Chapter 8: Constraint Networks—Quality Boundaries in the Agent Era

Agents are high-performance engines; constraints are the chassis and brakes. Constraint networks transform quality assurance from passive documentation into actively executed codified rules, ensuring that autonomy does not become chaos.

1 From Documentation Standards to Executable Constraints

The emergence of AI coding Agents (Cursor, Claude Code, etc.) has dramatically improved code generation throughput, but they are essentially pursuers of local optima. Without clear boundaries, Agents often write code that runs but creates logical coupling, lacks abstraction, or introduces redundancy. This “patchwork” development approach gradually plunges projects into “AI-generated spaghetti code” without anyone noticing.

This dilemma stems from several inherent Agent characteristics:

Lack of global vision is the primary problem. Agents’ context windows are limited; when implementing the 10th feature, they may have forgotten architectural principles from the 1st. Like a craftsman building a house who forgets previous design drawings with every wall—the final structure may have upright walls but a shaky overall framework.

Over-generation is another common phenomenon. To implement simple features, Agents may introduce unnecessary third-party libraries or rewrite existing utility methods. This “using a sledgehammer to crack a nut” approach quietly bloats codebases.

Path dependence makes problems worse. Agents tend to patch existing code rather than refactor when necessary. While this delivers features quickly, it accumulates technical debt rapidly.

Consistency gaps make team collaboration difficult. Different sessions and Agents may use different coding styles, naming conventions, or architectural patterns. Team members must constantly switch cognition between multiple “dialects.”

The root issue: traditional software engineering “standards” are passively followed, while Agents need actively executed boundaries.

In traditional mode, architecture standards live on Wikis, coding standards become PDF manuals, and best practices are passed down verbally during Code Review. These “rules on paper” rely on human memory and discipline, are difficult to track, lag in updates, and often become neglected decorations.

AOSE proposes a fundamental shift: constraints become part of the code. Constraints are no longer documents for humans to read, but rules for machines to execute—machine-readable, automatically executed, version-managed, and synchronously evolved. When Agents write code, these constraints run in the background like security checkpoints, ensuring only quality-compliant code passes.

This evolution parallels traffic management: from the “rule of man” era relying on driver consciousness, to the “rule of law” era of automatic execution by traffic lights and electronic monitoring. Constraint networks are the “traffic signals” of software engineering—they tell Agents when to stop, when to proceed, and which roads cannot be taken.

2 Five Dimensions of Constraint Networks

Constraint networks form a multi-dimensional quality assurance system, defining code quality boundaries from five perspectives:

Architecture Constraints

Ensure overall system structure remains intact. The most common Agent mistake is “logical overstepping”—writing SQL queries directly in Controllers, embedding business logic in Entities, or letting domain layers depend on infrastructure implementations. Architecture constraints prevent “patch-style development” from corroding system structure by defining clear layered boundaries and dependency directions.

Interface layers should only handle protocol conversion; application layers handle process orchestration; domain layers carry core business logic; infrastructure layers handle technical implementations. This “each performs its own duties” constraint maintains clear overall structure even through long-term evolution.

Complexity Constraints

Prevent code from becoming incomprehensible “spaghetti logic.” When Agents face complex requirements, they often pile conditional branches and loops in one method rather than splitting into single-responsibility functions. Complexity constraints force Agents to split rather than pile up by limiting function line counts, controlling nesting depth, and requiring single responsibility.

Security Constraints

Are non-negotiable hard thresholds. Agents sometimes ignore security risks for quick feature implementation—using string concatenation for SQL, passing user input to system internals without validation, or storing sensitive data in plaintext. Security constraints define “red lines”: all external input must be validated; all database operations must use parameterized queries; all sensitive data must be encrypted. Code violating security constraints is immediately blocked.

Performance Constraints

Transform service quality commitments into verifiable goals. Define upper limits for API response times (P99 < 100ms), lower limits for system capacity (≥1000 RPS), and resource usage quotas (single request memory < 100MB). These constraints require Agents to consider algorithm complexity, caching strategies, and database optimization while writing code.

Maintainability Constraints

Ensure team collaboration efficiency. Unify code style, naming conventions, documentation requirements, and testing standards. When multiple Agents and developers work on the same codebase, consistent style greatly reduces reading and understanding costs.

3 Governance: Human-Machine Collaborative Evolution

Unlike traditional static standards, constraint networks in AOSE form a human-machine collaborative evolution system—a “quality contract” and “education protocol” between human engineers and AI Agents.

Constraints as “Real-Time Textbooks”

In traditional software engineering, standard updates are lengthy: write documents, organize training, rely on memory and compliance. In AOSE, constraint networks have instant education characteristics—once rules update, Agents immediately learn and apply them.

This creates a new “teaching-learning” relationship:

Human identifies problem patterns → Encode as constraints → Write to constraint network
    ↑                                                              ↓
    └──── Human verifies effects ←── Agent generates code ←── Agent instant learning

For example, when teams discover Agents repeatedly using inefficient database queries in high-concurrency scenarios, engineers transform this experience into performance constraints. Unlike traditional “advocacy-compliance” models, this constraint takes effect immediately: Agents receive real-time warnings during next coding and automatically seek alternatives.

