Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/vertex-agent-builder/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill vertex-agent-builder. Copyright stays with the author (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

• Implementation patterns (model selection, RAG wiring, deployment config): ${CLAUDE_SKILL_DIR}/references/implementation.md
• Worked examples (RAG support agent, multimodal extraction): ${CLAUDE_SKILL_DIR}/references/examples.md
• Error-handling and recovery patterns: ${CLAUDE_SKILL_DIR}/references/errors.md
• Product / architecture context: ${CLAUDE_SKILL_DIR}/PRD.md, ${CLAUDE_SKILL_DIR}/ARD.md
• Vertex AI docs: https://cloud.google.com/vertex-ai/docs
• Agent Engine docs: