《实用笔记》：反重力管理智能体教程：舰船生产AI智能体
《实用笔记》操作指南：反重力管理智能体教程：舰船生产AI智能体——适用于采用该模式的团队的合同、检查项及可直接插入的代码位置。
以下笔记为“反重力管理代理教程：飞船生产AI代理”提供了一条实用的学习路径。重点在于合同定义、校验机制以及可直接插入的代码占位符，而非激励性描述。 在完成概览阶段时，首先列出合同要求：所需输入、成功信号以及部分失败时的处理方式。这样的清单能确保后续的代码修改更加规范。 建议采用小型、可测试的单元而非庞大的脚本。当某一步骤失败时，故障应能指向单一责任点，而非复杂的流程链。
from google import genai
import subprocess
client = genai.Client()
# Step 1: Ask the model for code
response = client.models.generate_content(
model="gemini-2.5-flash",
contents="Write a Python script that checks password strength and saves a report."
)
# Step 2: YOU must manually extract the code block from the text response
code = extract_code_from_markdown(response.text) # You wrote this function
# Step 3: YOU must save the file and run it locally
with open("password_checker.py", "w") as f:
f.write(code)
# ⚠️ Danger: Running untrusted AI-generated code on YOUR machine!
result = subprocess.run(["python3", "password_checker.py"], capture_output=True, text=True)
print(result.stdout)
什么是管理代理？
“受管理代理是什么”这一阶段若被视为可度量的对象，效果会更好。在扩大范围之前，先收集一份典型的成功案例、一个故障实例以及回滚说明。 将这一阶段视为输入与已验证输出之间的契约。为相关成果命名，明确成功标准，绝不允许出现无声的、不完整的处理结果。 保持图结构的层次清晰且类型明确。嵌套的数据块会掩盖哪个节点修改了哪个字段的信息，还会在处理中断后导致无法继续。
人工智能系统的三个层级
将“三个层级”模型视为可度量的框架来使用效果最佳。在扩大范围之前，先记录一个成功的案例、一个失败案例以及回滚说明。在功能结果旁同时记录执行时间以及令牌或查询成本。提前了解成本情况，就能避免在从演示环境过渡到共享环境时出现意外费用。要保持图表状态简洁且类型明确，嵌套的数据块会掩盖是哪个节点修改了哪个字段，还会在出现中断后导致流程无法继续。
核心智能代理循环
将“核心代理循环”阶段视为可度量的对象来处理时，其效果最佳。在扩大范围之前，需记录一份理想的操作日志、一个故障案例以及回滚说明。 应将配置置于应用程序代码之外。环境文件、密钥存储和功能标志应集中存放于一个位置，以便操作人员无需查看整个系统结构即可进行审计。 需保持系统状态的简洁性与类型化。嵌套的数据结构会掩盖具体是哪个节点修改了哪个字段，还会导致中断后无法继续执行。 将“核心代理循环”阶段视为可度量的对象来处理时，其效果最佳。在扩大范围之前，需记录一份理想的操作日志、一个故障案例以及回滚说明。 相比庞大的脚本，更应采用小型且可测试的单元。当某个步骤出现故障时，故障原因应能明确指向单一责任模块，而非复杂的流程链。
┌────────────────────────────────────────┐
│ 1. PLAN & REASON │
│ (Analyzes objective & breaks it down) │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ 2. ACT │
│ (Executes a tool: Bash, Python, Web) │
└───────────────────┬────────────────────┘
│
▼
┌────────────────────────────────────────┐
│ 3. OBSERVE │
│ (Reads execution output, errors, data) │
└───────────────────┬────────────────────┘
│
└─── Loop back until task is complete
远程沙箱
在远程沙箱阶段，应在修改代码之前明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 将此阶段视为输入与经过验证的输出之间的契约。为相关成果命名，定义成功判定标准，并拒绝默许的半完成状态。 对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的连接方式并不等同于业务上的完整性。
架构深度解析：运作原理
在“架构深入分析”阶段，修改代码之前需明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。应在功能结果旁记录执行时间以及令牌或查询成本。提前了解成本情况，可避免在从演示环境过渡到共享环境时出现意外费用。对于会产生支出或修改生产数据的操作，必须经过人工审批。编译时的配置并不等同于业务功能的完整性。
┌─────────────────────────────────┐
│ YOUR APP │
└────────┬───────────────┬────────┘
│ │
1. Define/Configure │ │ 2. Run Task
(System rules, ID) │ │ (Send Prompt)
▼ ▼
┌───────────────────────────────────┐ ┌───────────────────────────────────┐
│ THE CONTROL PLANE │ │ THE RUNTIME PLANE │
│ (Agents API) │ │ (Interactions API) │
├───────────────────────────────────┤ ├───────────────────────────────────┤
│ Saves persistent identity, system │ │ Spawns the Ubuntu sandbox, logs │
│ instructions, and data mounts. │ │ live traces, and processes loops. │
└───────────────────────────────────┘ └───────────────────────────────────┘
安全性：出口代理
在安全方面的出口代理阶段，修改代码之前需明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 配置应置于应用程序代码之外。环境文件、密钥存储以及功能标志应集中存放于一个位置，以便操作人员无需查看整个系统结构即可进行审计。 对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的连接方式并不等同于业务功能的完整性。 在安全方面的出口代理阶段，修改代码之前需明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 相较于庞大的脚本，应优先使用小型且可测试的单元。当某个步骤失败时，故障原因应能明确指向单一责任模块，而非复杂的整体问题。
管道。 ┌─────────────────────────┐
│ REMOTE UBUNTU SANDBOX │
│ (No internal secrets) │
└───────────┬─────────────┘
│ Agent attempts outbound API call
▼
┌─────────────────────────┐
│ EGRESS PROXY LAYER │ ◄── Checks Domain Allowlist
├─────────────────────────┤
│ Intercepts connection, │
│ injects secrets securely│
└───────────┬─────────────┘
│ Safe, Authenticated Request Sent
▼
[ External Target API ]
入门指南：5分钟内创建您的第一个智能体
在完成“入门指南：您的第一个智能体”这一阶段时，首先需明确合同条款：所需的输入参数、成功信号以及部分失败时的处理方式。这样的检查清单能确保后续的代码修改保持一致性。 将这一阶段视为输入与验证后输出之间的契约。为相关文件命名，定义成功检测标准，并杜绝无声的局部完成。 在成本较高的步骤之后设置检查点。当操作员重新尝试后续节点时，恢复流程不应再次调用相同的大型语言模型。
前提条件
在完成前置条件阶段时，首先写下相关约定：所需的输入参数、成功信号以及部分失败时的处理方式。这样的清单能确保后续的代码修改保持一致性。 在功能结果旁记录执行时间以及令牌或查询成本。提前了解成本情况，可避免在从演示环境过渡到共享环境时出现意外费用。 在耗时较高的步骤之后设置检查点。当操作员重新尝试后续节点时，恢复流程不应再次收取相同的LLM调用费用。
python3 -m venv managed-agents-env
source managed-agents-env/bin/activate # On Windows: managed-agents-env\Scripts\activate
# Install (or upgrade) the Google GenAI SDK
pip install -U google-genai
# Verify the version — must be 1.14.0 or later
python3 -c "import google.genai; print(google.genai.__version__)"
# Set your API key (get one at https://aistudio.google.com/apikey)
export GEMINI_API_KEY="your-api-key-here"
运行示例
在完成“运行示例”阶段时，首先需明确相关规范：所需输入、成功标志以及部分失败时的处理方式。这样的清单能确保后续的代码修改保持一致性。 将配置信息与应用程序代码分开存放。环境文件、密钥存储以及功能开关应集中于一处，以便操作员无需查看整个系统结构即可进行审计。 在耗时较高的步骤之后设置检查点。当操作员重新尝试后续节点时，恢复流程不应再次调用相同的大型语言模型。 在完成“运行示例”阶段时，首先需明确相关规范：所需输入、成功标志以及部分失败时的处理方式。这样的清单能确保后续的代码修改保持一致性。 优先选择小型、可测试的单元，而非庞大的脚本。当某个步骤失败时，故障应能指向具体的责任模块，而非整个复杂的流程。
# 1. Save the code to a file (an example name used here is hello_agent.py
vim hello_agent.py # or use any editor
# 2. Run it
python3 hello_agent.py
您的首次交互：您好，托管代理
“首次交互：您好”阶段若被视为可度量的指标，效果会更好。在扩大范围之前，需记录一份最佳案例、一个失败案例以及回滚说明。 将此阶段视为输入与已验证输出之间的契约。为相关文档命名，明确成功标准，杜绝默许的半完成状态。 保持图形状态简洁且类型化。嵌套的数据块会掩盖哪个节点编写了哪个字段，且在中断后会导致无法继续处理。
from google import genai
client = genai.Client()
# One API call. One autonomous agent. One remote Linux sandbox.
interaction = client.interactions.create(
agent="antigravity-preview-05-2026",
input="Write a Python script that generates 100 random exam scores between 0 and 100, "
"calculates the mean, median, and standard deviation, prints a summary, "
"and saves a grade distribution histogram as grade_report.png using matplotlib.",
environment="remote" # This provisions a secure Linux sandbox hosted by Google
)
print(interaction.output_text)
python3 hello_agent.py
I have successfully written and executed a Python script that generates 100 random exam scores, computes their key statistics, and plots a grade distribution histogram.
