AI Coding Assistants: Strengths, Risks, and a Practical Guide to Building an E-commerce System with Terminal AI Agents

1. Introduction: Building an E-commerce System from Scratch with AI

Calling a large language model that can read and write project files and execute system commands directly from the command line to complete tasks from requirement understanding to code implementation is becoming a common workflow for many developers. These tools can be described as “terminal-based AI coding assistants” (or “terminal assistants”). They differ fundamentally from traditional code-completion tools: instead of merely predicting the next line of code, they continuously execute multiple steps around a defined goal. Reading files, creating directories, writing code, running tests, and correcting themselves based on errors can all happen within a single session.

In one example, a developer used a terminal assistant to build an e-commerce system from scratch: the requirements were submitted as-is, code was generated in phases, and the system was finally run. Errors appeared repeatedly, and each one was handed to the assistant for analysis and repair until the system reached a usable state. This process shows that the overall capabilities of these tools are already sufficient for building small- and medium-sized full-stack projects. It also reveals a key fact: project success often depends not on how “smart” the model is, but on how humans organize requirements, constrain the AI, and verify results.

The question “Which tool is stronger?” rarely has a standard answer. Model capability, usage quotas, billing models, service availability, and account stability can change with versions and operational policies, so any static conclusion may become outdated quickly. Rather than comparing two fixed options, it is more useful to build an analytical framework: break down capabilities by dimension, assign tasks by project phase, and rely on engineering practices as a safeguard. This article follows two threads: first, a systematic analysis of the strengths and weaknesses of terminal assistants; second, a practical guide to using them effectively, using an e-commerce system as an example.

2. Capability Boundaries: What Terminal Assistants Can and Cannot Do

When evaluating any coding tool, it is important to clarify its capability boundaries first. The capabilities of terminal assistants can be summarized in three layers:

1. Understanding: parsing natural-language requirements, reading source code, and inferring dependencies between modules.
2. Execution: reading and writing files, executing commands, and running tests in the local environment, forming a “modify-run-observe” loop.
3. Correction: locating causes based on runtime results and error messages, adjusting the approach, and revalidating.

Combined, these layers allow terminal assistants to cover the full chain from requirements to runnable code. This is their core difference from code-completion tools.

However, their boundaries are also clear. Terminal assistants cannot automatically know business rules that have not been documented, such as regional promotion policies or settlement agreements with physical stores. They cannot make architectural trade-offs for humans, such as choosing between monolithic and microservices architectures or determining how data should meet compliance requirements. They also cannot guarantee that the execution environment itself is correct: dependency version mismatches, occupied ports, insufficient permissions, and similar environment problems may appear to the assistant as “code problems.”

Understanding these boundaries helps with task division. AI should handle high-density work such as coding, debugging, and validation, while humans should define goals, establish standards, and control key decisions. It is often more effective to treat AI as a capable engineer who needs a clear task specification and acceptance criteria than as an “all-capable universal assistant.”

3. Advantages: Four Values of Terminal AI Coding Assistants

3.1 End-to-End Closed Loop Reduces Context Loss

In traditional workflows, requirement organization, code generation, copy-pasting, running tests, and reporting errors often happen across different tools and windows. Every switch can cause information loss: developers may manually select fragments of error messages, while the AI cannot see the full directory structure or configuration files and must guess based on partial information. In the same session, a terminal assistant can grasp the entire project, read relevant files, run commands, observe outputs, and adjust continuously.

For example, adding a coupon redemption endpoint may follow a closed-loop process: locate the relevant order service code, add the endpoint and database fields, write unit tests, run tests, discover a conflict with inventory validation logic, modify it, and continue until tests pass. The developer only needs to describe the goal and constraints, avoiding many mechanical intermediate operations.

3.2 Repository-Wide Context Improves Consistency

In medium-sized or large projects with multiple collaborators, consistency is often more important than speed. Adding a field may require synchronized changes across data models, data access layers, business logic layers, API layers, and frontend displays. Missing any layer can cause runtime errors. A terminal assistant can make coordinated changes to multiple files within a single task and run builds and tests afterward for self-checking, reducing the probability of omissions.

3.3 Autonomous Debugging Turns Errors into Iteration Signals

When developers report errors to AI, they often paste only a line such as “NullPointerException” or “500 Internal Server Error” without enough context. A terminal assistant can reproduce the issue locally, read the full stack trace, inspect related source code, and even add logging and rerun the code. Debugging changes from “guess-based Q&A” to “experiment-based validation,” improving localization efficiency. The earlier case in which errors appeared one after another and each was handed to the AI is essentially treating the AI as an indefatigable debugging partner.

