This article maps the four-stage evolution of AI coding: intelligent code completion, Agent Coding, Rules-based constraints, and Specification-Driven Development (SDD). It explains how to improve engineering efficiency, control code decay, and preserve project context. Core keywords: AI coding, Rules, SDD.
Technical Specification Snapshot
| Parameter | Details |
|---|---|
| Domain | Engineering practices for AI coding |
| Core languages | Java, Markdown |
| Typical tools | GitHub Copilot, Cursor, Claude Code, OpenCode, OpenSpec |
| Collaboration paradigms | Prompting, Rules, Agent Coding, SDD |
| Common protocols/frameworks | Spring Boot, gRPC/StarRPC, MyBatis, Apollo |
| Reference repository stars | Not provided in the original article |
| Core dependencies | Large language models, project rule files, specification documents, testing constraints |
The value of AI coding has shifted from “completing code” to “managing context”
The real benefit of AI coding does not come from writing a few extra lines of code for developers. It comes from reducing repetitive work, shortening documentation lookup time, and standardizing implementation paths. In complex business domains such as finance, trading, and back-office systems, efficiency and maintainability must improve together.
The original practice outlines a clear path: first use AI to solve localized coding problems, then let AI generate complete features, next solidify team conventions through Rules, and finally explore SDD so that specifications become the entry point to development. This is not a tool replacement. It is an engineering paradigm upgrade.
The first stage works best when intelligent code completion handles repetitive code quickly
At the entry stage, teams mainly rely on tools such as GitHub Copilot. The most visible gains appear in object construction, field mapping, CRUD completion, and single-method refactoring. This stage works best for localized tasks with clear rules and simple context.
public List
<ItemCardVO> buildItemCards(List<ContentEntity> entities) {
List
<ItemCardVO> result = new ArrayList<>();
for (ContentEntity entity : entities) {
ItemCardVO itemCard = new ItemCardVO();
itemCard.setItemId(entity.getItemId()); // Map product ID
itemCard.setItemTitle(entity.getTitle()); // Map product title
itemCard.setItemImg(entity.getPicUrl()); // Map product image
result.add(itemCard); // Add to result set
}
return result;
}This code shows where AI excels at template-style code generation: developers write less boilerplate and can focus more on whether business fields are mapped correctly.
But the limits of this stage are equally clear: AI only understands the current function. It does not understand module relationships, business constraints, or team conventions. As a result, it can solve for “local speed,” but not for “system-wide stability.”
Agent Coding moves AI into full-feature generation, but it also amplifies the risk of losing control
The goal of the second stage is to upgrade AI from a completion assistant to a feature agent. Developers use a single prompt to describe the background, requirements, boundary conditions, and implementation details, expecting AI to generate the full stack of controller, service, and data-layer code.
This model is especially efficient for new feature prototypes, internal tools, and loosely coupled modules. However, the problems become obvious as iteration continues: the same requirement may produce different styles across multiple runs, naming becomes inconsistent, and exception handling diverges, which gradually fragments the codebase.
Requirement: Implement a manual account adjustment feature
Background: Financial account system involving balance queries, balance updates, and transaction record insertion
Requirements:
1. Generate the API endpoint and request parameters
2. Validate that the balance never becomes negative
3. Log failures and skip the current user
4. Add test codeThis kind of prompt is already detailed, but it still cannot guarantee stable output. The missing piece is not one-time task comprehension. It is long-term project memory.
The diagram shows how AI coding evolves from isolated capabilities to systematic collaboration
AI Visual Insight: This diagram presents a staged summary of the AI coding evolution path, typically shown as a timeline or layered model. It moves from code completion to Agent Coding, then to Rules-based constraints and Specification-Driven Development, emphasizing that capability growth is not a simple linear stack. It is a gradual improvement in context management.
The essence of a Rules system is to inject a project-specific operating system into AI
When a team realizes that prompting alone cannot solve consistency problems, the most effective next step is not to keep extending prompts. It is to codify Rules. In essence, Rules make implicit team consensus explicit, including naming conventions, layered architecture, exception strategies, monetary handling, logging requirements, and security boundaries.
Once these constraints are fed into AI as structured documents, the output is no longer a “genius-style random performance.” It becomes high-quality execution within a defined framework. This is also the dividing line between experimental AI coding and production-ready AI coding.