### 1. Generated Python Script (`generate_report.py`)
Here is the Python script, which is saved in the environment as `generate_report.py`. It uses `numpy` for data generation, `statistics` conventions for calculation (such as sample standard deviation), and `matplotlib` to generate a high-quality visualization.
```python
import numpy as np
import matplotlib.pyplot as plt
import statistics
def generate_and_analyze_grades():
# Set a random seed for reproducibility
np.random.seed(42)
# Generate 100 random exam scores between 0 and 100 (inclusive)
scores = np.random.randint(0, 101, size=100)
# Calculate statistics using Python's standard statistics module and NumPy
# (Using ddof=1 for sample standard deviation, which is standard for student grade samples)
mean_val = np.mean(scores)
median_val = np.median(scores)
std_val = np.std(scores, ddof=1)
# Print summary to console
print("=========================================")
print(" EXAM GRADE REPORT SUMMARY ")
print("=========================================")
print(f"Number of Students : {len(scores)}")
print(f"Minimum Score : {np.min(scores)}")
print(f"Maximum Score : {np.max(scores)}")
print(f"Mean Score : {mean_val:.2f}")
print(f"Median Score : {median_val:.2f}")
print(f"Standard Deviation : {std_val:.2f}")
print("=========================================")
# Create the histogram plot
plt.figure(figsize=(10, 6))
# Customize the grid and style
plt.grid(axis='y', linestyle='--', alpha=0.7, zorder=0)
# Draw the histogram with bins of size 10 (0-10, 10-20, ..., 90-100)
bins = range(0, 111, 10)
plt.hist(scores, bins=bins, color='#4A90E2', edgecolor='black', rwidth=0.85, zorder=3)
# Add labels and title
plt.title('Distribution of Exam Scores', fontsize=16, fontweight='bold', pad=15)
plt.xlabel('Exam Scores', fontsize=12, labelpad=10)
plt.ylabel('Number of Students', fontsize=12, labelpad=10)
# Mark Mean and Median with vertical lines
plt.axvline(mean_val, color='#D0021B', linestyle='dashed', linewidth=2, zorder=4, label=f'Mean: {mean_val:.2f}')
plt.axvline(median_val, color='#F5A623', linestyle='dotted', linewidth=2, zorder=4, label=f'Median: {median_val:.2f}')
# Set x-ticks to align with bins
plt.xticks(range(0, 101, 10))
# Add a legend
plt.legend(fontsize=11, loc='upper left')
# Add details text box on the plot
textstr = '\n'.join((
f'Total Students: {len(scores)}',
f'Mean: {mean_val:.2f}',
f'Median: {median_val:.2f}',
f'Std Dev: {std_val:.2f}'
))
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
plt.gca().text(0.05, 0.75, textstr, transform=plt.gca().transAxes, fontsize=10,
verticalalignment='top', bbox=props)
# Adjust layout and save the figure
plt.tight_layout()
output_filename = 'grade_report.png'
plt.savefig(output_filename, dpi=300)
print(f"\nHistogram successfully saved to '{output_filename}'")
if __name__ == '__main__':
generate_and_analyze_grades()
```
---
### 2. Output Statistics Summary
Upon running the script in the sandbox environment, it produced the following output:
```text
=========================================
EXAM GRADE REPORT SUMMARY
=========================================
Number of Students : 100
Minimum Score : 1
Maximum Score : 100
Mean Score : 51.31
Median Score : 53.50
Standard Deviation : 29.70
=========================================
Histogram successfully saved to 'grade_report.png'
```
---
### 3. Generated Histogram Details (`grade_report.png`)
The script successfully outputted `grade_report.png` in high-resolution format (300 DPI) containing:
- **Clean Grid & Styling**: Standard light-gray horizontal grids to easily identify column heights.
- **Grade Bins**: Defined score ranges of size 10 (e.g., 0–10, 10–20, ..., 90–100) using custom spacing to give the columns a professional look.
- **Key Metrics Highlighted**:
- A red dashed line representing the **Mean** (`51.31`).
- A yellow dotted line representing the **Median** (`53.50`).
- **An Info Legend and Box**: In the top-left area summarizing all descriptive statistics for easy offline reference.
### How to Run it Yourself
If you want to run this script in another environment, ensure you have the required packages installed:
```bash
pip install matplotlib numpy
python generate_report.py
```
from google import genai
client = genai.Client()
interaction = client.interactions.create(
agent="antigravity-preview-05-2026",
input="""
Research the top 5 most popular Python web frameworks in 2026.
For each framework:
1. Find the latest version number and release date
2. Count the GitHub stars
3. Write a comparison table in Markdown
""",
environment="remote"
)
print(interaction.output_text)
The landscape of Python web development in **2026** is marked by a fascinating split [1, 2]. On one side, classic **backend-first** and **API-focused** frameworks (Django, FastAPI, Flask) remain the dominant standards for enterprise applications and microservices [3, 5]. On the other side, **pure-Python UI and full-stack** frameworks (Streamlit, Reflex) have seen explosive growth—driven by the need to build interactive web apps, data dashboards, and AI/ML interfaces without writing JavaScript or TypeScript [1, 2].
Below is an in-depth research report on the **top 5 most popular Python web frameworks in 2026**, ranked by GitHub stars and overall developer adoption.
---
### Detailed Breakdown of the Top 5 Python Web Frameworks
#### 1. FastAPI (99,034 GitHub Stars)
* **Latest Version:** `0.136.3` (Released: **May 23, 2026**)
* **Architecture Type:** ASGI (Asynchronous Server Gateway Interface)
* **Primary Focus:** High-Performance APIs and Microservices [4]
* **Core Philosophy:** FastAPI is designed to build APIs quickly with standard Python type hints. It leverages **Pydantic v2** for extremely fast data validation and serialization, and **Starlette** for high-concurrency async handling [3, 4]. It natively auto-generates interactive Swagger UI and ReDoc documentation.
* **Ideal Use Case:** AI/ML model deployment, backend APIs for single-page applications (React/Vue/Svelte), and low-latency microservices [5].
#### 2. Django (87,811 GitHub Stars)
* **Latest Version:** `6.0.6` (Released: **June 3, 2026**)
* **Architecture Type:** WSGI & ASGI (Fully async-supported)
* **Primary Focus:** "Batteries-Included" Monolithic Web Applications [4]
* **Core Philosophy:** As an 18-year industry standard, Django remains the premier full-featured web framework [2]. It handles everything—database routing (via its powerful ORM), administration panels, user authentication, form validation, and robust security defaults (SQLi, CSRF, XSS protection) [1]. In 2026, Django 6.x is highly optimized with full native asynchronous views and type hints [4].
* **Ideal Use Case:** Large-scale enterprise applications, content management systems, e-commerce platforms, and fast SaaS MVP development where you need a complete, secure architecture out of the box [2, 5].
#### 3. Flask (71,638 GitHub Stars)
* **Latest Version:** `3.1.3` (Released: **February 19, 2026**)
* **Architecture Type:** WSGI
* **Primary Focus:** Lightweight, Flexible Microframework [4]
* **Core Philosophy:** Flask provides a minimalist core, leaving the choice of database ORM, form handling, and security components completely up to the developer [1, 2]. Paired with modern 2026 extensions (SQLAlchemy 2.x, Pydantic), it represents a simple, un-opinionated foundation that never gets in the developer's way [3].
* **Ideal Use Case:** Small utility applications, lightweight APIs, microservices, and custom projects where developers want total control over their system design [1, 5].
#### 4. Streamlit (44,895 GitHub Stars)
* **Latest Version:** `1.58.0` (Released: **May 28, 2026**)
* **Architecture Type:** UI-First Scripting Paradigm
* **Primary Focus:** Rapid Data and Machine Learning Applications [1]
* **Core Philosophy:** Streamlit turns standard Python scripts into interactive, beautiful web apps in a matter of minutes. It handles the entire frontend and backend flow by re-running the script from top to bottom whenever a user interacts with a widget.
* **Ideal Use Case:** Quick data dashboards, machine learning model prototypes, and internal analytical tools for teams without frontend resources [1, 2].
#### 5. Reflex (28,467 GitHub Stars)
* **Latest Version:** `0.9.4` (Released: **June 4, 2026**)
* **Architecture Type:** Full-Stack Async React-Compiled
* **Primary Focus:** Interactive, Pure-Python Full-Stack Apps [2]
* **Core Philosophy:** Formerly known as Pynecone, Reflex compiles Python code into a high-performance **React/Next.js frontend** and a **FastAPI backend**, using real-time WebSockets to synchronize states between them [1, 2]. It bypasses JavaScript entirely, offering 60+ pre-built Radix UI components with native Tailwind integration [1, 2].
* **Ideal Use Case:** Complex, interactive web applications, real-time streaming dashboards (e.g., AI chat applications, financial trackers), and user-facing SaaS applications built entirely in Python [2].
---
### Other Honorable Mentions in 2026
While they didn't make the top 5 by GitHub stars, these frameworks are heavily utilized:
* **Tornado** (22,182 stars, v6.5.7, Released June 8, 2026): A mature, asynchronous networking framework ideal for long-lived WebSocket connections [4].
* **Sanic** (18,629 stars, v25.12.1, Released May 31, 2026): An ASGI web framework built for extreme speed and Flask-like simplicity, running on its own high-performance web server.
