased inspection process and `${CLAUDE_SKILL_DIR}/references/inspection-categories.md` for detailed check criteria.
38
39## Output
40
41- Inspection report in YAML format with per-category scores and overall readiness percentage
42- Runtime configuration summary: model, tools, VPC, scaling settings
43- A2A protocol compliance matrix: pass/fail for AgentCard, Task API, Status API
44- Security posture score with breakdown: IAM, VPC-SC, Model Armor, encryption, secrets
45- Performance metrics dashboard: error rate, latency percentiles, token usage, daily cost estimate
46- Prioritized recommendations with estimated score improvement per item
47
48See `${CLAUDE_SKILL_DIR}/references/example-inspection-report.md` for a complete sample report.
49
50## Error Handling
51
52| Error | Cause | Solution |
53|-------|-------|----------|
54| 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 |
55| 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 |
56| Cloud Monitoring metrics empty | Monitoring API not enabled or no recent traffic | Run `gcloud services enable monitoring.googleapis.com`; generate test traffic first |
57| 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 |
58| 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 |
59
60See `${CLAUDE_SKILL_DIR}/references/errors.md` for additional error scenarios.
61
62## Examples
63
64**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.
65
66**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.
67
68**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.
69
70## Resources
71
72- [Vertex AI Agent Engine Documentation](https://cloud.google.com/vertex-ai/docs/agents) -- deployment and configuration
73- [A2A Protocol Specification](https://google.github.io/A2A/) -- AgentCard, Task API, protocol compliance
74- [Cloud Monitoring API](https://cloud.google.com/monitoring/api/v3) -- metrics queries and dashboard configuration
75- [VPC Service Controls](https://cloud.google.com/vpc-service-controls/docs) -- perimeter setup and access policies
76- [Model Armor](https://cloud.google.com/vertex-ai/docs/generative-ai/model-armor) -- prompt injection protection configuration
aibot88