界面已本地化;技能介绍与说明文档按所选语言由 AI 自动翻译,代码、命令、路径和专业术语保留原文,以原作者文档为准。
探索技能
部署与运维 · SKILL.md

adk-deployment-specialist

adk-deployment-specialist

【部署与运维】Deploy and orchestrate Vertex AI ADK agents using A2A protocol. Manages AgentCard discovery, task submission, Code Execution Sandbox, and Memory Bank. Use when asked to "deploy ADK agent" or "orchestrate agents". Trigger with phrases like 'deploy', 'infrastructure', or 'CI/CD'.

独立 GitHub 快照 · 自动分类 · 以原作者文档为准

技能说明

GitHub

name: adk-deployment-specialist description: | Deploy and orchestrate Vertex AI ADK agents using A2A protocol. Manages AgentCard discovery, task submission, Code Execution Sandbox, and Memory Bank. Use when asked to "deploy ADK agent" or "orchestrate agents". Trigger with phrases like 'deploy', 'infrastructure', or 'CI/CD'. allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*) version: 1.0.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT

Adk Deployment Specialist

Overview

Expert in building and deploying production multi-agent systems using Google's Agent Development Kit (ADK). Handles agent orchestration (Sequential, Parallel, Loop), A2A protocol communication, Code Execution Sandbox for GCP operations, Memory Bank for stateful conversations, and deployment to Vertex AI Agent Engine.

Prerequisites

  • A Google Cloud project with Vertex AI enabled (and permissions to deploy Agent Engine runtimes)
  • ADK installed (and pinned to the project’s supported version)
  • A clear agent contract: tools required, orchestration pattern, and deployment target (local vs Agent Engine)
  • A plan for secrets/credentials (OIDC/WIF where possible; never commit long-lived keys)

Instructions

  1. Confirm the desired architecture (single agent vs multi-agent) and orchestration pattern (Sequential/Parallel/Loop).
  2. Define the AgentCard + A2A interfaces (inputs/outputs, task submission, and status polling expectations).
  3. Implement the agent(s) with the minimum required tool surface (Code Execution Sandbox and/or Memory Bank as needed).
  4. Test locally with representative prompts and failure cases, then add smoke tests for deployment verification.
  5. Deploy to Vertex AI Agent Engine and validate the generated endpoints (/.well-known/agent-card, task send/status APIs).
  6. Add observability: logs, dashboards, and retry/backoff behavior for transient failures.

Output

  • Agent source files (or patches) ready for deployment
  • Deployment commands/config (e.g., adk deploy invocation + required flags)
  • A verification checklist for Agent Engine endpoints (AgentCard + task APIs) and security posture

Error Handling

See {baseDir}/references/errors.md for comprehensive error handling.

Examples

See {baseDir}/references/examples.md for detailed examples.

Resources

我的收藏