* **Litestar** (8,269 stars, v2.23.0, Released May 29, 2026): A highly structured, strict, and enterprise-grade ASGI alternative to FastAPI [3, 4].
---
### 2026 Python Web Frameworks Comparison Table
| Framework | GitHub Stars | Latest Version | Release Date | Architecture | Best For | Key Strength | Learning Curve |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| **FastAPI** | ~99,034 | `0.136.3` | May 23, 2026 | ASGI (Async) | High-performance API development & AI model deployments [4, 5] | Autogenerated Swagger docs, native Pydantic v2 validation [4] | **Easy / Moderate** |
| **Django** | ~87,811 | `6.0.6` | Jun 03, 2026 | WSGI / ASGI | Enterprise apps, content-heavy websites, secure SQL backends [1, 2] | "Batteries-included" (built-in admin, ORM, security, auth) [1, 2] | **Moderate / Hard** |
| **Flask** | ~71,638 | `3.1.3` | Feb 19, 2026 | WSGI (Sync-first) | Microservices, custom MVPs, lightweight apps [1, 5] | Unrivaled design flexibility, minimal core overhead [1] | **Very Easy** |
| **Streamlit**| ~44,895 | `1.58.0` | May 28, 2026 | Scripted Reactive | Data visualization dashboards & ML interactive tools [1] | Zero frontend experience needed; fast prototyping [1] | **Very Easy** |
| **Reflex** | ~28,467 | `0.9.4` | Jun 04, 2026 | Async React-compiled | Interactive, real-time SaaS & full-stack apps in pure Python [2] | True multi-page routing, state management without JavaScript [2] | **Moderate** |
---
### Guidance: Which Framework Should You Choose in 2026?
1. **Choose FastAPI** if you are building modern REST/GraphQL APIs, backend microservices, or deploying AI/ML models with high concurrency and automatic documentation [3, 5].
2. **Choose Django** if you are building database-driven business platforms or SaaS architectures where user authentication, administrative interfaces, and built-in security are paramount [1, 2].
3. **Choose Flask** if you need to build simple backend services or you want to hand-select every tool in your stack (e.g., combining custom database layers with external identity providers) [1].
4. **Choose Streamlit** if your core team consists of data scientists or business analysts who need to quickly present internal analytics, charts, and interactive widgets without building a complex web architecture [1, 2].
5. **Choose Reflex** if you want to build a fully interactive, production-ready full-stack application (with complex states, WebSocket interactions, and beautiful custom styles) but do not want to divide your team between Python and JavaScript [2].
---
### Sources & References
* [1] [Best Python Web Frameworks 2026 Compared - Reflex](https://reflex.dev/blog/top-python-web-frameworks/)
* [2] [Django vs Flask vs Reflex (April 2026) - Reflex](https://reflex.dev/blog/django-vs-flask-vs-reflex-comparison/)
* [3] [12 Modern Python Frameworks to Try in 2026 - Medium](https://medium.com/the-pythonworld/12-modern-python-frameworks-to-try-in-2026-e7089305bb19)
* [4] [5 top Python web frameworks of 2026 - Educative.io](https://www.educative.io/blog/top-python-web-frameworks)
* [5] [The Python Backend Framework Decision Guide for 2026 - Rollbar](https://rollbar.com/blog/python-backend-frameworks/)
多轮对话：持久化沙箱状态
多轮对话持久化沙箱阶段若被视为可度量的测试环境，效果会最佳。在扩大范围之前，先记录一份理想的对话文本、一个失败案例以及回滚说明。 在功能结果旁同时记录处理时间以及令牌或查询成本。提前了解成本情况，可避免从演示环境过渡到共享环境时出现意外费用。 保持图结构的状态简洁且类型明确。嵌套的数据块会掩盖具体是哪个节点修改了哪一字段，且在中断后会导致无法继续处理。
from google import genai
client = genai.Client()
# Turn 1: Research and create a report
interaction_1 = client.interactions.create(
agent="antigravity-preview-05-2026",
input="Research the current state of carbon capture technology in 2026. "
"Write a comprehensive 500-word report and save it as report.md",
environment="remote"
)
print(f"Environment ID: {interaction_1.environment_id}")
print(f"Interaction ID: {interaction_1.id}")
print(interaction_1.output_text)
# Turn 2: Build on the previous work - same sandbox, same files
interaction_2 = client.interactions.create(
agent="antigravity-preview-05-2026",
environment=interaction_1.environment_id, # ← Re-attaches to same Ubuntu sandbox
previous_interaction_id=interaction_1.id, # ← Preserves conversation memory
input="Convert that report.md file into a clean index.html webpage "
"with modern inline CSS styling and responsive design."
" Please output the contents of the index.html file"
)
print(interaction_2.output_text)
Environment ID: 5eb9b4b8-c02f-4a8f-b7a0-c90a796c01a6
Interaction ID: v1_ChdidXNuYXJLQkF1eWlqdU1QODRmUjJRZxIXYnVzbmFyS0JBdXlpanVNUDg0ZlIyUWc
I have researched the state of carbon capture, utilization, and storage (CCUS)
technology in 2026 and compiled a comprehensive 494-word report.
The report has been successfully saved to your environment as **`report.md`**.
Below is the full text of the report,
highlighting how the CCUS sector has transitioned from pilot-stage
demonstrations to industrial infrastructure in 2026.
***
# The State of Carbon Capture Technology in 2026: From Pilot to Infrastructure
## Executive Summary
....
## Point-Source Capture: The Current Backbone
...
## Scaling Direct Air Capture (DAC)
...
## Infrastructure, Transport, and Policy
...
## Key Challenges
...
***
### Sources Cited
* **[1] S&P Global:** [2026 CCUS: Navigating the tides of the great realignment](https://www.spglobal.com/energy/en/news-research/blog/energy-transition/041426-2026-ccus-navigating-the-tides-of-the-great-realignment) (April 2026)
* **[2] Carbon Herald:** [What's Next For Carbon Capture, Utilization & Storage (CCUS) In 2026](https://carbonherald.com/whats-next-for-carbon-capture-utilization-storage-ccus-in-2026/) (January 2026)
* **[3] Nature Tech Memos:** [Top 10 Carbon Capture Startups for Corporate Partnerships in 2026](https://www.naturetechmemos.com/p/top-10-carbon-capture-startups-for-corporate-partnerships-in-2026) (April 2026)
.....
I have successfully converted your `report.md` file into a clean, modern, and highly responsive **`index.html`** webpage!
### What was done:
1. **Designed a cohesive palette:** Implemented a modern climate-tech inspired aesthetic using Slate (`#1e293b`) and a range of deep and light teals (`#0f766e`, `#14b8a6`, `#115e59`).
2. **Added an Interactive Stat Dashboard:** Created a high-level statistical summary card grid displaying key 2026 data at a glance (e.g., global Mtpa capacity, Stratos targets, US Section 45Q tax credits).
3. **Structured visual highlights:** Embedded two distinct grid comparison cards for the **Climeworks Mammoth** and **1PointFive Stratos** facilities, as well as a custom callout block highlighting the trans-European **Northern Lights** shipping route.
4. **Professionalized the typography & chemistry:** Used system-ui fonts for excellent loading speeds and correctly formatted all chemical formulas to standard subscripts (e.g., **CO<sub>2</sub>**).
5. **Citations & Interactivity:** Mapped the original references into clickable, superscript citation numbers (e.g., `[1]`) that link smoothly down to a beautifully bulleted "Sources Cited" section with custom index bullet styling.
The full HTML contents of the newly created `index.html` are shown below:
```html
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>The State of Carbon Capture Technology in 2026</title>
<style>
:root {
--primary: #0f766e;
--primary-light: #14b8a6;
--primary-dark: #115e59;
--bg: #f8fafc;
--card-bg: #ffffff;
--text-main: #1e293b;
--text-muted: #475569;
--accent: #0284c7;
--border: #e2e8f0;
--shadow: 0 4px 6px -1px rgba(15, 118, 110, 0.05), 0 2px 4px -2px rgba(15, 118, 110, 0.05);
--shadow-md: 0 10px 15px -3px rgba(15, 118, 110, 0.1), 0 4px 6px -4px rgba(15, 118, 110, 0.1);
}
* {
box-sizing: border-box;
}
body {
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
background-color: var(--bg);
color: var(--text-main);
line-height: 1.8;
margin: 0;
padding: 0;
-webkit-font-smoothing: antialiased;
}
<!-- LOTS MORE CSS STUFF :-) -->
footer {
text-align: center;
color: var(--text-muted);
font-size: 0.875rem;
margin-top: 4rem;
padding-top: 2rem;
border-top: 1px solid var(--border);
}
</style>
</head>
<body>
<header>
<div class="header-content">
<span class="meta">Special Intelligence Report</span>
<h1>The State of Carbon Capture Technology in 2026</h1>
<p>From Pilot to Infrastructure</p>
</div>
</header>
<main>
<!-- Key Metrics Dashboard -->
<div class="stats-grid">
<div class="stat-card">
<div class="stat-num">~73 Mtpa</div>
<div class="stat-label">Global Operational Capacity</div>
</div>
<div class="stat-card">
<div class="stat-num">500k Tons</div>
<div class="stat-label">Stratos DAC Annual Target</div>
</div>
<div class="stat-card">
<div class="stat-num">Up to $180</div>
<div class="stat-label">US 45Q Subsidy per Ton</div>
</div>
<div class="stat-card">
<div class="stat-num">36,000 T</div>
<div class="stat-label">Mammoth Iceland Capacity</div>
</div>
</div>
<div class="content-wrapper">
<!-- Executive Summary -->
<section id="executive-summary">
<h2>Executive Summary</h2>
<p>
In 2026, the Carbon Capture, Utilization, and Storage (CCUS) industry is undergoing an "industrial hardening" phase, transitioning decisively from demonstration-stage pilots to commercial-scale infrastructure<sup><a href="#ref-1" class="citation-link">[1]</a></sup>. Global operational capture capacity has reached approximately 73 million metric tons per annum (Mtpa) as of mid-2026, up from 50 Mtpa in early 2025<sup><a href="#ref-1" class="citation-link">[1]</a></sup><sup><a href="#ref-2" class="citation-link">[2]</a></sup>. This momentum is propelled by robust climate policies, corporate carbon-removal commitments, and the commissioning of megaton-scale facilities.