3.4 Full Traceability Supports Review and Collaboration

Every command execution, file modification, and test run by a terminal assistant can be preserved in session records. These records can serve as reference material for code review, explain “why this change was made,” and become team knowledge that helps new members understand project evolution. Compared with verbal descriptions or scattered chat logs, traceable operation logs are more valuable when troubleshooting production issues.

4. Disadvantages: Five Risks That Must Be Addressed

4.1 Unstable Generated Code Quality Requires Human Safeguards

Large models are fundamentally probabilistic generation models. The same requirement may produce multiple implementation options, some of which may contain redundant logic, over-abstraction, or practices that do not match project conventions. Some implementations may run in a demo environment but expose performance and stability issues under high concurrency or in production.

Mitigation strategies:

• Establish clear coding standards and project conventions, and provide them to the AI as constraints in each task.
• Include code review, static analysis, unit testing, and API automation testing as fixed process steps, ensuring AI-generated code cannot bypass quality gates.
• Use small-step commits: assign the AI one independently verifiable small task at a time, confirm it is correct, and then proceed to the next step.

4.2 Account and Quota Risks Can Disrupt Work

Some commercial large-model services have risk-control mechanisms for abnormal call frequency or multi-region login, and accounts may be restricted. Services with small quotas may be exhausted quickly during long-term, high-frequency coding tasks. Service availability may also suffer from rate limiting, slower responses, or temporary outages. In addition, model version updates may change output style and capability, so a prompt that worked well yesterday may not produce the same quality today.

Mitigation strategies:

• Persist key information to disk: requirements documents, API agreements, and data model designs should be saved as files in the project rather than existing only in conversations. When switching to any tool, a new session can quickly restore context.
• Commit at phase-level granularity: commit after each functional module is completed, and use feature branches to avoid rework caused by interruptions.
• Use models in layers: assign architecture design, complex algorithms, and difficult debugging tasks to stronger models; assign boilerplate code, unit tests, and documentation comments to lower-cost models.
• Use tools or gateways that support multiple model switching to prevent project stagnation when a single service fails.

4.3 Prompt Quality Determines Output Quality

“Just sending the requirement” can work, but the precision of the requirement description directly affects code quality. “Build an e-commerce system” and “Build a B2B e-commerce system for enterprise procurement, supporting tiered pricing and approval-based ordering, with the following technology stack, data model, and API specification” can produce results that differ by orders of magnitude. The more ambiguous the problem description, the more the AI must guess, and the more guessing leads to rework.

Mitigation strategies:

• Use structured requirement descriptions: list goals, user roles, feature lists, non-functional requirements, technical constraints, and acceptance criteria item by item.
• Maintain a “project specification” for the project, including architecture conventions, directory structure, naming conventions, and API styles. At the start of each task, ask the AI to read this document first.
• Save effective prompts as reusable templates rather than improvising every time.

4.4 Long Tasks Can Drift and Become Increasingly Messy

In multi-step, long-cycle autonomous tasks, as the session becomes longer, the AI may forget or misunderstand earlier decisions, leading to “drift” phenomena such as repeated modifications, overturning existing designs, or introducing unrelated features. Errors can accumulate and eventually make the codebase difficult to maintain. Some operations are irreversible, such as deleting files, rewriting core modules, or modifying database structures. If the AI performs such operations without a confirmation step, it may cause serious losses.

Mitigation strategies:

• Break tasks down: split large goals into small tasks with clear boundaries and independent validation, each within the scope of one session.
• Maintain a task checklist: record completed, in-progress, and pending items in a document, and ask the AI to confirm current progress before each conversation round.
• Add confirmation mechanisms for irreversible operations, and back up sensitive items before executing sensitive commands.
• Periodically ask the AI to review: summarize deviations between the current implementation and the original design, and adjust the direction promptly.

4.5 Security and Compliance Risks Cannot Be Ignored

AI-generated code may contain common security defects, such as SQL injection caused by missing parameter validation, cross-site scripting caused by rendering user input without escaping, missing API authentication, or hard-coded secrets in configuration files. Code that handles user data may also violate personal information protection requirements if not handled properly.

Mitigation strategies:

• Clearly specify security requirements in the requirements document, for example, all write APIs must be authenticated, all user input must be validated, and secrets must be injected through environment variables or a secret management service.
• Before deployment, use dependency vulnerability scanning and code security scanning tools to perform systematic checks.
• For an e-commerce system that handles personal information such as user addresses and phone numbers, clarify the scope of collection, storage methods, and usage boundaries from the design stage, following the principle of minimum necessary data.

5. Practical Route: Building an E-commerce System from Scratch with a Terminal Assistant

Theoretical analysis should eventually lead to practice. Using an e-commerce system as an example, this section presents a five-stage practical route. The core idea is: let the AI execute, and let humans define and accept.

