How to Build an Agent With Gemini 3

When you watch an AI agent edit files, run commands, recover from errors, and solve tasks step by step, it feels like magic.But it isn’t.

The truth is simple: an agent is just a large language model running in a loop, with tools it can choose to use.

If you can write a while loop in Python, you can build an agent.

This guide walks through the process step by step. We start with a basic Gemini 3 call, then progressively add memory, tools, and control flow until we end up with a fully working agent.

What Actually Is an Agent?

Traditional software follows fixed paths:

Step A → Step B → Step C
Agents are different. An agent uses a language model to decide the control flow dynamically based on a goal.

At a minimum, every agent has four parts:

1. The Model (the brain)A Gemini model that reasons, plans, and decides what to do next.
2. Tools (hands and eyes)Functions the agent can execute to interact with the outside world. Files, APIs, databases, services.
3. Context / Memory (workspace)The information the agent has access to at any moment.
4. The Loop (life)A loop that lets the agent observe, decide, act, and observe again until the task is complete.

The Agent Loop (Core Mental Model)

Nearly every agent follows the same loop:

1. You define the available tools and describe them to the model
2. You send the user prompt + context to the model
3. The model decides whether it needs a tool
4. If so, it returns a structured tool call
5. Your code executes the tool
6. You send the tool result back to the model
7. The model either calls another tool or produces the final response

Step 1: Start With Basic Text Generation

Before we build an agent, we start with a simple abstraction that maintains conversation state. This is not an agent yet. It has memory, but no ability to act.

from google import genai

class Agent:
def __init__(self, model: str):
self.model = model
self.client = genai.Client()
self.contents = []

def run(self, message: str):
self.contents.append({
"role": "user",
"parts": [{"text": message}]
})

response = self.client.models.generate_content(
model=self.model,
contents=self.contents
)

self.contents.append(response.candidates[0].content)
return response.text
At this stage, the model can remember prior messages, but it has no hands and no eyes. It cannot interact with the environment.

Step 2: Give the Agent Hands and Eyes (Tool Use)

To turn this into an agent, we add tools.

A tool has two parts:

1. The definition (what the model sees)
2. The implementation (what your code runs)

Tool definitions (schemas)

read_file_definition = {
"name": "read_file",
"description": "Reads a file and returns its contents.",
"parameters": {
"type": "object",
"properties": {
"file_path": {
"type": "string",
"description": "Path to the file to read"
}
},
"required": ["file_path"]
}
}
Clear naming and precise descriptions are critical. The model relies on these descriptions to decide when to use a tool.

Tool implementations

def read_file(file_path: str) -> str:
with open(file_path, "r") as f:
return f.read()

Step 3: Integrate Tools Into the Agent

Now we register tools and pass them to the model.

from google.genai import types

tools = {
"read_file": {
"definition": read_file_definition,
"function": read_file
}
}
Inside the agent:

config = types.GenerateContentConfig(
tools=[
types.Tool(
function_declarations=[tool["definition"] for tool in tools.values()]
)
]
)
At this point, the model can request a tool call, but nothing happens yet. We still need to close the loop.

Step 4: Close the Loop (This Is the Agent)

An agent is not about generating one tool call. It’s about generating a sequence of tool calls and reacting to the results.

class Agent:
def __init__(self, model, tools, system_instruction):
self.model = model
self.client = genai.Client()
self.contents = []
self.tools = tools
self.system_instruction = system_instruction

def run(self, message):
self.contents.append({
"role": "user",
"parts": [{"text": message}]
})

response = self.client.models.generate_content(
model=self.model,
contents=self.contents,
config=types.GenerateContentConfig(
system_instruction=self.system_instruction,
tools=[types.Tool(
function_declarations=[tool["definition"] for tool in self.tools.values()]
)]
)
)

self.contents.append(response.candidates[0].content)

if response.function_calls:
for call in response.function_calls:
tool = self.tools.get(call.name)
result = tool["function"](**call.args)

self.contents.append({
"role": "user",
"parts": [{
"functionResponse": {
"name": call.name,
"response": result
}
}]
})

return self.run("") # continue loop

return response

Gemini 3 requirement: thought signatures

Gemini 3 uses thought signatures to preserve reasoning state across calls. These must be returned exactly as received when looping, or tool calls may fail.

If you use the SDK’s chat/session abstractions, this is handled for you. If you manually manage message parts, you must preserve them carefully.

Step 5: Turn It Into a CLI Agent

Once you have the loop, creating a CLI agent is trivial.

agent = Agent(
model="GEMINI_3_MODEL_ID",
tools=tools,
system_instruction="You are a helpful coding assistant."
)

print("Agent ready. Type 'exit' to quit.")
while True:
user_input = input("You: ")
if user_input.lower() in ["exit", "quit"]:
break
response = agent.run(user_input)
print("Agent:", response.text)

Best Practices for Agents

Here are a few things to consider when creating your agent.

1. Tool design

• Use clear, human-readable names
• Write descriptions like instructions, not summaries
• Return meaningful errors the model can reason about

Context engineering

• Avoid dumping large datasets into context
• Load data just-in-time via tools
• Summarize or compress long histories

Control and safety

• Add max-iteration limits
• Use system instructions for hard rules
• Require human confirmation for sensitive actions
• Log every tool call for debugging

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