</p>
</section>
<!-- Point-Source Capture -->
<section id="point-source">
<h2>Point-Source Capture: The Current Backbone</h2>
<p>
Point-source carbon capture remains the commercial backbone of CCUS<sup><a href="#ref-2" class="citation-link">[2]</a></sup>. Technologies deployed at industrial and energy facilities—such as cement, steel, chemicals, and refining—now represent the vast majority of active capture capacity. Post-combustion chemical absorption using amine-based solvents is the most widely deployed technology<sup><a href="#ref-2" class="citation-link">[2]</a></sup>. Companies like Carbon Upcycling Technologies are successfully integrating capture systems with utilization, converting captured carbon dioxide (CO<sub>2</sub>) into high-quality construction materials, turning emissions from cement manufacturers into a low-carbon concrete feedstock<sup><a href="#ref-3" class="citation-link">[3]</a></sup>.
</p>
</section>
<!-- Scaling Direct Air Capture -->
<section id="dac">
<h2>Scaling Direct Air Capture (DAC)</h2>
<p>
Direct Air Capture is experiencing a dramatic scale-up<sup><a href="#ref-4" class="citation-link">[4]</a></sup>. Climeworks' "Mammoth" facility in Iceland, operational since May 2024, captures up to 36,000 tons of CO<sub>2</sub> annually, storing it permanently underground via Carbfix mineralization<sup><a href="#ref-4" class="citation-link">[4]</a></sup><sup><a href="#ref-5" class="citation-link">[5]</a></sup>. Meanwhile, 1PointFive's (a subsidiary of Occidental Petroleum) "Stratos" facility in Ector County, Texas, is entering active operation in 2026<sup><a href="#ref-4" class="citation-link">[4]</a></sup>. Designed to capture up to 500,000 tons of atmospheric CO<sub>2</sub> annually using liquid solvent technology licensed from Carbon Engineering, Stratos is currently the world’s largest DAC plant<sup><a href="#ref-4" class="citation-link">[4]</a></sup><sup><a href="#ref-6" class="citation-link">[6]</a></sup>.
</p>
<div class="highlight-grid">
<div class="highlight-box">
<h4>Climeworks: Mammoth (Iceland)</h4>
<p>Nameplate capacity of 36,000 tons/year. Relies on solid-sorbent collectors powered by clean geothermal energy with deep Carbfix basaltic storage.</p>
</div>
<div class="highlight-box">
<h4>1PointFive: Stratos (Texas)</h4>
<p>World's largest facility with 500,000 tons/year target. Employs liquid-solvent infrastructure designed for rapid regional scalability.</p>
</div>
</div>
<p>
Major technology firms (including Microsoft, Google, Meta, and Amazon) have signed multi-year offtake agreements for high-quality, durable carbon credits, paying between $200 and $300 per ton, though current baseline DAC capture costs remain high, between $400 and $1,000 per ton<sup><a href="#ref-3" class="citation-link">[3]</a></sup><sup><a href="#ref-7" class="citation-link">[7]</a></sup>.
</p>
</section>
<!-- Infrastructure, Transport, and Policy -->
<section id="infrastructure">
<h2>Infrastructure, Transport, and Policy</h2>
<p>
The commercial viability of carbon capture relies heavily on dedicated transportation and storage networks<sup><a href="#ref-2" class="citation-link">[2]</a></sup>. In Europe, 2026 marks the active operation of Norway’s "Northern Lights" project, the world's first open-source CO<sub>2</sub> transport and storage network<sup><a href="#ref-8" class="citation-link">[8]</a></sup>. For example, Yara's flagship Sluiskil ammonia plant in the Netherlands is liquefying up to 800,000 tons of CO<sub>2</sub> annually to be shipped by Northern Lights for permanent undersea storage<sup><a href="#ref-8" class="citation-link">[8]</a></sup>. Concurrently, Denmark’s "Greensand" offshore storage initiative is beginning operations<sup><a href="#ref-2" class="citation-link">[2]</a></sup>.
</p>
<div class="callout">
<div class="callout-title">The Trans-European Shipping Pathway</div>
<p>Northern Lights bridges emission-heavy inland industrial sites like Yara Sluiskil in the Netherlands directly with permanent injection storage wells beneath the North Sea seabed.</p>
</div>
<p>
On the policy front, the sector is heavily anchored by government subsidies. In the United States, the Inflation Reduction Act’s (IRA) modified Section 45Q tax credit provides up to $180 per metric ton for DAC and $85 per metric ton for point-source capture<sup><a href="#ref-9" class="citation-link">[9]</a></sup>. In the European Union, the Net Zero Industry Act has accelerated cross-border transport approvals, providing regulatory certainty.
</p>
</section>
<!-- Key Challenges -->
<section id="challenges">
<h2>Key Challenges</h2>
<p>
Despite rapid progress, critical bottlenecks persist. Chief among these is the high energy intensity of DAC, which requires 1.5 to 2.5 megawatt-hours (MWh) of zero-carbon energy per ton of captured CO<sub>2</sub><sup><a href="#ref-7" class="citation-link">[7]</a></sup>. Permitting delays for geologic injection wells—specifically the EPA’s rigorous Class VI permits in the United States—also restrict how fast captured carbon can be sequestered<sup><a href="#ref-4" class="citation-link">[4]</a></sup>. Overcoming these economic and infrastructure hurdles remains essential to achieving megaton targets by 2030.
</p>
</section>
<!-- Sources Cited -->
<div class="sources-section">
<h3>Sources Cited</h3>
<ul class="sources-list">
<li id="ref-1" data-index="1"><strong>S&P Global:</strong> <a href="https://www.spglobal.com/energy/en/news-research/blog/energy-transition/041426-2026-ccus-navigating-the-tides-of-the-great-realignment" target="_blank">2026 CCUS: Navigating the tides of the great realignment</a> (April 2026)</li>
<li id="ref-2" data-index="2"><strong>Carbon Herald:</strong> <a href="https://carbonherald.com/whats-next-for-carbon-capture-utilization-storage-ccus-in-2026/" target="_blank">What's Next For Carbon Capture, Utilization & Storage (CCUS) In 2026</a> (January 2026)</li>
<li id="ref-3" data-index="3"><strong>Nature Tech Memos:</strong> <a href="https://www.naturetechmemos.com/p/top-10-carbon-capture-startups-for-corporate-partnerships-in-2026" target="_blank">Top 10 Carbon Capture Startups for Corporate Partnerships in 2026</a> (April 2026)</li>
<li id="ref-4" data-index="4"><strong>Senken:</strong> <a href="https://www.senken.io/blog/top-direct-air-capture-carbon-removal-projects-buyers-guide" target="_blank">The Top 3 Direct Air Capture Carbon Removal Projects</a> (February 2026)</li>
<li id="ref-5" data-index="5"><strong>Climeworks:</strong> <a href="https://climeworks.com/plant-mammoth" target="_blank">Mammoth: our newest direct air capture and storage facility</a> (May 2024)</li>
<li id="ref-6" data-index="6"><strong>Carbon Credits:</strong> <a href="https://carboncredits.com/top-3-carbon-capture-leaders-to-drive-the-net-zero-race-in-2026/" target="_blank">Top 3 Carbon Capture Leaders to Drive the Net-Zero Race in 2026</a> (January 2026)</li>
<li id="ref-7" data-index="7"><strong>Energy Solutions Intelligence:</strong> <a href="https://energy-solutions.co/articles/sub/carbon-capture-direct-air-dac-cost-analysis" target="_blank">Direct Air Capture in 2026: Cost, Scale, and Path to $200/tCO2</a> (January 2026)</li>
<li id="ref-8" data-index="8"><strong>World Economic Forum:</strong> <a href="https://www.weforum.org/stories/2026/01/scale-carbon-capture-storage-climate-action/" target="_blank">How to scale carbon capture and storage for climate action</a> (January 2026)</li>
<li id="ref-9" data-index="9"><strong>International Energy Agency (IEA):</strong> <a href="https://www.iea.org/policies/16255-inflation-reduction-act-2022-sec-13104-extension-and-modification-of-credit-for-carbon-oxide-sequestration" target="_blank">Inflation Reduction Act 2022: Sec. 13104 Extension and Modification of Credit for Carbon Oxide Sequestration</a> (February 2026)</li>
</ul>
</div>
<footer>
<p style="text-align: center; color: var(--text-muted); font-size: 0.875rem; margin: 0;">© 2026 Carbon Capture Intelligence. Compiled June 2026.</p>
</footer>
</div>
</main>
</body>
</html>
```
理解两种标识符
将“理解两个ID”阶段视为可度量的对象来处理时，效果最佳。在扩大范围之前，需记录一份理想状态下的日志、一个故障案例以及回滚说明。 应将配置与应用程序代码分开。环境文件、密钥存储和功能标志应集中存放于一处，以便操作人员无需查看整个系统结构即可进行审计。 需保持系统状态的简洁性与类型化。嵌套的数据结构会掩盖是哪个节点修改了哪个字段，还会导致在出现中断后无法继续执行。 将“理解两个ID”阶段视为可度量的对象来处理时，效果最佳。在扩大范围之前，需记录一份理想状态下的日志、一个故障案例以及回滚说明。 相比庞大的脚本，更应优先使用小型且可测试的单元。当某个步骤失败时，故障应指向单一的责任模块，而非复杂的流程链。
下载文件
在下载文件阶段，应在修改代码之前明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 将此阶段视为输入与经过验证的输出之间的契约。为相关成果命名，定义成功判定标准，并拒绝默许的半完成状态。 对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的配置并不等同于业务上的完整性。
import os
import requests
import tarfile
from google import genai
client = genai.Client()
# Turn 1: Research and create a report
interaction_1 = client.interactions.create(
agent="antigravity-preview-05-2026",
input="Research the current state of carbon capture technology in 2026. "
"Write a comprehensive 500-word report and save it as report.md",
environment="remote"
)
print(f"Environment ID: {interaction_1.environment_id}")
print(f"Interaction ID: {interaction_1.id}")
print(interaction_1.output_text)
# Turn 2: Build on the previous work - same sandbox, same files
interaction_2 = client.interactions.create(
agent="antigravity-preview-05-2026",
environment=interaction_1.environment_id, # ← Re-attaches to same Ubuntu sandbox
previous_interaction_id=interaction_1.id, # ← Preserves conversation memory
input="Convert that report.md file into a clean index.html webpage "
"with modern inline CSS styling and responsive design."