Layered Intensity Strategy

Constraint networks adopt layered intensity strategies, distinguishing different constraint levels:

Red-line constraints (blocking level)—Security vulnerabilities, data loss, system crashes. Once triggered, Agents must immediately stop, report to humans, and request guidance. Few in number but absolutely insurmountable.

Standard constraints (mandatory level)—Architectural principles, core performance indicators, key quality attributes. When violated, Agents receive clear correction prompts and automatically adjust implementation plans. This forms the main body of constraint networks.

Guidance constraints (advisory level)—Code style, implementation techniques, optimization suggestions. When violated, Agents receive prompts but can choose to ignore or improve in subsequent iterations. Used to gradually improve quality rather than instant enforcement.

This layered design allows Agents to maintain high autonomy within safety boundaries. Human teams dynamically adjust coverage based on project phase and risk tolerance.

Human-Machine Collaborative Evolution

Constraint networks are not static rules unilaterally formulated by architects, but products of continuous collaboration:

Human-led definition—Engineers define initial frameworks based on business characteristics, technology stacks, and team experience.

Agent feedback verification—Some constraints may trigger frequently, indicating systematic Agent deviations; others never trigger, suggesting over-constraint or internalized rules. This feedback helps optimize constraint rationality.

Data-driven adjustment—Analyzing trigger frequency, violation type distribution, and correction costs helps identify: which constraints are too strict? Which domains need increased coverage? Which constraints can be downgraded?

Exceptions and evolution—When constraints are unreasonable in specific scenarios, developers apply for exceptions. Frequent exceptions often mean the constraint itself needs evolution.

The art of finding balance between constraint and autonomy is the core governance challenge: giving Agents enough freedom to exercise creativity while delineating boundaries to prevent quality loss.

4 Implementation: From Rule Files to Automated Gates

Constraint networks are implemented through three core technical means:

Agent Instruction Files

Create Agent-readable behavior specification files in project roots. These serve as Agents’ “system prompts,” informing them of rules at each session’s start.

.cursorrules (Cursor) and CLAUDE.md (Claude Code) are the most common formats:

• Architecture principles: Mandatory layered patterns (e.g., “no business logic in Controllers”)
• Technology stack constraints: Specifying framework versions and libraries
• Coding style: Naming conventions, code structure preferences
• Quality requirements: Test coverage thresholds, documentation requirements
• Prohibition list: Explicitly prohibited anti-patterns

Automated Code Checking

Lint tools and static analyzers form the “automatic patrol” of constraint networks:

Coding standard checking (ESLint, Checkstyle, Ruff) ensures style consistency.

Type safety checking (TypeScript strict mode, Python type hints) catches type errors before compilation.

Architecture compliance checking (ArchUnit, Dependency-Check) verifies architectural constraints, transforming them from “verbal requirements” to “compilation errors” that Agents cannot ignore.

Code quality scanning (SonarQube, CodeClimate) detects potential defects, security vulnerabilities, and technical debt.

Process-Based Quality Gates

Pre-commit Hooks execute quick checks before submission, providing instant feedback while context is fresh.

CI/CD Pipeline gates execute comprehensive checks before merging:

• Static analysis and security scanning
• Unit and integration tests (usually requiring coverage thresholds, e.g., >80%)
• Architecture compliance tests
• Performance benchmark tests

Only when all checks pass can code merge into main branches, forming the final defense line of constraint networks.

Three-Layer Execution

These three means form a complete constraint execution chain: instruction files tell Agents “what should be done,” automated tools check “whether it was done,” process gates ensure “non-compliant is not released.”

5 Chapter Summary

In AOSE, constraint networks are the core infrastructure for quality assurance and the prerequisite for Agent autonomy.

Core Cognition:

• Agents are “high-power engines,” constraints are “chassis and brakes”
• Constraints as code—transforming from passively read documents to actively executed rules
• The essence of constraint networks is “rule of law”—transforming quality assurance from relying on individual experience to relying on system capabilities

Five Dimensions: Architecture constraints ensure system structure; complexity constraints ensure maintainability; security constraints guard risk red lines; performance constraints define SLA boundaries; maintainability constraints unify collaboration language.

Governance Principles:

• Constraints as education—instant learning, real-time feedback
• Layered intensity—red-line constraints block risks, standard constraints enforce quality, guidance constraints flexibly improve
• Human-machine collaborative evolution—human-led definition, Agent feedback verification, data-driven optimization
• Golden balance—constraints provide bottom-line assurance, autonomy stimulates creativity

Paradigm Comparison:

The more complete the constraint network, the higher Agent autonomy can be, and the lighter human supervision burden. Ultimately, teams can focus on places truly needing human wisdom—business understanding, architecture design, and innovation—while confidently entrusting coding implementation to Agents.