Vertex AI Agent Builder

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