" Please output the contents of the index.html file"
)
print(interaction_2.output_text)
env_id = interaction_2.environment_id
api_key = os.environ.get("GEMINI_API_KEY")
response = requests.get(
f"https://generativelanguage.googleapis.com/v1beta/files/environment-{env_id}:download",
params={"alt": "media"},
headers={"x-goog-api-key": api_key},
allow_redirects=True,
)
with open("snapshot_env.tar", "wb") as f:
f.write(response.content)
os.makedirs("extracted_env_snapshot", exist_ok=True)
with tarfile.open("snapshot_env.tar") as tar:
tar.extractall(path="extracted_env_snapshot")
自定义智能体：技能、角色设定与配置
在“定制智能体技能角色”阶段，应在修改代码之前明确输入参数、该步骤的负责人以及完成标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。应在功能结果旁记录执行时间以及令牌或查询成本。提前显示成本信息，可避免在从演示环境切换到共享环境时出现意外账单。对于会产生费用或修改生产数据的操作，必须经过人工审批。编译时的配置并不等同于业务功能的完整性。
📁 Your-Project-Directory/
└── 📁 .agents/
├── 📄 AGENTS.md ← Global system instructions (persona, rules, standards)
└── 📁 skills/
└── 📁 data-cleaner/
└── 📄 SKILL.md ← Specific skill with step-by-step instructions
AGENTS.md — 智能体角色定义
在 AGENTS md 的 Agent 阶段，修改代码之前需明确输入参数、该步骤的负责人以及终止条件。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 配置信息应置于应用程序代码之外。环境文件、密钥存储以及功能标志应集中存放于一个位置，以便操作人员无需查看整个流程即可进行审计。 对于涉及资金支出或修改生产数据的操作，需设置人工审批环节。编译时的连接方式并不等同于业务流程的完整性。 在 AGENTS md 的 Agent 阶段，修改代码之前需明确输入参数、该步骤的负责人以及终止条件。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 相较于庞大的脚本，应优先使用小型且可测试的单元。当某个步骤出现故障时，故障原因应能明确指向单一责任主体，而非复杂的流程链。
SKILL.md — 模块化专长
在处理SKILL md的模块化专长阶段时，首先需明确相关约定：所需输入、成功标志以及部分失败时的处理方式。这样的检查清单能确保后续的代码修改保持一致性。 将此阶段视为输入与验证后输出之间的契约。为相关成果命名，定义成功判定标准，并杜绝无声的半完成状态。 在成本较高的步骤之后设置检查点。当操作员重新尝试后续节点时，恢复流程不应再次调用相同的LLM接口。
---
name: data-cleaner
description: Use when the user needs to clean, normalize, or validate tabular data files.
---
# Data Cleaner
## When to Use
- User provides CSV, Excel, or JSON files that need cleaning
- Data has missing values, inconsistent formatting, or duplicate rows
## Steps
1. Load the data file using pandas
2. Profile the data: count nulls, duplicates, and type mismatches
3. Apply cleaning rules (fill nulls, normalize strings, deduplicate)
4. Save the cleaned output and generate a summary report
持久化智能体创建
在处理“持久化智能体创建”阶段时，首先需记录下相关契约：所需的输入参数、成功信号以及部分失败时的处理方式。这样的检查清单能确保后续的代码修改保持一致性。 在功能结果旁记录执行时间以及令牌或查询成本。提前了解成本情况，可避免在从演示环境过渡到共享环境时出现意外费用。 在耗时较高的步骤之后设置检查点。当操作员重新尝试后续节点时，恢复流程不应再次收取相同的LLM调用费用。
from google import genai
client = genai.Client()
# Register a reusable, named agent with the data-cleaner skill baked in
agent = client.agents.create(
id="my-csv-cleaner",
base_agent="antigravity-preview-05-2026",
system_instruction="You are a data quality engineer. Always use pandas for data manipulation. "
"Always generate a before/after summary showing what changed.",
base_environment={
"type": "remote",
"sources": [
{
"type": "inline",
"target": ".agents/AGENTS.md",
"content": (
"# Data Quality Agent\n\n"
"## Standards\n"
"- Never drop rows silently — log every removal with a reason\n"
"- Normalize all string columns to lowercase, stripped of whitespace\n"
"- Output cleaned files in UTF-8 CSV format\n"
"- Always print a summary table at the end\n"
)
},
{
"type": "inline",
"target": ".agents/skills/data-cleaner/SKILL.md",
"content": (
"---\n"
"name: data-cleaner\n"
"description: Use when the user needs to clean, normalize, or validate tabular data files.\n"
"---\n"
"# Data Cleaner\n\n"
"## When to Use\n"
"- User provides CSV, Excel, or JSON files that need cleaning\n"
"- Data has missing values, inconsistent formatting, or duplicate rows\n\n"
"## Steps\n"
"1. Load the data file using pandas\n"
"2. Profile the data: count nulls, duplicates, and type mismatches\n"
"3. Apply cleaning rules (fill nulls, normalize strings, deduplicate)\n"
"4. Save the cleaned output and generate a summary report\n"
)
}
]
}
)
print(f"Agent created with ID: {agent.id}")
name,email,phone,country
John Smith,john@example.com,+14155551234,US
jane doe, JANE@EXAMPLE.COM ,4155559999,
John Smith,john@example.com,+14155551234,US
Bob Wilson,bob@@invalid,+442071234567,
María García,maria@test.com,+34612345678,Spain
alice brown ,alice@example.com,,US
Charlie Lee,charlie@test.com,+61412345678,AU
jane doe,jane@example.com,+14155559999,
from google import genai
client = genai.Client()
# Read your local CSV file
with open("messy_customers.csv", "r") as f:
csv_content = f.read()
# Inject the file into the sandbox and tell the agent to clean it
interaction = client.interactions.create(
agent="my-csv-cleaner",
input="Clean the file at data/customers.csv. "
"Remove duplicate rows by email (case-insensitive), "
"standardize phone numbers to E.164 format, "
"fill missing 'country' fields by looking up the phone country code, "
"and flag any rows where the email format is invalid. "
"Generate a before/after summary showing what changed.",
environment={
"type": "remote",
"sources": [
{
"type": "inline",
"target": "data/customers.csv", # ← Path inside the sandbox
"content": csv_content # ← Your local file contents
}
]
}
)
print(interaction.output_text)
I have successfully cleaned the customer dataset at `data/customers.csv` using Python and `pandas`.
Below is a detailed report and before/after summary of the transformations applied.
---
### Summary of Transformations
1. **whitespace Trimming:** Stripped all leading and trailing whitespace from both column names and text values (e.g., ` alice brown ` became `alice brown`).
2. **Email Normalization & Validation:**
- Converted all email addresses to lowercase and trimmed whitespace.
- Added a new column `email_invalid` (boolean) to flag emails with an invalid format.
- Identified `bob@@invalid` as an invalid format (due to a duplicate `@` symbol and lack of a top-level domain).
3. **Phone Number Standardization:**
- Standardized all valid phone numbers to the international **E.164** format using the `phonenumbers` library.
- Converted `4155559999` (with a default US region fallback) to `+14155559999`.
- Ignored the missing phone number for `alice brown` while leaving it empty.