Stage 1: Structure the Requirements and Turn Ideas into Specifications

Vague verbal requirements are the biggest source of rework. Before starting implementation, produce a structured requirements specification document as the “single source of truth” for the project. Example:

# Requirements Specification (Example)
## 1. Project Goal
Build a B2B e-commerce system for enterprise procurement, supporting product catalogs, ordering and payment, order management, and merchant onboarding.
## 2. User Roles
- Buyer: browse products, add items to a purchase list, submit orders, and track logistics
- Approver: approve orders and set spending limits
- Merchant: manage products and inventory, and process shipments
- Platform operator: review merchant onboarding and configure marketing activities
## 3. Feature List (by Priority)
- P0: product catalog and search, purchase list, orders, payment, basic authentication
- P1: multi-level approval, coupons, invoice requests
- P2: product recommendations, data dashboards
## 4. Non-Functional Requirements
- Core API P95 response time must not exceed 500 ms
- Order creation must be idempotent to avoid duplicate orders caused by repeated submission
- Inventory deduction must prevent overselling
- Sensitive operations must be fully recorded in audit logs
## 5. Technical Constraints
- Backend: Java + Spring Boot, layered into controller / service / repository
- Frontend: React + TypeScript
- Database: PostgreSQL; cache: Redis
- API style: RESTful, with a unified response structure { code, message, data }
- All configuration must be injected through environment variables; hard-coded secrets are prohibited
## 6. Acceptance Criteria
Each P0 feature must include unit tests and API test cases, and the core order flow must be covered by end-to-end tests.

The value of this document is that it is both the “task specification” for the AI and the “reference checklist” for human acceptance. After each feature is completed, the team can return to the document and check whether it meets the agreed requirements.

Stage 2: Establish Architecture and Infrastructure First

Many developers prefer to let AI write business code first. However, they may later discover that unified exception handling, logging standards, or database migration mechanisms are missing, forcing large-scale rework. A safer approach is to build the foundation first:

1. Define the project skeleton: directory structure, layering standards, unified response format, global exception handling, and logging standards.
2. Define the data model: fields and relationships of core entities such as orders, order items, products, inventory, users, and approval records. It is recommended to first create an entity relationship diagram in document form, confirm it, and then generate database migration scripts.
3. Define infrastructure dependencies: database connections, caching, message queues, and object storage. Prefer managed cloud services to reduce self-managed operational costs. Teams can use container services to deploy applications, managed databases to store business data, caching services to accelerate hot data reads and writes, object storage to hold product images and invoice files, message queues for asynchronous processing after ordering, and logging and monitoring services to observe runtime status.
4. Establish quality and automation baselines: code formatting tools, static analysis rules, test frameworks, and CI pipelines. With baselines in place, every line of code generated by the AI will be constrained by standards.

Stage 3: Generate Business Code in Phases

Once the foundation is stable, proceed module by module. A recommended order is: users and permissions → products and inventory → orders and payments → marketing and approvals → data dashboards. This order follows the principle of “dependencies first”: earlier modules provide foundational capabilities for later modules, and each completed module can be independently validated.

A unified prompt template can be used for each module:

Task: Implement the "create order" API in the order module
Reference documents: docs/requirements.md (Section 3, P0), docs/api-contract.md (order API agreement)
Constraints:
1. Follow project layering standards; the API layer should only handle parameter validation and response wrapping
2. Order creation must be idempotent; duplicate submissions must return the same order
3. Use optimistic locking for inventory deduction; if it fails, roll back the order
4. Add unit tests covering normal order creation, insufficient inventory, and duplicate submission
Completion criteria: mvn test passes completely, and API documentation is updated

This format clearly states the task, references, constraints, and acceptance criteria, making the AI output more aligned with the actual project.

Stage 4: Run and Validate the System

After code generation, the real test begins. Common runtime problems include:

• Dependency conflicts or version incompatibilities
• Database connection failures or errors while executing migration scripts
• APIs returning unexpected status codes
• Field name mismatches between frontend and backend
• Inventory overselling under concurrency

Most of these problems are not caused by the model being “not smart enough,” but by issues connecting the environment, configuration, and code. The principle is “locate first, then fix”: read the complete error message, determine whether the problem lies in the environment, configuration, or code, and then ask the AI to fix it. Avoid sending an entire log without explaining what has already been investigated.

