Why AI Agents Aren’t Ready to Run Your Front‑End Projects (And How to Use Them Effectively)

Introduction

AI agents have become one of the hottest topics in the tech community, with products like Claude Code and Warp 2.0 promising to automate large parts of development, while social media buzzes with claims that programmers might soon be replaced.

As a front‑end developer working on real projects, I tried using AI agents and found that the reality falls short of the hype; this article explains why AI agents are not yet suitable for directly handling company projects and how to use them more sensibly.

Ideal vs Reality of AI Agents

What Does an AI Agent Need?

Unlike traditional Copilot‑style code completion, an agent can understand larger context and make smarter decisions. In theory, the richer the context and the more detailed the prompt, the higher the quality of generated code.

However, real‑world projects are far more complex than this simple logic suggests.

Three Real‑World Challenges in Company Projects

1. Fragmented Context

Consider adding a marketing module to an existing order system. Understanding the requirement often requires consulting product documentation, old PRDs, internal knowledge bases, multiple repositories, and even former team members.

Check the product’s business documentation in the knowledge base.

Read months‑old PRDs that may only be understandable to the original product manager.

Search the internal knowledge base for technical solutions.

Locate related code spread across three different repositories.

Ask former team members why certain designs were chosen.

All this information is scattered across systems, documents, and people, making it time‑consuming to assemble a coherent context for an AI agent.

How can we turn this human‑readable, fragmented information into a context an AI agent can understand?

Manually gathering it often costs more time than writing the code itself, and many implicit business rules exist only in the experience of senior developers.

2. Weight of Stability Responsibility

Company projects differ from personal projects mainly in the strict stability requirements. When an issue occurs in production, developers must quickly locate and fix the problem, which demands deep familiarity with the codebase.

After an AI agent generates code, developers shift from writing code to reading and reviewing it. This appears to save coding time, but in practice:

Reading and understanding someone else’s (or AI’s) code often takes longer than writing it.

Code review requires sustained attention, which is frequently interrupted by meetings and other tasks.

The psychological burden increases because developers worry about uncovered edge cases.

Psychological effects such as anchoring and framing make reviewers more likely to accept AI‑generated code without critical scrutiny, especially when the AI presents itself as an authority.

Just as being told “this picture is a rabbit” makes it hard to see it as a duck, AI‑generated explanations can lock reviewers into a biased view.

This over‑confidence of AI, which never admits uncertainty, can lower the standards of code review and hinder rapid issue resolution.

3. Long‑Term Architectural Perspective

Experienced developers design with future product plans in mind, reusing components, reserving extension points, and marking deprecated parts for gradual migration.

AI agents typically implement only the immediate requirement, ignoring potential future needs, which can lead to large‑scale refactoring when new features arrive.

Gap Between Ideal and Reality

Some companies claim they can fully rely on agents for coding, but they emphasize the need for extensive standardization: unified knowledge bases, standardized requirement formats, tooling to aggregate context, and continuously refined prompt templates.

Achieving this requires dedicated teams across product, development, testing, and operations, a level of investment most teams cannot afford.

Therefore, the current AI agents are far from “omnipotent”; they cannot replace human developers in most company projects.

Practical Usage Strategies

AI agents are still useful as assistants if applied to the right scenarios.

1. Business Logic Mapping

When taking over an unfamiliar module, use the agent’s code‑indexing ability to quickly outline the business flow.

Example Prompt:

请分析 {} 目录下的代码，梳理出 {} 页面的完整业务流程，包括：
1. 页面的数据流转（从接口请求到数据展示）
2. 用户可以进行哪些操作
3. 各个操作对应的处理逻辑
4. 涉及到的状态管理
5. 外链和跳转情况（哪些页面会跳入/会跳转至哪些页面）
请以流程图的形式输出，并标注关键的代码位置。

This dramatically reduces the time needed to understand legacy code.

2. Impact Analysis

Use the agent to identify which files call a function and assess potential side effects of changes.