4. **Country Code Lookup:**
- Looked up and populated missing `country` values based on the telephone country code prefix.
- Filled `jane doe`'s country as `US` (derived from prefix `+1`).
- Filled `Bob Wilson`'s country as `GB` (derived from prefix `+44`).
- Kept existing valid country entries (e.g., `Spain`, `US`, `AU`) intact.
5. **Deduplication:**
- Removed duplicate rows by performing a case-insensitive match on the cleaned `email` column, keeping the first occurrence.
- Safely removed the duplicate row for `john@example.com` (Row 3).
- Safely removed the duplicate row for `jane@example.com` (Row 8).
---
### Before/After Summary
#### 1. Data Quality Metrics
| Metric | Before Clean | After Clean | Change Description |
| :--- | :---: | :---: | :--- |
| **Total Rows** | 8 | 6 | 2 duplicate rows were removed. |
| **Duplicates Removed** | 0 | 2 | Dropped rows 3 (`john@example.com`) and 8 (`jane@example.com`). |
| **Invalid Emails Flagged** | 0 | 1 | Flagged `bob@@invalid` as invalid (`email_invalid = True`). |
| **Standardized Phone Numbers** | 1 | 5 | All numbers formatted to E.164 (e.g., `4155559999` -> `+14155559999`). |
| **Filled Country Fields** | 5 | 6 | Filled 2 missing country values (`US` and `GB`) via phone prefix lookups. |
#### 2. Row-by-Row Comparison
Below is the row-by-row evolution from the raw file to the cleaned output:
| Row # | Name (Before) | Name (After) | Email (Before) | Email (After) | Phone (Before) | Phone (After) | Country (Before) | Country (After) | Email Invalid Flag | Action Taken / Status |
| :---: | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :---: | :--- |
| **1** | `John Smith` | `John Smith` | `john@example.com` | `john@example.com` | `+14155551234` | `+14155551234` | `US` | `US` | `False` | Retained as first unique instance. |
| **2** | `jane doe` | `jane doe` | ` JANE@EXAMPLE.COM ` | `jane@example.com` | `4155559999` | `+14155559999` | *Missing* | `US` | `False` | Email normalized; Phone formatted to E.164; Country populated from `+1` prefix. |
| **3** | `John Smith` | — | `john@example.com` | — | `+14155551234` | — | `US` | — | — | **Removed** (Duplicate email). |
| **4** | `Bob Wilson` | `Bob Wilson` | `bob@@invalid` | `bob@@invalid` | `+442071234567` | `+442071234567` | *Missing* | `GB` | `True` | Country populated from `+44` prefix; Email flagged as invalid. |
| **5** | `María García` | `María García` | `maria@test.com` | `maria@test.com` | `+34612345678` | `+34612345678` | `Spain` | `Spain` | `False` | Retained; Email valid; Preserved existing country. |
| **6** | ` alice brown ` | `alice brown` | `alice@example.com` | `alice@example.com` | *Missing* | *Missing* | `US` | `US` | `False` | Name whitespace stripped; Phone left empty; Preserved existing country. |
| **7** | `Charlie Lee` | `Charlie Lee` | `charlie@test.com` | `charlie@test.com` | `+61412345678` | `+61412345678` | `AU` | `AU` | `False` | Retained; Email valid. |
| **8** | `jane doe` | — | `jane@example.com` | — | `+14155559999` | — | *Missing* | — | — | **Removed** (Duplicate email). |
---
### Cleaned Dataset Output
The cleaned data has been written back to `data/customers.csv` and contains the following records:
```csv
name,email,phone,country,email_invalid
John Smith,john@example.com,+14155551234,US,False
jane doe,jane@example.com,+14155559999,US,False
Bob Wilson,bob@@invalid,+442071234567,GB,True
María García,maria@test.com,+34612345678,Spain,False
alice brown,alice@example.com,,US,False
Charlie Lee,charlie@test.com,+61412345678,AU,False
```
构建现实世界中的智能体：若干应用场景
在完成“构建现实世界智能体”A阶段时，首先需明确相关规范：所需输入、成功标志以及部分失败时的处理方式。这样的清单能确保后续的代码修改保持一致性。 将配置信息置于应用程序代码之外。环境文件、密钥存储以及功能开关应集中存放于一个位置，以便操作员无需查看整个系统结构即可进行审计。 在耗时较高的步骤之后设置检查点。当操作员重新尝试后续节点时，恢复流程不应再次调用相同的大型语言模型。 在完成“构建现实世界智能体”A阶段时，首先需明确相关规范：所需输入、成功标志以及部分失败时的处理方式。这样的清单能确保后续的代码修改保持一致性。 相较于庞大的脚本，应优先使用小型且可测试的单元。当某个步骤失败时，故障原因应能指向单一责任模块，而非复杂的流程链。
用例1：自动代码重构与测试修复
在将用例1的自动化阶段视为可度量的工作面时，效果最佳。在扩大范围之前，先记录一份标准测试用例、一个故障案例以及回滚说明。 将此阶段视为输入与已验证输出之间的契约。为相关成果命名，明确成功标准，杜绝默许的半完成状态。 保持图结构简洁且类型明确。嵌套的数据块会掩盖哪个节点修改了哪个字段，还会在中断后导致流程无法继续。
需为智能体修复的已知漏洞
将需要修复的已知漏洞视为可度量的对象来处理，效果最佳。在扩大范围之前，先记录一份完美的测试用例、一个故障案例以及回滚说明。在功能结果旁同时记录执行时间以及令牌或查询成本。提前了解这些成本，就能避免在从演示环境过渡到共享环境时出现意外费用。要保持图表状态简洁且类型明确，嵌套的数据块会掩盖是哪个节点修改了哪个字段，还会在出现中断后导致流程无法继续。
代码
将“The Code”阶段视为可度量的对象来管理效果最佳。在扩大范围之前，先记录一份完美的操作日志、一个故障案例以及回滚说明。 应将配置与应用程序代码分开。环境文件、密钥存储和功能开关应集中存放于一处，以便操作人员无需查看整个系统结构即可进行审计。 要保持系统状态的简洁性与类型化。嵌套的数据结构会掩盖是哪个节点修改了哪个字段，还会导致在出现中断后无法继续执行。 将“The Code”阶段视为可度量的对象来管理效果最佳。在扩大范围之前，先记录一份完美的操作日志、一个故障案例以及回滚说明。 相比庞大的脚本，更应采用小型且可测试的单元。当某个步骤失败时，故障应指向单一的责任模块，而非复杂的流程链。
from google import genai
client = genai.Client()
system_instructions = """
You are an expert QA and Refactoring Engineer. Your workflow is:
1. Clone the target repository into the workspace
2. Install all dependencies from requirements.txt
3. Run the full pytest suite and capture all output
4. For each failing test:
a. Read the error traceback carefully
b. Open the source file causing the failure
c. Diagnose the root cause
d. Apply the minimal fix
5. Re-run pytest after each fix to confirm resolution
6. Continue until ALL tests pass (0 failures)
7. Generate a summary of every change you made
IMPORTANT: Make the minimal possible change to fix each test.
Do not refactor working code unnecessarily.
"""
interaction = client.interactions.create(
agent="antigravity-preview-05-2026",
system_instruction=system_instructions,
input="""
Clone this repository: https://github.com/rominirani/uc1-legacy-app
Run the pytest suite to check for environment or code failures.
Isolate the bugs causing test failures and fix them one by one.
Keep running the tests until you achieve a 100% pass rate.
When done, provide:
1. A list of all files modified with a diff summary
2. The final pytest output showing all tests passing
3. Any dependency changes you had to make
""",
environment="remote"
)
print(interaction.output_text)
python3 uc1_refactor.py
I have successfully diagnosed and resolved all the issues causing test failures in the `uc1-legacy-app` repository. Below is a detailed summary of the findings, modifications, and the final test execution output.
---
### 1. Modified Files & Diff Summary
#### File: `src/models.py`
- **Issue**: In the `Invoice.summary()` method, the `self.total` method itself was passed to `format_currency()` as an object, instead of being invoked (`self.total()`). This led to a `TypeError` during format evaluation.
- **Fix**: Changed `self.total` to `self.total()`.
- **Diff Summary**:
```diff
@@ -23,7 +23,7 @@ class Invoice:
"""Return a one-line summary of the invoice."""
from src.utils import format_currency, normalize_name
name = normalize_name(self.customer_name)
- return f"Invoice {self.invoice_id}: {name} - {format_currency(self.total)}"
+ return f"Invoice {self.invoice_id}: {name} - {format_currency(self.total())}"
```
#### File: `src/utils.py`
- **Issues**:
1. **`calculate_total()`**: When `tax_rate` is received as a string, Python fails to perform multiplication of float/string (`TypeError: can't multiply sequence by non-int of type 'float'`).
2. **`normalize_name()`**: When passed a `None` value (common in CSV imports), the code raised `AttributeError: 'NoneType' object has no attribute 'strip'`.
- **Fixes**:
1. Explicitly cast `subtotal` and `tax_rate` to `float` prior to calculating the tax.
2. Add a safeguard to return an empty string (`""`) when `name` is `None`.
- **Diff Summary**:
```diff
@@ -11,8 +11,10 @@ def calculate_total(subtotal, tax_rate):
Returns:
The total as a float rounded to 2 decimal places.
