Evaluating AI Coding Tools in 2026: Building an E-commerce Site from Scratch

I. Background and Evaluation Objectives

By 2026, AI coding tools have transitioned from “code completion plugins” to “task-level agents.” Developer expectations for these tools are no longer limited to single-file function generation. Instead, there is a demand for them to understand complete requirements, plan project structures, generate runnable pages and logic, and autonomously fix errors when they occur. Centered around this trend, several product paradigms have emerged in the industry: AI IDEs deeply integrated into editors, conversation and agent-centric coding assistants, and autonomous programming agents aimed at complete task automation.

This article does not reiterate the feature lists of specific commercial products. Instead, it abstracts a universal problem: When developers need to use AI tools to build a front-end site with complete business processes from scratch in an environment without external network dependencies, what are the true capability boundaries of these tools? To answer this, we designed a unified evaluation task, prompt, and acceptance criteria to horizontally observe four representative paradigms of “intelligent coding tools” and provide a reusable selection methodology.

II. Evaluation Task and Acceptance Criteria Design

2.1 Unified Task Definition

The evaluation task is to build a front-end shopping site from scratch using the React tech stack, covering 10 pages:

1. Home Page (Product List)
2. Product Detail Page
3. Product Purchase Page
4. Purchase Success Page
5. User Login Page
6. User Registration Page
7. User Dashboard
8. User Orders Page
9. User Wallet (Coupons)
10. User Settings Page

The unified prompt is: “Use React to build a front-end shopping site including the above 10 pages, ensuring the completeness and overall consistency of the site.”

2.2 Core Challenges and Acceptance Dimensions

The core challenge of this task lies not in individual pages, but in site completeness and overall consistency: whether the routing structure is closed, whether page transitions are coherent, whether state management is unified, whether the styling system is self-consistent, and whether mock data is decoupled from the views. Based on these factors, we established six acceptance dimensions:

• Task Comprehension: Whether all 10 pages are covered in one go, with no omissions or fabricated pages.
• Runnability: Whether the project can be started directly after the first generation, the number of errors, and the cost of fixing them.
• Project Structure: Whether the directory division is reasonable, and if structural chaos like nested directories with the same name exists.
• Code Quality: Whether styles and logic are separated, whether mock data is decoupled, and whether extension points like hooks/context are reserved.
• Interaction Consistency: Whether form validations, button visibility, and responsive adaptations are properly handled.
• Generation Efficiency: Response speed, whether queuing or paid acceleration is required, and whether multiple rounds of fixes are needed.

The following sections detail the observations of the four capability paradigms. To maintain neutrality, they are referred to as: Paradigm A (Integrated AI IDE, Conversation + Craft Mode), Paradigm B (Inherited AI IDE, Builder Mode), Paradigm C (Multi-Agent Coding Assistant), and Paradigm D (Autonomous Task Agent).

III. Practical Observations of Four Paradigms

3.1 Paradigm A: Integrated AI IDE (Conversation + Craft Mode)

User Experience: This paradigm features one of the highest levels of interface friendliness among the four. The operation path is clear, avoiding the “cold” feel of traditional IDEs. Its Craft mode can be understood as a “task-level automated execution” mode—after the user provides the goal, the tool plans the steps and executes them continuously, rather than engaging in step-by-step Q&A.

Task Completion: Under the unified prompt, Craft mode was able to generate all 10 pages in one go. The shopping process was complete, including basic form validation. The server response speed was fast, and the generation process had no noticeable lag.

Main Issues:

• The finished UI leans towards a mobile style. Although simple adaptive design was applied, the login/registration pages became distorted on wide screens.
• Image assets were entirely placeholders, and the information density on the product detail page was low.
• The project architecture was overly simple. Mock data was directly coupled within the business code, with no data layer abstraction.

Code Quality: Styles and JS were basically separated, ensuring readability. However, it lacked a data mock layer and unified state management, requiring a round of refactoring for future expansion.

Summary: Suitable for scenarios requiring a “quickly runnable prototype,” where a single generation can demonstrate the complete process. However, manual effort is needed to supplement the data layer and responsive details for better engineering quality.

3.2 Paradigm B: Inherited AI IDE (Builder Mode)

User Experience: The operation logic follows the interaction paradigm of mainstream AI IDEs and provides a privacy mode, suitable for scenarios sensitive to code exfiltration. Its Builder mode is also a task-level automated execution, but the execution chain is longer compared to Craft mode.

Task Completion: The initial generated output could start but contained errors, requiring multiple rounds of fixes to see the complete result. The advantage is that form validation was relatively complete, and the information volume on the product detail page was higher than that of Paradigm A.

Main Issues:

• The overall UI was rudimentary and not responsive on mobile devices.
• “White text on a white background” buttons appeared, rendering text invisible—a typical style contrast defect.
• Images were still placeholders, and the color scheme lacked a unified design specification.
• The workload for later modifications was significant.

Code Quality: Styles were separated but not into independent files. The directory reserved places for hooks and context, but they were empty—meaning “structural reservation without implementation,” representing a semi-finished architecture.

Generation Efficiency: The development speed was slow, prompting users to desire “paid acceleration.” Currently, this paradigm only offers a free version, with model capabilities supported by its own system.

Summary: It possesses good architectural awareness and reserves extension points, but the implementation is incomplete and style defects are obvious. It is suitable for teams willing to invest in manual fixes and who value directory structure standards.

3.3 Paradigm C: Multi-Agent Coding Assistant

User Experience: It offers multiple Agents to choose from, leading to a high selection cost for first-time users—they often do not know which Agent fits the current task.

Mode Differences:

• Quick Mode: Fast generation speed, but the project structure was chaotic, featuring a nesting issue of “creating a folder with the same name inside a folder,” which ultimately prevented the project from starting. This is a typical path resolution and engineering scaffolding defect.
• Architecture Mode: Claimed to handle complex tasks, but the server response was slow. The generated project could run, but had many errors. Multiple attempts to fix them failed, leading to abandonment.

Summary: The multi-Agent design is conceptually sound (quick tasks use Quick Mode, complex tasks use Architecture Mode). However, it lacks guidance on matching modes to tasks, and both modes exposed issues of “structural chaos” and “insufficient repair capability” respectively. It is suitable as an auxiliary completion tool rather than the sole dependency for building a site from scratch.

3.4 Paradigm D: Autonomous Task Agent

This paradigm advertises “end-to-end completion of development tasks,” typically accepting natural language task descriptions to autonomously plan, code, and debug. Under the unified task of this article, its performance depended on two factors: the planning capability for the completeness of the 10 pages, and the autonomous repair loop after errors. In practice, this type of tool performed well in task decomposition, but still required manual verification for cross-file consistency (such as aligning routing with page naming and unifying state management). Since such tools iterate rapidly, it is recommended to use “whether it supports task interruption and recovery” and “whether it outputs reviewable change sets” as key judgment indicators during selection.

IV. Horizontal Comparison and Selection Dimensions

Dimension	Paradigm A	Paradigm B	Paradigm C	Paradigm D
One-time Task Completion	High (10 pages covered)	Medium (needs multiple fixes)	Low-Medium (large mode variance)	Medium-High (depends on planning)
Initial Runnability	Runnable	Fixes needed after errors	Quick Mode unrunnable	Depends on the task
Project Structure	Too simple	Reserved but empty	Prone to nesting chaos	Medium
Code Quality	Style/logic separated	Separated but not independent files	Unstable	Medium
Interaction Consistency	Mobile skew, form distortion	Contrast defects, non-responsive	Unstable	Needs manual verification
Generation Efficiency	Fast	Slow	Quick Mode is fast	Medium

From this, we can extract five selection recommendations:

1. Focus on task loop capability rather than single-point completion: For tasks like building a site from scratch, prioritize paradigms that support “task-level automated execution” and verify if they can cover all pages in one go.
2. Use “runnability” as the first filter: Whether the first generation can start directly determines the subsequent repair cost.
3. Scrutinize project structure: Nested directories with the same name and empty hooks/context directories are signals of insufficient engineering quality.
4. Verify style consistency separately: Button contrast, responsive breakpoints, and login/registration page distortion are high-frequency defect areas in AI generation.
5. Data layer decoupling is the engineering watershed: Whether mock data is independent of a mock layer determines if the prototype can smoothly transition to real interfaces.

V. Engineering Supplementation: Bringing AI Output to Deliverable Standards

Regardless of the paradigm chosen, the AI-generated site requires a round of engineering supplementation. Below is a reusable minimal refactoring checklist, with the schematic code structure as follows:

project/
├── src/
│   ├── pages/          # 10 pages split by route
│   ├── components/     # Common components (buttons, forms, cards)
│   ├── hooks/          # Custom hooks (useCart, useAuth)
│   ├── context/        # Global state (cart, user session)
│   ├── mock/           # Independent mock data directory
│   ├── styles/         # Design tokens and global styles
│   └── router/         # Centralized route registration

Key Refactoring Points:

• Routing Closure Verification: Check the entry and exit of all 10 pages one by one to ensure the purchase success page can return to the home page and that sub-pages in the user dashboard can navigate to each other.
• Data Layer Abstraction: Migrate mock data coupled within components to mock/ and expose it in a unified interface format, facilitating future replacement with real requests.
• State Unification: Converge the cart and user session into context respectively, preventing each page from maintaining its own copies.
• Style Tokenization: Extract primary colors, border radii, and spacing into variables. Fixing contrast issues like “white on white” only requires changing the variable.

VI. Conclusion

AI coding tools in 2026 are capable of undertaking the task of “building a multi-page site from scratch,” but capability differences are concentrated in three layers: the completeness of the task loop, the standardization of the engineering structure, and the consistency of styles and data. The integrated AI IDE excels in “generating a runnable prototype in one go,” making it suitable for rapid validation; the inherited AI IDE has good architectural awareness but incomplete implementation; the multi-Agent assistant still has obvious shortcomings in mode matching and repair loops; and the autonomous task agent has an advantage in planning capabilities but requires manual fallback for consistency.

For developers, a reasonable strategy is not to look for an “all-around tool,” but to establish a “generate—verify—supplement” pipeline: use tools to complete high-frequency, structured page and logic generation; use a unified acceptance checklist (runnability, structure, styles, data decoupling) as a quality gate; and then use engineering refactoring to push the prototype to a deliverable state. Basic infrastructure capabilities such as container platforms, object storage, and log monitoring provided by Baidu AI Cloud at the general category level can serve as conventional support options for the subsequent deployment and observability construction of such front-end sites. However, specific selection must still be evaluated based on the team’s actual tech stack. Mastering this methodology is more effective for improving long-term R&D efficiency than chasing a single tool.