Meet Your Next Personal AI Assistant: How to Build One with Gemini

Introduction

As artificial intelligence continues to evolve, so does its ability to handle complex tasks through intelligent agents autonomous systems capable of reasoning, planning, and acting on user instructions. Google’s Gemini AI, part of its powerful Vertex AI platform, introduces a transformative way to build such agents.

In this article, we will walk through how to build, create, and call an agent using Gemini AI, including code examples and architectural best practices.

What Is an AI Agent?

AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. They show reasoning, planning, and memory and have a level of autonomy to make decisions, learn, and adapt. Their capabilities are made possible in large part by the multimodal capacity of generative AI and AI foundation models. AI agents can process multimodal information like text, voice, video, audio, code, and more simultaneously; can converse, reason, learn, and make decisions. They can learn over time and facilitate transactions and business processes. Agents can work with other agents to coordinate and perform more complex workflows.

Key features of an AI agent

As explained above, while the key features of an AI agent are reasoning and acting (as described in ReAct Framework) more features have evolved over time.

• Reasoning: This core cognitive process involves using logic and available information to draw conclusions, make inferences, and solve problems. AI agents with strong reasoning capabilities can analyze data, identify patterns, and make informed decisions based on evidence and context.

• Acting: The ability to take action or perform tasks based on decisions, plans, or external input is crucial for AI agents to interact with their environment and achieve goals. This can include physical actions in the case of embodied AI, or digital actions like sending messages, updating data, or triggering other processes.

• Observing: Gathering information about the environment or situation through perception or sensing is essential for AI agents to understand their context and make informed decisions. This can involve various forms of perception, such as computer vision, natural language processing, or sensor data analysis.

• Planning: Developing a strategic plan to achieve goals is a key aspect of intelligent behavior. AI agents with planning capabilities can identify the necessary steps, evaluate potential actions, and choose the best course of action based on available information and desired outcomes. This often involves anticipating future states and considering potential obstacles.

• Collaborating: Working effectively with others, whether humans or other AI agents, to achieve a common goal is increasingly important in complex and dynamic environments. Collaboration requires communication, coordination, and the ability to understand and respect the perspectives of others.

• Self-refining: The capacity for self-improvement and adaptation is a hallmark of advanced AI systems. AI agents with self-refining capabilities can learn from experience, adjust their behavior based on feedback, and continuously enhance their performance and capabilities over time. This can involve machine learning techniques, optimization algorithms, or other forms of self-modification.

How do AI agents work?

Every agent defines its role, personality, and communication style, including specific instructions and descriptions of available tools.

• Persona: A well defined persona allows an agent to maintain a consistent character and behave in a manner appropriate to its assigned role, evolving as the agent gains experience and interacts with its environment.

• Memory: The agent is equipped in general with short term, long term, consensus, and episodic memory. Short term memory for immediate interactions, long-term memory for historical data and conversations, episodic memory for past interactions, and consensus memory for shared information among agents. The agent can maintain context, learn from experiences, and improve performance by recalling past interactions and adapting to new situations.

• Tools: Tools are functions or external resources that an agent can utilize to interact with its environment and enhance its capabilities. They allow agents to perform complex tasks by accessing information, manipulating data, or controlling external systems, and can be categorized based on their user interface, including physical, graphical, and program-based interfaces. Tool learning involves teaching agents how to effectively use these tools by understanding their functionalities and the context in which they should be applied.

• Model: Large language models (LLMs) serve as the foundation for building AI agents, providing them with the ability to understand, reason, and act. LLMs act as the "brain" of an agent, enabling them to process and generate language, while other components facilitate reason and action.

Let’s start Building a agent now

Step 1: Setting Up the Environment

Before developing and deploying an AI agent using Gemini AI, it’s essential to ensure that your local or cloud-based development environment is properly configured. This preparation includes installing the required SDKs, authenticating access, and enabling necessary APIs in your Google Cloud project. A properly set up environment ensures smooth development, secure access, and a seamless connection to Google’s Vertex AI services.

Prerequisites:

• Google Cloud Project

• Vertex AI API enabled

• Python 3.8+ or Node.js (for SDK access)

• Google Cloud CLI installed and authenticated

Installation

pip install google-cloud-aiplatform
gcloud auth application-default login

Configure your project:

gcloud config set project [PROJECT_ID]

Step 2: Designing the Agent

Before implementation, it’s important to outline what your agent should do. Consider the following:

A well-defined goal and input/output design ensures that your agent is focused and capable of delivering meaningful outcomes.

Step 3: Creating the Agent with Gemini AI

Using the Vertex AI Python SDK, you can create an agent by interacting with the Gemini Pro model. Here’s a basic example.

Example: Initializing and Prompting Gemini

from vertexai.preview.generative_models import GenerativeModel
model = GenerativeModel("gemini-pro")

prompt = """
You are a travel planning assistant. Ask the user:
1. Preferred destination type (beach, city, mountain)
2. Budget range
3. Travel dates

Then recommend three locations with reasons.
"""

chat = model.start_chat()
response = chat.send_message(prompt)

print(response.text)

GenerativeModel("gemini-pro"): This line initializes an instance of the Gemini language model provided by Vertex AI.

start_chat(): This method creates a chat session with the Gemini model that maintains context over time.

send_message(): This method sends a prompt or message to the Gemini model and returns an intelligent, generated response.

Step 4: Enhancing Agents with Tool Use

Real-world agents often require access to external APIs or services (e.g., weather data, calendar tools, search engines). While Gemini Pro doesn't directly call APIs yet (as of mid-2025), you can simulate tool use by integrating responses with custom logic.

Simulated Tool Example

def get_weather(destination):
    return f"The weather in {destination} is currently 26°C and sunny."

user_input = "I want to go to Tokyo in July with a moderate budget."

response = chat.send_message(
    f"User says: {user_input}. Also, weather info: {get_weather('Tokyo')}."
)

print(response.text)

Step 5: Calling the Agent in Real Applications

Once you've built and tested your Gemini AI agent, the next step is deployment—making the agent available to users or systems in the real world. The flexibility of Gemini models and Google Cloud infrastructure allows you to integrate your agent into a variety of platforms, including:

• RESTful APIs: You can expose your Gemini AI agent as a REST API endpoint, allowing external systems (like frontend apps or third-party services) to communicate with it using standard HTTP requests.

• Web and Mobile Applications: Integrate your Gemini-powered agent directly into user interfaces.

• Enterprise Systems (CRM, ERP): Gemini agents can be embedded in enterprise platforms.

• Messaging Platforms (Slack, Google Chat, MS Teams): Use Gemini AI to power smart assistants within internal messaging tools.

Step 6: Testing and Optimizing

No agent is perfect on day one. You’ll need to:

• Test extensively with different user inputs

• Log and monitor performance and edge cases

• Tune prompts based on real-world feedback

• Incorporate guardrails to manage risk and compliance

Using Vertex AI’s built-in tools or custom logging systems, you can iterate quickly and safely.

Conclusion

The Gemini AI platform offers a robust and flexible foundation for building intelligent agents that can understand, plan, and act. Whether you’re creating a travel assistant, a financial planner, or a smart helpdesk bot, Gemini’s language understanding and multimodal capabilities set a new standard in AI agent design.

Key Benefits:

• Easy to start with powerful SDKs

• Rich natural language reasoning

• Ability to integrate tools and APIs

• Scalable deployment across platforms