"""
- tax = subtotal * tax_rate / 100
- return round(subtotal + tax, 2)
+ subtotal_val = float(subtotal)
+ tax_rate_val = float(tax_rate)
+ tax = subtotal_val * tax_rate_val / 100
+ return round(subtotal_val + tax, 2)
def format_currency(amount):
@@ -36,6 +38,8 @@ def normalize_name(name):
Returns:
Cleaned, title-cased name.
"""
+ if name is None:
+ return ""
return name.strip().title()
```
---
### 2. Dependency Changes
No additional packages or dependency changes were made. All required libraries from `requirements.txt` (consisting of `pytest>=7.0.0`) were successfully installed and utilized without modification.
---
### 3. Final Pytest Output
Running the full suite confirms that all 10 tests now pass:
```
============================= test session starts ==============================
platform linux -- Python 3.12.3, pytest-9.0.3, pluggy-1.6.0
rootdir: /uc1-legacy-app
plugins: anyio-4.13.0
collected 10 items
tests/test_models.py .... [ 40% ]
tests/test_utils.py ...... [100% ]
============================== 10 passed in 0.02s ==============================
```
幕后发生了什么
对于“幕后发生什么”这一阶段，应在修改代码之前明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 将这一阶段视为输入与经过验证的输出之间的契约。为相关成果命名，定义成功判定标准，并拒绝默许的半完成状态。 对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的连接方式并不等同于业务上的完整性。
用例2：实时竞争情报引擎
对于用例2的实时阶段，在修改代码之前需明确输入参数、各步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。应在功能结果旁记录执行时间以及令牌或查询成本。提前了解成本情况，可避免在从演示环境过渡到共享环境时出现意外费用。对于会产生支出或修改生产数据的操作，必须经过人工审批。仅靠编译时的配置并不足以确保业务的完整性。
代码
在“代码编写”阶段，应在修改代码之前明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 配置信息应置于应用程序代码之外。环境文件、密钥存储以及功能开关应集中存放，以便操作人员无需查看整个系统结构即可进行审核。 对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的连接方式并不等同于业务功能的完整性。
from google import genai
client = genai.Client()
system_instructions = """
You are a precise Business Intelligence Analyst. Your workflow:
1. Use web search to find current, real-time information
2. Navigate to official product/pricing pages for each competitor
3. Extract: pricing tiers, key features, target audience, notable limitations
4. Structure ALL findings into a pandas DataFrame
5. Export to both CSV and a formatted Markdown comparison table
6. Include the date/time of research and source URLs for every data point
Rules:
- Only report data you can verify from official sources
- If pricing isn't publicly available, note "Contact Sales" - don't guess
- Use USD for all pricing normalization
"""
interaction = client.interactions.create(
agent="antigravity-preview-05-2026",
system_instruction=system_instructions,
input="""
Research the pricing models and core feature matrices of the top 3 alternative
platforms to Vercel for frontend deployment:
1. Netlify
2. Cloudflare Pages
3. Cloud Run
For each, extract:
- Free tier limits (bandwidth, builds, sites)
- Pro/paid tier pricing and what it unlocks
- Key differentiating features
- Notable limitations or complaints from developer communities
Create:
1. A competitive_matrix.csv spreadsheet with all data
2. A competitive_analysis.md report with a formatted comparison table
and a "Recommendation" section at the bottom
""",
environment="remote"
)
print(interaction.output_text)
在“代码编写”阶段，应在修改代码之前明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。相比冗长的脚本，更应采用小型、可测试的单元。当某个步骤失败时，故障原因应能明确指向某个具体的责任模块，而非整个复杂的流程。
As a Business Intelligence Analyst, I have conducted a precise competitive analysis of the top three frontend deployment alternatives to Vercel: **Netlify**, **Cloudflare Pages**, and **Google Cloud Run**.
All findings have been structured using `pandas` and exported to `/` (the current working directory) as:
1. `competitive_matrix.csv` — The raw, structured dataset.
2. `competitive_analysis.md` — A comprehensive, reader-ready report including a detailed markdown comparison matrix, architectural breakdowns, limitation analyses, and a tailored recommendation framework.
All data has been verified against official vendor pricing, product, and developer documentation as of **June 9, 2026**. All currency values are normalized in **USD**.
---
### Core Finding Highlights
#### 1. Netlify
* **The Architectural Shift (April 2026):** Netlify made a massive strategic update to its Pro plans, moving from a seat-based model ($19/member/month) to a **flat $20/month per organization** [10]. This includes unlimited team members (Owners, Developers, Reviewers, and Git Contributors) [10], which is highly disruptive compared to Vercel's strict per-seat billing.
* **The Credits Trap:** Usage is now managed entirely via a unified credit-based model (300 credits/mo free, 3,000 on Pro) [2, 10]. However, credits are consumed fast: bandwidth costs 20 credits per GB [2, 10] (meaning Free tier is only **~15 GB max** and Pro is only **~150 GB max** if solely used for traffic). If Free tier credits run out, **all site traffic pauses immediately** (no auto-recharge is supported on Free) [2, 11].
* **Capabilities:** Highly integrated features like Netlify Database (managed Postgres via Neon) [4], Blob storage, Forms, and Auth [10]. Serverless timeout is 10s on Free/Personal [2] and 26s on Pro [2].
#### 2. Cloudflare Pages
* **Unmetered Freedom:** Genuinely **unlimited and unmetered static bandwidth and requests** across all tiers (including the $0 Free tier) [24, 25, 27].
* **The Pro Tier ($20/mo billed annually / $25/mo monthly):** Tied directly to Cloudflare's Workers Paid plan [25, 27]. It increases build limits from 500/mo to 5,000/mo, concurrent builds from 1 to 5, and the file-count ceiling per project from 20,000 to 100,000 assets [31] (unlocked via the `PAGES_WRANGLER_MAJOR_VERSION=4` env variable) [31].
* **Edge Isolates Constraints:** While running serverless code on Cloudflare's global network of 310+ cities is blazingly fast with zero cold starts [24, 33], it uses a **strict V8 edge runtime** [24]. Standard Node.js library modules (`fs`, `path`, native `crypto`) do not run natively [24], forcing developers to compile meta-frameworks like Next.js via edge adapters (e.g., `@cloudflare/next-on-pages` or OpenNext) [24] which restricts certain features like Incremental Static Regeneration (ISR).
#### 3. Google Cloud Run
* **Pure Container Portability:** Cloud Run runs any application, language, or compiled binary (Node.js, Python, Go, Rust) packaged in a standard Docker container [37, 39]. It runs full Node.js natively [39], ensuring 100% Next.js feature parity (ISR, Server Actions, dynamic image optimization) with zero edge-runtime workarounds [39].
* **No Monthly Base Fee:** Operates on a pure pay-as-you-go serverless model (billed in granular vCPU-seconds and GiB-seconds) [41], backed by a generous Always Free tier (2 million requests/mo, 180,000 vCPU-seconds, 360,000 GiB-seconds, and 120 daily Cloud Build minutes free) [37, 38, 41].
* **The Infrastructure Overhead:** Not a standard "push-to-deploy" platform. Developers must write Dockerfiles, configure Artifact Registry, and manage IAM and networking [37, 39]. It is regionally hosted; to get a global CDN with SSL mapping, you must set up Cloud CDN and a Global HTTP Load Balancer [37] (~$20-$30/mo base fee) and navigate container cold starts (2 to 10+ seconds) [37] and expensive internet egress rates ($0.085 - $0.12 per GB) [37].
---
### Structure of Generated Files
#### 1. `competitive_matrix.csv`
A raw, tabular CSV structured for business intelligence pipelines, featuring the following schema:
- `Platform Name`
- `Free Tier Pricing (USD)`
- `Free Tier Bandwidth Limit`
- `Free Tier Build Limit`
- `Free Tier Sites/Projects Limit`
- `Free Tier Key Specs & Features`
- `Paid/Pro Tier Price (USD)`
- `Paid/Pro Tier Features Unlocked`
- `Key Differentiating Features`
- `Notable Limitations & Developer Complaints`
- `Date of Research`
- `Source URLs`
#### 2. `competitive_analysis.md`
A professionally formatted report including:
* **Executive Summary:** Contextualizing why frontend teams are exploring alternatives to Vercel in 2026.
* **Structured Comparison Table:** Transposed for high-density reading and side-by-side comparison.
* **Deep-Dive Section:** Dedicated analyses of the Free limits, paid features, unique differentiators, and community complaints for Netlify, Cloudflare Pages, and Google Cloud Run.
