Welcome to the guide How to Build Your Own AI Writing Agent Using Google Gemini Gems 🚀, where we will learn together how to create a digital assistant that does exactly what you tell it! If you’ve ever wanted a helper who remembers your writing style, makes zero grammar mistakes, and is always in the mood to work, you are in the right place. This text is accompanied by a detailed video tutorial that you can find embedded at the bottom of the page, so we can go through all the steps together. Get ready to boost your productivity and forget about typing the exact same prompts over and over again. Dive with me into the fascinating world of custom AI agents! 😊
What are Google Gemini Gems writing agents
Regular language models are like freelance workers to whom you have to explain how you like your text formatted every single morning. Google Gems, on the other hand, are your loyal apprentices who permanently remember your instructions, context, and specific tone of communication. This is a feature available within Gemini Advanced and Workspace accounts that allows you to create specialized experts for almost any topic. Whether you need help with proofreading, writing SEO blogs, or technical documentation, your personal Gem will do it quickly and efficiently. When I tested this myself, I made the mistake of giving the agent a prompt that was way too long, which led to context bloat and model confusion, so my advice is to always keep your instructions precise and concise.
Architecture and connection schema of the AI system
Even though we aren’t soldering wires on Arduino boards here, the connection schema of this AI system’s architecture is equally logical and important. The input module represents your prompt or text draft that you send directly through the Gemini user interface. This prompt then travels to the “brain”, which is the Gemini 3.1 Pro model that processes data using “Deep Think” algorithms for advanced and deep reasoning. The memory module is directly connected to your Google Drive account, from where the agent pulls knowledge files, like your previous texts (it’s possible to connect up to 10 files per agent). Finally, the output module applies strict formatting rules and returns a perfectly edited text to you, ready for publishing.
Writing system instructions for the AI agent
The secret of an excellent digital assistant lies in the system instructions you give it during creation in the Gem manager. The best practice in the industry is using the PTCF framework: Persona, Task, Context, and Format. First, you define its role, for example, to act as a senior technical writer with 15 years of experience in the IT sector. Then you clearly assign the task, explain the background knowledge of your target audience, and demand the exact visual layout of the final output. If you use clear subheadings in Markdown format when writing these rules, the model will follow them much better and hallucinate less.
Complete code for the Google ADK script
Besides the textual prompt itself, more advanced users can integrate these agents directly into their applications using the Agent Development Kit (ADK) in Python. First, we will define our system script (prompt) that you enter into the Gem settings on the interface, and then we will write the Python code for automation.
Persona
You are “Tech Scribe”, an expert IT copywriter and editor.
Task
Your task is to transform messy drafts into engaging tutorials.
Context
The audience consists of beginners and hobbyists. The tone must be encouraging, with mandatory use of analogies. Never use complicated academic jargon.
Format
Format the output using Markdown. First, display an tag with a short overview, and then <finished_text> with the revised content.
If you want to connect your agent with other tools via API, here is the complete Python code for initialization:
Python
# We import the LlmAgent class from the Google ADK library
from google.adk.agents import LlmAgent
# We initialize our AI writing agent
writing_agent = LlmAgent(
# We give an internal name to our agent
name=”tech_scribe_agent”,
# We choose the Gemini 3.1 Pro model for top-tier logical reasoning
model=”gemini-3.1-pro”,
# We insert system instructions that define the tone and task
instruction=”You are an expert IT copywriter. Your task is editing tech texts for beginners using a relaxed tone and analogies.”,
# We define the output key under which the result is saved
output_key=”revised_text”
)
# The input text draft that we want the AI to fix
bad_text = “AI agents run in a loop and use LLMs to parse data.”
# We run the agent to process and improve the text
result = writing_agent.run(bad_text)
# We print the finished result to the screen
print(result)
Detailed line-by-line code explanation
This Python script is quite straightforward, but let’s break it down in detail to understand the magic under the hood.
- from google.adk.agents import LlmAgent: Here we import the main component from Google’s library that allows us to construct intelligent agents in just a few lines of code.8
- writing_agent = LlmAgent(…): We create a new object (our digital assistant) and assign it to the variable writing_agent so we can easily call it later.
- name=”tech_scribe_agent”: With this line, we assign an internal name to our agent for easier identification, especially if we later build a system with multiple different agents.
- model=”gemini-3.1-pro”: We determine which “brain” the agent uses; version 3.1 Pro is perfect because it provides the superior reasoning needed for creative writing and text editing.5
- instruction=”…”: This is the heart of the agent, the textual prompt where we precisely explain to it exactly how it should behave, what tone to use, and what its ultimate goal is.
- output_key=”revised_text”: We define the key in the dictionary under which the model will save the final result, which makes it easier for us to extract that data later in larger applications.9
- bad_text = “…”: Here we simulate user input, specifically a short, unfinished sentence that we want our new AI assistant to stylistically fix and expand.
- result = writing_agent.run(bad_text): We call the run function which triggers the model’s thinking process, sending it our bad text for processing.
- print(result): At the very end, we display the improved and optimized text on the screen, ready for reading or further use.
Conclusion with application and improvement ideas
Creating your own Google Gem agent is an incredibly powerful way to save hours you would otherwise spend on proofreading and formatting content. Instead of being a regular user who constantly repeats prompts, you become a true architect of your digital team. To further improve this project, I recommend adding “Knowledge” files to your agent, like your best blogs in PDF format, so it can accurately “mimic” your personal writing style. Also, you can explore Google Opal, a great new tool for visually connecting multiple agents into complex workflows without writing a single line of code. Don’t forget to regularly test and refresh your system instructions so your results get better every day! 💡