## Example coding constraints
- Methods must not exceed 80 lines
- Always use BigDecimal for monetary values
- Do not write magic values directly into code
- Treat API inputs as low-trust by default and validate them independently
- Do not use exceptions for control flowThe value of these rules lies not only in normalizing generated output, but also in keeping code consistent across different team members and different models.
Clear division of labor between the main Agent and sub-Agents can significantly reduce context costs
AI Visual Insight: This diagram illustrates the collaboration structure between a main Agent and sub-Agents. The main Agent orchestrates the task and maintains continuous context, while sub-Agents handle search, exploration, testing, or other independent subtasks. The key design principle is to split large tasks into loosely coupled units to reduce token consumption and context pollution.
AI Visual Insight: This diagram further explains strategies for using sub-Agents, potentially including task routing, parallel execution, and result aggregation. The technical takeaway is that exploratory tasks fit independent Agents well, while debugging and modification tasks that strongly depend on context should remain in the main session.
More Rules do not automatically mean better outcomes. Overly long rule sets dilute the focus and increase context cost. In practice, teams should prioritize hard constraints that directly affect code consistency and production stability.
SDD transforms development from “write code first” to “write specifications first”
The fourth stage, SDD (Specification-Driven Development), goes a step further. It no longer asks developers to tell AI directly how to write the code. Instead, developers first define requirements, boundaries, tests, and task decomposition, and then AI implements the solution based on the specification.
This approach is highly suitable for multi-person collaboration, complex business domains, and scenarios with strict audit requirements, because the specification becomes the single source of truth. Requirements, design, testing, and code all close the loop around the same document, which can significantly reduce rework.
AI Visual Insight: This diagram explains the core concept of SDD. It typically connects “natural language ideas → specification documents → task execution → code testing” into one workflow, emphasizing that AI no longer works directly from ambiguous requirements. It develops around structured specifications.
openspec init # Initialize the project constitution and global constraints
/opsx:propose # Generate proposals and task breakdowns from requirements
/opsx:apply # Implement according to the specification
/opsx:archive # Archive proposals and retain long-term knowledgeThis command set reflects the minimum SDD workflow: define first, execute second, archive last. The result is a development process where code and documentation remain traceable.
AI Visual Insight: This diagram shows a proposal generation interface or directory structure in OpenSpec or a similar tool. It highlights how specification files, task files, and execution files are organized, showing that SDD already has practical workflow tooling support.
AI Visual Insight: This diagram shows the results of specification execution or archiving, potentially including task status, change tracking, or a structured file tree. Its technical significance lies in turning the AI development process into auditable, maintainable, and reusable assets.
Legacy projects are better served by a “Rules first, selective SDD pilot later” rollout path
From an engineering reality perspective, SDD is not suitable for immediate full-scale rollout. Legacy projects often carry historical baggage, lack formal specifications, and contain deeply coupled modules. A direct switch to specification-driven development introduces high learning and migration costs.
A more stable path is to establish unified standards with Rules first, then pilot SDD in new modules or high-value subsystems. This approach controls risk while gradually validating whether specification-driven workflows truly improve quality.
The recommended four-step implementation method fits most teams better
- Standardize foundational Rules first, including naming, layering, exceptions, logging, and testing.
- Add domain context and implementation templates for key modules.
- Introduce main-Agent and sub-Agent collaboration to reduce the cost of complex tasks.
- Pilot SDD in new modules and build a documentation-first closed loop.
FAQ structured Q&A
FAQ 1: Where should teams adopt AI coding first?
Start with scenarios that are highly repetitive and low risk, such as DTO/VO transformations, back-office CRUD, test cases, and single-method refactoring. These use cases reveal productivity gains quickly and make it easier to build team confidence.
FAQ 2: Why is it hard to produce stable, high-quality code over time with prompts alone?
Because prompts solve one-time communication problems, while Rules solve long-term context problems. Without project standards, domain knowledge, and historical constraints, AI behaves as if it is improvising inside a repository it has never seen before.
FAQ 3: Is SDD suitable for immediate adoption by every team?
No. For legacy projects and less experienced teams, SDD can be too heavy. A more practical approach is to engineer Rules first, then run targeted pilots in new modules, small teams, or scenarios that demand strong consistency.
Core Summary: This article systematically reconstructs the AI coding adoption path used by frontline engineering teams—from code completion and Agent Coding to Rules-based governance and Specification-Driven Development. It focuses on productivity gains, context gaps, uncontrolled inconsistency, and maintenance costs, while offering a practical rollout strategy for legacy projects.