vertex-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