* **Architectural Recommendation Flowchart & Matrix:** Tailored decision pathways based on project profile:
* *Static / JAMstack / Zero-Budget* $\rightarrow$ **Cloudflare Pages**
* *Collaborative Teams / Rapid Prototyping / Seat-Free DX* $\rightarrow$ **Netlify**
* *Complex Server-Side Rendering / Backend Pipelines / Custom Docker* $\rightarrow$ **Google Cloud Run**
---
### Source Verification & Citations
1. **Netlify:**
- [Official Pricing & Plans](https://www.netlify.com/pricing/)
- [Introducing Seat-Free Collaborative Billing (April 14, 2026)](https://www.netlify.com/blog/pricing-netlify-for-3-billion-builders/)
- [Credit-Based Pricing Documentation](https://docs.netlify.com/manage/accounts-and-billing/billing/billing-for-credit-based-plans/credit-based-pricing-plans/)
- [Netlify Free Tier Credit Analysis (Temps)](https://temps.sh/compare/vs-netlify)
2. **Cloudflare Pages:**
- [Workers & Pages Pricing Matrix](https://www.cloudflare.com/plans/developer-platform/)
- [Official Pages Limits Documentation](https://developers.cloudflare.com/pages/platform/limits/)
- [Cloudflare Pages Platform Features](https://pages.cloudflare.com/)
- [Cloudflare Pages Edge Limitations (Temps)](https://temps.sh/compare/vs-cloudflare-pages)
3. **Google Cloud Run:**
- [Official Cloud Run Pricing Breakdown](https://cloud.google.com/run/pricing)
- [Google Cloud Free Tier Inclusions](https://cloud.google.com/free)
- [Cloud Run Quotas & Limits](https://docs.cloud.google.com/run/quotas)
- [Cloud Run Cost Optimization & Egress Guide (Cloudchipr)](https://cloudchipr.com/blog/cloud-run-pricing)
The generated files are saved directly in your working directory and are immediately available for download or integration into your reporting pipelines. Let me know if you would like me to modify the analysis parameters or explore a specific platform further!
基于分析的结果进行多轮迭代
在“基于分析进行构建”阶段工作时，首先写下相关契约：所需的输入、成功信号以及部分失败时会发生什么。这样的检查清单能确保后续的代码修改保持一致性。 将此阶段视为输入与经过验证的输出之间的契约。为相关成果命名，明确成功判定标准，杜绝无声的半完成状态。 在成本较高的步骤之后设置检查点。当操作员重新尝试后续节点时，恢复流程不应再次调用相同的大型语言模型。
# Follow-up: generate a visual chart from the data we just collected
interaction_2 = client.interactions.create(
agent="antigravity-preview-05-2026",
environment=interaction.environment_id,
previous_interaction_id=interaction.id,
input="""
Using the competitive_matrix.csv you just created:
1. Create a grouped bar chart comparing the free tier limits across all 3 platforms
2. Create a pricing comparison chart for the paid tiers
3. Save both charts as PNG files with clear labels and a professional color scheme
4. Add the charts as embedded images in the competitive_analysis.md report
"""
)
print(interaction_2.output_text)
更多应用场景
在处理“更多用例”阶段时，首先需写下相关契约：所需输入、成功信号以及部分失败时的处理方式。这样的清单能确保后续的代码修改保持一致性。 在功能结果旁记录执行时间以及令牌或查询成本。提前了解成本情况，可避免在从演示环境过渡到共享环境时出现意外费用。 在耗时较高的步骤之后设置检查点。当操作员重新尝试后续节点时，恢复流程不应再次收取相同的LLM调用费用。
最佳实践
在遵循最佳实践阶段时，首先需明确相关规范：所需的输入参数、成功标志以及部分失败时的处理方式。这样的清单能确保后续的代码修改保持透明可追溯。 应将配置信息与应用程序代码分开存放。环境文件、密钥存储以及功能开关应集中于一处，以便操作人员无需查看整个系统结构即可进行审计。 在耗时较高的步骤之后设置检查点。当操作人员重新执行后续节点时，恢复流程不应再次调用相同的大型语言模型接口。 在遵循最佳实践阶段时，首先需明确相关规范：所需的输入参数、成功标志以及部分失败时的处理方式。这样的清单能确保后续的代码修改保持透明可追溯。 相较于庞大的脚本，更应优先使用小型且易于测试的单元。当某个步骤失败时，故障原因应能明确指向某个具体的功能模块，而非整个复杂的流程。
构建即删除
将“待删除构建”阶段视为可度量的对象使用效果最佳。在扩大范围之前，先记录一份完美的测试结果、一个故障案例以及回滚说明。 把这一阶段视为输入与已验证输出之间的契约。为相关成果命名，明确成功标准，绝不允许出现无声的半完成状态。 保持图结构的状态简洁且类型明确。嵌套的数据块会掩盖是哪个节点修改了哪个字段，还会在中断后导致无法继续处理。
将智能体视为微服务
将代理视为微服务的方法在将其作为可度量的对象来处理时效果最佳。在扩大范围之前，先记录一份完美的操作日志、一个故障案例以及回滚说明。 在功能结果旁同时记录执行时间以及令牌或查询成本。提前了解成本情况，可避免在从演示环境过渡到共享环境时出现意外账单。 保持图结构的状态简洁且具有类型定义。嵌套的数据块会掩盖是哪个节点修改了哪个字段，且在中断后会导致状态无法恢复。
错误作为输入，而非崩溃
将“错误作为输入”阶段视为可度量的对象来处理效果最佳。在扩大范围之前，先记录一份标准日志、一个故障案例以及回滚说明。 应将配置与应用程序代码分开。环境文件、密钥存储和功能标志应集中存放于一处，以便操作人员无需查看整个架构就能进行审计。 要保持架构状态的简洁性与类型化。嵌套的数据结构会掩盖哪个节点修改了哪个字段的信息，还会导致中断后无法继续执行。 将“错误作为输入”阶段视为可度量的对象来处理效果最佳。在扩大范围之前，先记录一份标准日志、一个故障案例以及回滚说明。 相比庞大的脚本，更应优先使用小型且可测试的单元。当某个步骤失败时，故障应指向单一的责任模块，而非复杂的流程链。
评估优于单元测试
在“单元测试后的评估”阶段，应在修改代码之前明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 将此阶段视为输入与验证后输出之间的契约。为相关成果命名，定义成功判定标准，杜绝默许的半完成状态。 对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的配置并不等同于业务上的完整性。
# ❌ Don't: Assert exact output
assert agent_output == "The answer is 42"
# ✅ Do: Evaluate behavioral success rate
results = [run_agent(task) for _ in range(20)]
success_rate = sum(1 for r in results if r.meets_criteria) / len(results)
assert success_rate >= 0.85 # Agent succeeds at least 85% of the time
代币成本意识
在令牌成本意识阶段，应在修改代码之前明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。应在功能结果旁记录执行时间以及令牌或查询成本。提前了解成本情况，可避免在从演示环境切换到共享环境时出现意外账单。当下一步操作是编写代码或调用工具时，优先选择具有架构验证的结构化输出，而非自由形式的文字描述。
错误处理模式
在错误处理模式阶段，应在修改代码之前明确输入参数、该步骤的负责人以及终止标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 配置信息应置于应用程序代码之外。环境文件、密钥存储和功能标志应集中存放于一个位置，以便操作人员无需查看整个系统结构即可进行审计。 对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的连接方式并不等同于业务功能的完整性。
托管代理的内部工作原理
在“托管代理如何运作”阶段，应在修改代码之前明确输入参数、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 需同时记录正常流程和故障恢复流程。重试机制、人工审核环节以及错误处理都是产品本身的组成部分，而非后续需要补充的内容。 对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的配置并不等同于业务功能的完整性。
资源
在资源准备阶段，应在修改代码之前明确输入内容、该步骤的负责人以及结束标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。相比冗长的脚本，更应采用小型且可测试的单元。当某个步骤失败时，故障原因应能指向单一责任点，而非复杂的流程链。对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的逻辑连接并不等同于业务功能的完整性。
下一个智能体的设计思路
在规划下一阶段时，应在修改代码之前明确输入内容、该步骤的负责人以及完成标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。 将这一阶段视为输入与经过验证的输出之间的契约。为相关成果命名，定义成功检测标准，并拒绝默许的半完成状态。 对于涉及资金支出或修改生产数据的操作，必须经过人工审批。编译时的连接方式并不等同于业务上的完整性。
最后思考
在“最终思考”阶段，应在修改代码之前明确输入参数、该步骤的负责人以及终止标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。除了功能结果外，还需记录执行时间以及令牌或查询成本。提前了解这些成本可以避免在从演示环境过渡到共享环境时出现意外费用。对于那些会消耗资金或修改生产数据的操作，必须经过人工审批。仅靠编译时的配置并不足以确保业务的完整性。
操作检查清单
在“操作检查清单”阶段，同样需要在修改代码之前明确输入参数、该步骤的负责人以及终止标准。操作人员应能够从已知的检查点重新运行该步骤，而无需猜测隐藏状态。
应将正常流程和恢复流程一并记录下来。重试机制、人工审核环节以及死信处理都是产品不可或缺的部分，而非后续需要补充的内容。
对于涉及资金支出或修改生产数据的操作，必须经过人工审批。仅靠编译时的配置并不能保证业务的完整性。
编写简短的操作手册：说明如何轮换密钥、如何清空队列以及如何回滚上一次的数据导入操作。
优先选择小型且可测试的单元，而非庞大的脚本。当某个步骤出现故障时，故障应能指向具体的责任模块，而非复杂的流程链。
对于涉及资金支出或修改生产数据的操作，必须经过人工审批。仅靠编译时的配置并不能保证业务的完整性。
在推广该技术栈之前，应先冻结版本，为关键流程记录标准输出日志，并明确回滚步骤。共享环境需要设置速率限制、租户验证机制，以及负责密钥轮换的明确责任人。与其展示花哨的一次性演示，不如注重扎实的可靠性。
b5917844932b的批处理说明：不要将提供商密钥放入代码仓库，为每个会话设置令牌使用上限，并将日志存储在评估用示例文件旁边，以便后续模型更换时保持数据可比性。