AI Agent Developer

Grumpy (Google ADK & agents-cli)

An experimental sandbox built to test Google's agents-cli harness and Google ADK capabilities, featuring strict persona enforcement and automated evaluation test sets.

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  • Google ADK
  • agents-cli
  • Gemini Flash
  • Python
  • LLM-as-a-Judge

Problem & Context

When exploring new agent harnesses, reading documentation only goes so far. Grumpy was created as a fun, zero-stakes weekend playground specifically designed to stress-test Google's agents-cli and the Google Agent Development Kit (ADK).

The goal wasn't to build a production SaaS or a serious enterprise utility, but to dive under the hood of agent tooling: learning how system instructions are compiled, how multi-turn context is managed in the CLI, how tool bindings function, and how to build a quick LLM-as-a-Judge evaluation loop to measure prompt adherence.

Architecture & How It Works

Grumpy is a lightweight, command-line-driven experiment powered by Gemini Flash via Google's developer toolchain.


              [ CLI Input / Test Code ] ──> ( agents-cli & Google ADK ) ──> ( Gemini Flash ) ──> [ Grumpy Output / Eval Suite ]
            

Playground Harness

Built to explore agents-cli execution workflows, configuration options, and local debugging patterns.

Persona Sandbox

Utilized a comedic, hyper-critical "grumpy reviewer" persona as a stress test to see how aggressively an LLM can be constrained to avoid sycophancy, pleasantries, and code generation.

Eval Harness

A simple Python evaluation script that runs batch prompts against the agent to verify whether prompt instructions hold up across iterative turns.

Key Features

  • CLI Harness Exploration

    Hands-on testing of Google ADK structure and local execution via agents-cli.

  • Persona Stress-Testing

    Deliberate experimentation with hard negative constraints (e.g., banning apologies, praise, and auto-generated fixes).

  • Automated Test Scripter

    Quick evaluation suite checking output consistency against specific behavioral criteria.

Results & Metrics

Tool Mastery

Successfully mapped the entire lifecycle of building, configuring, and executing custom agents using Google ADK and agents-cli.

Eval Validation

Achieved 100% adherence on experimental test prompts within the local sandbox environment.

Tech Stack Deep Dive

Google ADK
Framework used for defining agent structure and tool boundaries.
agents-cli
Command-line harness used for local agent execution and orchestration.
Gemini Flash
Fast, lightweight LLM engine used for rapid prototyping and response generation.
Python
Language used for scripting the test harness and evaluation loops.

Links & Resources

Lessons Learned

  • 01

    CLI Harnesses Accelerate Prototyping

    Exploring raw developer tools like agents-cli provides much deeper insight into agent runtime behavior than abstract high-level frameworks.

  • 02

    Fun Projects Drive Deep Learning

    Building a humorous, low-pressure sandbox is often the fastest way to master a new SDK because it removes the friction of worrying about production constraints.

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