---
title: "The Engines Powering Autonomy: Gemini 3.5 Flash and Spark"
description: "An in-depth look at Gemini 3.5 Flash and Spark, Google's next-generation models engineered for raw sequential throughput, long-horizon autonomy, and secure payments."
canonical: "https://zainahmed.net/blog/engines-powering-autonomy-gemini-3-5-flash-spark"
published: "2026-05-23"
---

# The Engines Powering Autonomy: Gemini 3.5 Flash and Spark

> Author: Zain Ahmed
> Published: 2026-05-23
> Read Time: 2 min read
> Category: AI & Tech
> Tags: Gemini 3.5, Gemini Spark, AI Agents, Python SDK, AP2
> Canonical URL: https://zainahmed.net/blog/engines-powering-autonomy-gemini-3-5-flash-spark

## Excerpt
An in-depth look at Gemini 3.5 Flash and Spark, Google's next-generation models engineered for raw sequential throughput, long-horizon autonomy, and secure payments.

---

Google made Gemini 3.5 Flash the default orchestration engine for Antigravity 2.0’s parallel agents, prioritizing throughput and fast tool execution.

It clocks an output generation speed of 289 tokens per second. That makes it roughly four times faster than comparable frontier models. That speed reduces accumulated latency when they loop through dozens of read, evaluate, and generate cycles.

The model features a 1-million-token input window alongside an expanded 64k-token output capacity. This allows it to dominate real-world execution benchmarks. It even scored 76.2% on Terminal-Bench 2.1.

## The Python SDK Architecture

If you want to build programmatic custom agents, the Google Antigravity Python SDK (`google-antigravity`) embeds the Go runtime binary directly within the PyPI wheel. It operates via an asynchronous context manager. By default, it enforces a fail-closed and read-only security posture.

You have to explicitly enable write access via `CapabilitiesConfig()`. `PreToolCallDecideHook` instances then evaluate this access to prevent Time-of-Check to Time-of-Use security vulnerabilities.

```python
import asyncio
from google.antigravity import Agent, LocalAgentConfig, CapabilitiesConfig
from google.antigravity.hooks import policy

policies = [
    policy.allow("run_command"),
    policy.deny("*") 
]

async def main():
    config = LocalAgentConfig(
        system_instructions="You are an expert architecture assistant.",
        capabilities=CapabilitiesConfig(),
        policies=policies,
    )
    
    async with Agent(config) as agent:
        response = await agent.chat("Initialize deployment script.")
        async for token in response:
            print(token, end="")

if __name__ == "__main__":
    asyncio.run(main())
```

## Gemini Spark: Breaking the Session Loop

While Antigravity handles local development, Gemini Spark takes over continuous execution. Spark agents run on dedicated Google Cloud virtual machines around the clock. They are completely detached from your local hardware state.

Spark uses Memory Banks to retain context across long-running tasks. Instead of running slow vector searches across massive databases during live sessions, Spark continuously extracts data into rigid `STRUCTURED_PROFILE` schemas.

When Spark shifts from observation to action (like making an autonomous purchase), it relies on the Agent Payments Protocol (AP2). AP2 separates the generative model from raw financial data. It requires a dual cryptographic mandate: one mandate handles the cart intent, and the other handles the payment processor. This guarantees the agent never actually touches raw credit card numbers.

---

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