ADK Engineer

ADK Engineer

Engineer production-ready Agent Development Kit (ADK) agents and multi-agent systems: clean structure, testability, safe tool usage, and deployment automation.

Overview

Use this skill to design and implement ADK agent code that is maintainable and shippable: clear module boundaries, structured tool interfaces, regression tests, and a deployment checklist (local or Agent Engine).

Prerequisites

• A target runtime (Python/​Java/​Go) consistent with the project’s pinned versions
• ADK installed (and any required model/​provider SDKs configured)
• A test runner available in the repo (unit tests at minimum)
• If deploying: access to a Google Cloud project and permissions for the chosen deployment target

Instructions

1. Clarify requirements: agent goals, tool surface, latency/​cost constraints, and deployment target.
2. Propose architecture: single agent vs multi-agent, orchestration pattern, state strategy (Memory Bank / external store).
3. Scaffold structure: agent entrypoint(s), tool modules, config, and tests.
4. Implement incrementally:
   • add one tool at a time with input validation and structured outputs
   • add regression tests for each tool and critical prompt flows
5. Add operational guardrails: retries/​backoff, timeouts, logging, and safe error messages.
6. Validate locally (tests + smoke prompts) and provide a deployment plan (when requested).

Output

• A concrete architecture plan and file layout
• Agent and tool implementations (or patches) with tests
• A validation checklist (commands to run, expected outputs, and failure triage)
• Optional: deployment instructions and post-deploy health checks

Error Handling

• Build/​test failures: isolate the failing module, minimize the repro, fix, and add a regression test.
• Tool/​runtime errors: enforce structured error responses and safe retries where appropriate.
• Deployment failures: provide the exact failing command, logs to inspect, and least-privilege IAM fixes.

Examples

Example: Productionizing an existing ADK agent

• Request: “Refactor this agent into a clean module structure and add tests before we deploy.”
• Result: reorganized src/ layout, tool boundaries, a test suite, and a deployment checklist.

Example: Multi-agent workflow

• Request: “Build a validator + deployer + monitor agent team with a sequential orchestrator.”
• Result: orchestrator skeleton, per-agent responsibilities, and smoke tests for each step.

Resources

• Full detailed playbook (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
• ADK / Agent Engine docs: https://cloud.google.com/vertex-ai/docs/agent-engine