Establishing a layered testing system can greatly reduce manual trial and error:

Testing pyramid (bottom-up)
├── End-to-end tests (E2E): cover the main flow "login → order → payment → shipping"
├── API tests: validate parameter validation, permission control, and idempotency for each API
├── Integration tests: validate interactions between services and databases, caches, and message queues
└── Unit tests: validate core business logic, such as inventory deduction, price calculation, and approval flow

After fixing each problem, add corresponding test cases to prevent the same issue from recurring. This is both quality assurance and a way to accumulate “project knowledge” for the AI: test cases are precise descriptions of business rules.

Stage 5: Debug and Turn AI into a Debugging Partner

Handling problems during runtime is key to successful delivery. An effective problem description should include four elements:

Symptom: order submission occasionally returns a 500 error, roughly once every 20 attempts
Reproduction condition: easier to trigger when 10 concurrent requests submit orders for the same product
Full stack trace:
java.lang.OptimisticLockingFailureException: ...
    at com.example.order.service.OrderService.createOrder(OrderService.java:87)
    ...
Already investigated: database connection is normal, single-request scenario has no exceptions, logs show that inventory update affected 0 rows
Expected: identify the root cause, provide a fix, and add test cases for concurrent scenarios

Providing the symptom, reproduction conditions, stack trace, investigated items, and expected output clearly helps the AI locate the issue more effectively than “There is an error; please check it.” If the AI’s proposed solution is unreasonable, continue asking “why” to guide it to show its reasoning, and then evaluate whether the solution is valid. For questionable solutions, first validate them in a test environment before merging into the main branch.

6. Human-in-the-Loop: What Must Remain Under Human Control

Even if AI capabilities are strong, they should not be left unchecked. The following items should always be controlled by humans:

1. Architecture decisions: choices with long-term impact, such as database selection, service decomposition granularity, and technology stack version upgrades, should be evaluated comprehensively based on team capability, operational cost, and long-term evolution plans.
2. Security design: authentication models, permission granularity, and encryption methods for sensitive data directly affect compliance baselines and should not rely on AI defaults.
3. Business rule confirmation: logic directly related to financial safety, such as price calculation, discount stacking, and refund rules, must be confirmed item by item by the business side.
4. Code review: code on core paths should be reviewed line by line by experienced developers, focusing on concurrency control, transaction boundaries, and exception handling.
5. Release and deployment: changes in production environments should follow standard processes such as canary releases and rollback plans. AI should not directly operate production environments.

A practical criterion is: if an operation has a high cost of error, is difficult to roll back, or has a broad impact, it should retain a human confirmation step. AI is good at efficient execution within clear boundaries, not at taking decision-making responsibility for humans.

7. Selection Recommendations and Conclusion

Returning to the original question: what are the strengths and weaknesses of terminal AI coding assistants? The answer is not a fixed conclusion, but an evaluation framework that can be adjusted according to actual project conditions.

Evaluation Dimension	Key Questions	Recommendation
Coding capability	How does it perform on complex business logic, cross-file refactoring, and difficult debugging?	Use real team projects for small-scale trials instead of relying only on general benchmarks
Cost and quota	Billing model, quota limits, and long-term usage cost	Use models in layers based on task complexity, and track the actual cost per feature point
Stability and availability	Service availability, response speed, and account risk-control policies	Prepare alternatives for key stages and support multi-model switching
Context management	Support for repository-wide understanding, long-session continuity, and project standard injection	Prefer tools that can read project configurations and documents
Security and compliance	Whether code is used for training, and how data is transmitted and stored	Read service terms, and consider private deployment for sensitive projects
Ecosystem and integration	Support for mainstream editors, version control, and CI/CD	Choose solutions that fit the existing toolchain and reduce switching costs

It is important to note that actual cost is not the same as listed price. The listed price only reflects the cost per call; actual cost also includes trial-and-error, rework time, human review effort, and waiting caused by interruptions. When evaluating cost-effectiveness, consider the “total cost required to complete one feature point,” not just the unit price per call.

In summary, terminal AI coding assistants have lowered the barrier from requirements to runnable code to an unprecedented level. However, a lower barrier does not mean lower standards: the more ambiguous the requirements, the more rework; the clearer the constraints, the more stable the output; the better the validation, the more reliable the delivery.

For developers who plan to use AI to build a project from scratch, three principles are worth remembering:

• Requirements first: turn ideas into specifications and let the AI work within clear boundaries.
• Small steps and fast feedback: break down tasks, validate frequently, and commit promptly to avoid losing control in long sessions.
• Engineering safeguards: testing, code review, security scanning, and monitoring/alerting are all indispensable.

Tools will continue to evolve, and today’s comparison may become invalid tomorrow. However, the collaboration model in which humans define problems, AI executes solutions, and engineering systems ensure quality has long-term value. Rather than spending too much time choosing a tool, it is better to start practicing with a real project. Learning by doing is the most accurate way to understand this type of tool.