vertex-agent-builder
Build and deploy production-ready generative AI agents using Vertex AI, Gemini models, and Google Cloud infrastructure with RAG, function calling, and multi-modal capabilities
Security Assessment
Detected risks:
About vertex-agent-builder
The vertex-agent-builder skill is designed to help teams build and deploy production-ready generative AI agents on Google Cloud using Vertex AI and Gemini models. It provides a structured approach for designing agents that incorporate retrieval-augmented generation (RAG), function calling, and operational guardrails such as validation, monitoring, and cost controls. The skill focuses on aligning implementations with Vertex AI Agent Engine deployment patterns, ensuring that agents are not only functional but also maintainable and production-safe. It addresses common challenges such as model selection, regional deployment constraints, retrieval integration, and deployment verification.
Key capabilities include generating a scaffold for a Vertex AI agent with clear extension points, guiding model and region selection, and defining structured tool or function interfaces with schemas and error contracts. When retrieval is required, the skill outlines chunking strategies, embedding and indexing considerations, and a citation-first response format. It also embeds evaluation practices, including golden prompts, offline checks, and minimal online smoke tests to prevent regressions. Operational guidance covers logging, metrics, alerting, quota and cost guardrails, rollback procedures, and structured error handling for quota, authentication, retrieval, and tool failures.
This skill is intended for developers, platform engineers, and AI teams working within Google Cloud who need a repeatable and reliable way to build advanced agents using Vertex AI. Typical use cases include support bots that answer from documentation with citations, multimodal intake agents that extract structured data from PDFs or images, and task-routing agents that rely on defined schemas and validation logic. It is particularly useful in production environments where deployment rigor, evaluation discipline, and operational resilience are critical.
FAQ
What do I need before using vertex-agent-builder?
You need a Google Cloud project with the Vertex AI API enabled and sufficient permissions to deploy or operate Agent Engine runtimes. If you are using retrieval (RAG), you also need a document source such as GCS, BigQuery, or Firestore, along with an embeddings and indexing strategy. Secrets must be handled via environment variables or Secret Manager.
Does this skill support retrieval-augmented generation (RAG)?
Yes. It provides guidance for implementing retrieval, including chunking, embeddings, indexing, and a citation-first response format. It also includes validation steps to check embedding dimensions and fallback behavior in case of retrieval failures.
Can it help with deployment to Vertex AI Agent Engine?
Yes. The skill produces a scaffold aligned with Vertex AI Agent Engine deployment patterns and can provide deployment commands or configuration along with endpoint and permission verification steps.
How does it handle errors and operational issues?
It includes structured error handling guidance for quota and region issues, authentication failures, retrieval errors, and tool or function errors. It recommends least-privilege remediation, structured error responses, regression tests, logging, metrics, alerting, and rollback steps.
Is it limited to text-only agents?
No. The skill supports multimodal agent scenarios, such as extracting structured fields from PDFs or images, by defining schema-first prompts and tool interface contracts.
Install vertex-agent-builder
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
jeremylongshore/claude-code-plugins-plus