Autonomous AI Website Reconstruction: Handling Long Tasks, Costs, and Quota Limits

An Experiment in Fully Delegating Website Reconstruction to AI

For independent developers and small teams, building a company website is rarely a one-time effort. A page must simultaneously satisfy requirements for brand expression, product explanation, mobile responsiveness, search engine visibility, loading performance, and conversion paths. The real time-consuming part is usually not writing a specific component, but the constant back-and-forth adjustments between visual design, structure, and interaction.

This experiment selected an existing frontend website for reconstruction. The goal was not a minor patch, but a relatively complete autonomous reconstruction handled by an intelligent coding system:

• Preserve the core product positioning while redesigning the homepage’s information architecture;
• Reference the visual organization methods of high-quality design works;
• Explore multiple homepage directions simultaneously;
• Automatically modify pages, styles, components, and resource references;
• Complete basic checks after running locally;
• Minimize manual intervention.

The experiment lasted about 85 minutes. The system modified 20 files, adding and adjusting approximately 4,320 lines of code. Towards the end of the task, a 5-hour resource quota limit was triggered, with recorded cumulative costs of about $52.55. This result indicates that long-duration autonomous coding can indeed improve execution efficiency, but it also exposes problems such as uncontrollable costs, blurred task boundaries, and quota triggers.

1. Establish a Rollback Baseline Before Starting

The most dangerous approach when letting AI directly modify an existing project is to “start immediately without preserving the current state.” If there are no pre-modification page screenshots, code versions, and runtime logs, it becomes difficult to determine whether a degraded result stems from design decisions, style overrides, or broken component logic.

It is recommended to prepare four types of baselines before starting.

1. Save Code Versions

First, create an independent branch or commit point to ensure all experimental changes can be undone. At the very least, record:

• Current code version;
• Dependency installation status;
• Build commands;
• Local startup commands;
• Environment variable list;
• Known issues and optimization to-dos.

If the project uses a version control system, you can follow a process similar to this:

git status
git add .
git commit -m "save baseline before redesign"
git switch -c ai-redesign-experiment

The key here is not the command itself, but establishing an isolation between the “experimental branch” and the “stable version.”

2. Save Desktop and Mobile Screenshots

Website reconstruction cannot solely focus on the desktop. It is recommended to save at least the following page states:

• Desktop homepage first screen;
• Desktop main content area;
• Mobile first screen;
• Mobile navigation expanded state;
• Key conversion areas;
• Forms, buttons, and footer.

Screenshots should ideally include viewport size information, such as standard desktop width, common mobile widths, and tablet widths. This allows for apples-to-apples comparisons after the redesign, rather than relying on the impression that it “looks better.”

3. Record Performance and Functionality Baselines

Before the visual overhaul, record the basic metrics of the current page:

• First screen load time;
• Number of page resources;
• Image file sizes;
• Console errors;
• Mobile overflow issues;
• Link availability;
• Form submission functionality;
• Dark or light theme switching.

If you do not record these, the AI may only optimize the visual effects while introducing larger images, more complex animations, or additional client-side scripts, ultimately making the page slower.

2. Prompts Cannot Just Say “Make the Website Pretty”

A simple “please reconstruct the entire website” is usually too vague. For complex frontend projects, prompts need to simultaneously define goals, boundaries, processes, acceptance criteria, and stop rules.

A more reliable task description can be divided into six parts.

1. State the Project Background

Tell the system what the current project is, who it serves, the existing page structure, and the core content that cannot be changed. For example:

This is an online product website for developers.
Please first read the project structure, page components, style files, and resource directory.
Propose a redesign plan after understanding the existing information architecture.
The product name, core features, and main conversion entry points must not be arbitrarily changed.

2. Define Design Goals

Design goals must be verifiable rather than resting on aesthetic descriptions:

Goals:
1. Strengthen the value proposition on the first screen;
2. Allow users to understand the product's purpose within 10 seconds;
3. Reduce visual noise;
4. Improve reading continuity on mobile;
5. Retain clear call-to-action buttons;
6. Ensure the page has a unified spacing, typography, and color system.

3. Specify Execution Order

Complex tasks must be phased. Do not let the system jump directly from analysis to massive modifications:

Please execute in the following order:
Phase 1: Check the project structure and summarize the current status;
Phase 2: Propose three different homepage directions;
Phase 3: Compare the pros and cons of the three directions;
Phase 4: Select one direction and explain the reasoning;
Phase 5: Modify the code in batches;
Phase 6: Run checks, fix errors, and output a change log.

4. Set Modification Boundaries

Without boundaries, the system might rewrite unrelated modules or even delete existing features:

Constraints:
- Do not modify server-side APIs;
- Do not delete existing core pages;
- Do not change build tools;
- Do not introduce unnecessary new dependencies;
- Do not use images with unconfirmed licensing;
- Do not consolidate all page logic into a single massive component;
- After completing each phase, verify that the project can still start.

5. Define Acceptance Criteria

Acceptance criteria should cover visual, functional, and engineering quality:

Acceptance Requirements:
- No horizontal scrolling on both desktop and mobile;
- All primary buttons have clear hover and focus states;
- Navigation is usable on narrow screens;
- No obvious console errors on the page;
- Images have appropriate dimensions and alt text;
- Main content supports keyboard accessibility;
- Build commands can execute successfully;
- Output the final modified files, risks, and unfinished items.

6. Add Stop Conditions

Stop conditions are critical for cost control:

If any of the following occurs, pause and report:
- Two consecutive modifications fail to improve the target issue;
- Build errors cannot be located;
- Large-scale rewriting of business logic is required;
- More than 10 additional files are expected to be modified;
- Resource consumption is approaching the budget;
- Conflicts between requirements are detected.

3. Multi-Agent Collaboration is for Exploration, Not Infinite Divergence

In this experiment, the system explored three homepage directions simultaneously through multiple sub-tasks before selecting one to implement. This approach is suitable for creative and plan comparison, but the scope of parallelization must be controlled.

Work can be divided into four roles:

1. Research Role

Responsible for reading existing code and pages, outputting:

• Current page structure;
• Content hierarchy;
• Component reuse relationships;
• Style organization methods;
• Known technical debt;
• Potential mobile issues.

2. Design Role

Responsible for proposing different visual directions but not directly making large-scale code changes. Each direction should clearly state:

• Target user feeling;
• First screen layout;
• Color and typography strategy;
• Animation usage principles;
• Information layering method;
• Potential implementation costs.

3. Implementation Role

Modifies code according to the selected plan, adhering to file boundaries. For example, handle design tokens and public components first, then the homepage, and finally responsive details.

4. Review Role

Responsible for checking if the implementation deviates from the goal, including:

• Whether the page starts normally;
• Whether layout overflow occurs;
• Whether duplicate styles exist;
• Whether there are invalid resource references;
• Whether original interactions are broken;
• Whether unnecessary dependencies are introduced.

More agents are not always better. Too many roles lead to redundant analysis, context transfer overhead, and extra costs. For small to medium websites, exploring three directions plus one unified review is usually sufficient.

4. The Benefits and Costs of High-Intensity Reasoning Modes

In complex reconstructions, increasing reasoning intensity usually helps the system understand component relationships, analyze visual schemes, and plan modification sequences, but it does not guarantee a better result.

This experiment showed three distinct characteristics.

First, the system spent a considerable amount of time early on reading the project and comparing directions. This helps avoid falling into a chaotic “modify while looking” state from the very beginning.

Second, the system repeatedly ran checks, read files, generated patches, and fixed issues. For tasks involving multiple pages and responsive layouts, this loop reduces obvious errors.

Third, the longer the reasoning time, the more input/output resources are consumed. Costs grow rapidly, especially when the system repeatedly reads large files, repeatedly outputs complete components, or initiates multiple sub-tasks.

Therefore, high-reasoning mode is more suitable for:

• Projects with complex component relationships;
• Redesigns requiring unified design across pages;
• Projects with a large amount of legacy code;
• Tasks requiring simultaneous consideration of visual and engineering constraints.

It is not suitable for:

• Changing a button color;
• Adjusting a single margin;
• Fixing an obvious typo;
• Performing a simple dependency upgrade.

5. Why Long Tasks Easily Hit Quotas

The “5-hour quota limit” message that appeared late in the experiment does not necessarily mean a single task ran for 5 hours. It is usually related to cumulative usage within a period, model computation volume, number of parallel tasks, and context length.

Common causes include:

1. Continuous Context Bloat
   The system continuously carries historical conversations, code snippets, and tool outputs, requiring more content to be processed at every subsequent step.

2. Repeated File Reads
   Certain tasks scan the same directory multiple times, especially when clear change logs are not established after modifications.

3. Parallel Sub-task Consumption
   If three design directions run simultaneously, actual resource consumption can be several times that of a single task.

4. Excessive Validation Loops
   Without clear “pass criteria,” the system may continuously perform low-value micro-adjustments.

5. Direct Output of Large Files
   Rewriting a component entirely wastes more resources than generating precise patches and is more prone to conflicts.

The solution is to divide tasks into independently verifiable phases. Save the results after each phase and start a new session. Simultaneously, require the system to output only summaries, patches, and necessary snippets to avoid repeatedly printing complete files.

6. Cost Control Should Be Front-Loaded, Not Fixed After Exceeding Limits

A simple budget model can be established before the task begins:

Estimated Cost =
Base Analysis Cost
+ Solution Exploration Cost
+ Code Modification Cost
+ Validation and Fix Cost
+ Parallel Task Overhead

For website redesigns, the following strategies are recommended:

• Use standard reasoning first to complete project scanning;
• Only increase reasoning intensity during solution comparison and complex implementation phases;
• Modify design tokens, layouts, and public components before specific pages;
• Validate immediately after completing each module; do not accumulate dozens of files before checking;
• Set clear budgets for images, animations, and dependencies;
• Stop exploring when nearing the quota, prioritizing build completion and rollback points.

If a fast mode is available, choose based on task type. Fast mode is suitable for small-scale modifications and repetitive operations but may not be more cost-effective for complex architectural judgments. Do not switch all tasks to a high-price mode simply because it is faster.

7. Human Handoff Points Must Be Designed in Advance

“Zero intervention” is suitable for testing system capabilities but not for direct use in production projects. A more reliable approach is to set up manual checkpoints:

• Before the plan is finalized, manual confirmation of information architecture;
• Before large-scale modifications, manual confirmation of file scope;
• Before introducing new resources, manual confirmation of licensing and size;
• After visual completion, manual inspection of brand expression;
• Before release, manual completion of mobile, accessibility, and performance acceptance.

AI excels at rapid execution and code traversal, but its understanding of product trade-offs, brand tone, real user feedback, and business priorities remains limited. Humans should not replace the system in mechanical labor but should focus their energy on directional judgment and risk control.

8. A Reusable Complete Workflow

A relatively robust autonomous website reconstruction workflow is as follows:

1. Create a version baseline and save screenshots;
2. Record build, startup, and testing methods;
3. Have the system read code only, without immediate modifications;
4. Output a list of current issues;
5. Propose a small number of design directions in parallel;
6. Manually select one direction;
7. First establish design tokens like colors, typography, and spacing;
8. Then modify public components and the page skeleton;
9. Process desktop and mobile versions in batches;
10. Run builds and static checks after each batch of modifications;
11. Perform browser validation on key pages;
12. Summarize file changes, remaining issues, and resource consumption;
13. Conduct manual acceptance;
14. Merge code or roll back the experimental branch.

The ultimate goal is not to let the AI “run on its own for as long as possible,” but to let it accomplish more high-value work within clear boundaries. An 85-minute experiment costing about $52.55 that ultimately triggered a quota limit shows that autonomous reconstruction possesses strong execution capabilities, but also demonstrates that long tasks must be paired with budgets, checkpoints, and stop mechanisms.

For developers, what is truly worth reusing is not a specific model or command, but a methodology: establish a baseline first, then clarify goals; explore solutions first, then implement in phases; let multiple intelligent roles assume different responsibilities while limiting parallelization and context scale; finally, ensure quality through a combination of automated checks and manual acceptance.