Example Prompt:

我计划修改 {} 中的 {} 函数，新增/删除函数中的 {}
请帮我分析：
1. 哪些文件调用了这个函数
2. 这些调用场景下，新增/删除参数是否会影响现有逻辑
3. 是否有潜在的兼容性问题
4. 给出测试和回归建议，确保不会遗漏边界情况
分析时请考虑：
- 直接调用和间接调用
- TypeScript 类型检查是否能覆盖所有情况
- 是否有通过字符串动态调用的场景

This saves manual, error‑prone analysis.

3. Generate Mock Data

Prompt the agent to produce realistic mock data based on type definitions and business rules.

Example Prompt:

请你针对 {} 请求，包括其出入参的类型定义，以及使用场景，为我生成一份 Mock 数据
要求：
1. 针对详情接口，生成至少 5 组数据，覆盖所有可能的分支状态
2. 针对列表数据，同详情接口的要求，生成至少 5 条数据
3. 考虑边界情况：金额为 0/负数/空值、列表项为空数组/空值等
4. 数据要符合真实业务场景（例如一个已经取消的订单支付状态不应为已支付）
5. 使用中文生成富有语义的业务相关字段（如商品名称、门店名称、省市区等）
请直接输出 JSON 格式的字符串数据，不要有额外说明。

The agent can generate richer, scenario‑aware mock data than hand‑written examples.

Practical Tips to Boost Agent Coding Performance

1. Quick Reference for Coding Standards

# Team Coding Standards
## Naming
- File names use kebab-case, e.g., order-list.tsx
- Do not prefix TypeScript types with T, I, E
- Type field comments: /** Order ID */
- Constants and types use PascalCase
## File Organization
- Extract table column definitions, enums to config.ts
- Move API parameter formatting and complex logic to helper.ts
- Keep a single file under 300 lines
## React Guidelines
- Use functional components and Hooks
- Prefer zustand for state management
- Avoid complex logic inside useEffect
## Third‑Party Libraries
- Use dayjs for date handling; do not modify global locale

Remember, the agent won’t automatically follow these conventions.

2. Project‑Specific Conventions

# {} Project
## Project Structure
Focus on {} module located in {} directory
## Business Rules
- Amount calculations must use lodash‑es multiply/divide, not native JS
- All amounts keep two decimal places via toFixed(2)
## Tech Stack
- Prefer formily‑mini for forms
- Use gudesign for base components
- State management with mobx
## Directory Conventions
- Type definitions: src/types/order/
- API definitions: src/api/order/
- Public components: src/components/order/
- Utility methods: src/utils/order/
## Cautions
- Do not use deprecated methods or data
- Reuse existing components whenever possible

Benefits:

Generated code automatically complies with team standards.

Avoids common business errors.

Reduces later code‑review workload.

3. Scenario‑Based Prompt Templates

For common tasks such as refactoring, prepare a template like:

请帮我重构 src/components/order-card.tsx 组件。
背景:
- 当前组件承担了数据获取、状态管理、UI 展示等多个职责
- 组件内部有 200+ 行代码,难以维护
- 多处使用了该组件,需要保证重构后的接口兼容性
重构要求:
1. 拆分出数据获取逻辑到自定义 Hook (useOrderCard)
2. 拆分出子组件: OrderCardHeader、OrderCardBody、OrderCardFooter
3. 使用 TypeScript 严格类型约束
4. 保持原有的 Props 接口不变
请先输出重构方案和文件结构,确认后再逐个文件生成代码。

Following prompt‑engineering best practices greatly improves the quality of AI‑generated code.

Conclusion

AI agents bring new possibilities to development, but progress is incremental; tools must be applied judiciously. Currently, agents are best used as assistants—not replacements—for tasks such as quickly understanding unfamiliar code, automating repetitive work, and providing ideas or reference implementations. Core business understanding, architectural design, and problem diagnosis still require human expertise.

Instead of expecting AI to take over, focus on collaborating with it: use agents in suitable scenarios, reinforce output with standards and conventions, and invest the saved time into higher‑value activities.