vertex-agent-builder

---
name: vertex-agent-builder
description: |
  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. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
allowed-tools: Read, Write, Edit, Grep, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT
---
# Vertex AI Agent Builder

Build and deploy production-ready agents on Vertex AI with Gemini models, retrieval (RAG), function calling, and operational guardrails (validation, monitoring, cost controls).

## Overview

- Produces an agent scaffold aligned with Vertex AI Agent Engine deployment patterns.
- Helps choose models/regions, design tool/function interfaces, and wire up retrieval.
- Includes an evaluation + smoke-test checklist so deployments don’t regress.

## Prerequisites

- Google Cloud project with Vertex AI API enabled
- Permissions to deploy/operate Agent Engine runtimes (or a local-only build target)
- If using RAG: a document source (GCS/BigQuery/Firestore/etc) and an embeddings/index strategy
- Secrets handled via env vars or Secret Manager (never committed)

## Instructions

1. Clarify the agent’s job (user intents, inputs/outputs, latency and cost constraints).
2. Choose model + region and define tool/function interfaces (schemas, error contracts).
3. Implement retrieval (if needed): chunking, embeddings, index, and a “citation-first” response format.
4. Add evaluation: golden prompts, offline checks, and a minimal online smoke test.
5. Deploy (optional): provide the exact deployment command/config and verify endpoints + permissions.
6. Add ops: logs/metrics, alerting, quota/cost guardrails, and rollback steps.

## Output

- A Vertex AI agent scaffold (code/config) with clear extension points
- A retrieval plan (when applicable) and a validation/evaluation checklist
- Optional: deployment commands and post-deploy health checks

## Error Handling

- Quota/region issues: detect the failing service/quota and propose a scoped fix.
- Auth failures: identify the principal and missing role; prefer least-privilege remediation.
- Retrieval failures: validate indexing/embedding dimensions and add fallback behavior.
- Tool/function errors: enforce structured error responses and add regression tests.

## Examples

**Example: RAG support agent**
- Request: “Deploy a support bot that answers from our docs with citations.”
- Result: ingestion plan, retrieval wiring, evaluation prompts, and a smoke test that verifies citations.

**Example: Multimodal intake agent**
- Request: “Build an agent that extracts structured fields from PDFs/images and routes tasks.”
- Result: schema-first extraction prompts, tool interface contracts, and validation examples.

## Resources

- Full detailed guide (kept for reference): `{baseDir}/references/SKILL.full.md`
- Repo standards (source of truth):
  - `000-docs/6767-a-SPEC-DR-STND-claude-code-plugins-standard.md`
  - `000-docs/6767-b-SPEC-DR-STND-claude-skills-standard.md`
- Vertex AI docs: https://cloud.google.com/vertex-ai/docs
- Agent Engine docs: https://cloud.google.com/vertex-ai/docs/agent-engine