Every engineering team has its own way of building software. There are agreed patterns for handling dependencies. APIs return errors in a consistent structure. Database queries follow a defined style. Over time, these standards make the codebase predictable and easier to maintain.

Then an AI assistant generates code that ignores those patterns. It creates objects directly instead of following your dependency setup. It returns a different error format. It writes queries in a style no one on your team uses.

The result isn’t broken code. It’s inconsistent code. And that inconsistency adds friction to every review.

This keeps happening because generic AI tools are trained on public repositories from everywhere. They suggest the most common pattern they’ve seen, not the one your team has agreed to follow. Without guidance, they default to average practices instead of your standards.

TLDR

Configure AI coding assistants to enforce team standards through:

• Custom agents in plain English that analyze code against your specific rules during every PR

• Syncing with existing tools like Cursor rules to maintain consistency between the IDE and review

• Community-proven agents from successful teams that you can adopt and customize

• Learning from team feedback to reduce false positives by 51% over time

• Multi-repository enforcement applies the same standards across all your projects

AI coding tools like cubic allow up to 5 custom agents per repository on Starter and Team plans, with Pro and Enterprise plans offering more.

Why generic AI doesn't follow your team's patterns

AI trained on public repositories learns common patterns. But "common" doesn't mean "right for your team."

The training data problem

If most repositories use async/await but your team prefers Promises with .then() for specific architectural reasons, AI will suggest async/await. Not because it's better for your system, but because it's more common in the training data.

Your team might have valid reasons for your patterns. Maybe your error handling strategy works better with .then() chains. Maybe your architecture team decided on specific patterns after careful evaluation.

Generic AI doesn't know this context.

What happens without configuration

Without configured agents, every AI-generated pull request needs manual cleanup:

• Renaming variables to match team conventions.

• Restructuring code to fit architectural patterns.

• Rewriting error handling to use team standards.

• Moving files to follow team organization.

How custom coding agents enforce team standards

Custom coding agents are AI systems configured to understand and enforce your specific patterns.

Plain English rule definitions

The most effective approach is to define rules in plain English that agents can understand and apply.

Example custom agent for API standards:

Name: API Response Format Validator Description: All API endpoints must return errors in the format { error: { code: string, message: string, details?: object } }. Success responses must include a data field. Never expose internal error details in API responses.

Example custom agent for architecture:

Name: Dependency Injection Enforcer
Description: All service classes must use dependency injection through constructor parameters. Never use direct class instantiation with 'new'. Services should be registered in the DI container in /config/dependencies.ts.

Example custom agent for database access:

Name: ORM Query Pattern ValidatorDescription: Database queries must use the TypeORM query builder. Raw SQL is only allowed in /migrations. All queries must include error handling and use transactions for multiple operations.

These agents run on every pull request, flagging violations before human reviewers see the code.

How agents analyze code

Custom agents work through specialized analysis:

• Planner agents coordinate overall review, identifying which specific agents should examine each code change.

• Security agents check for vulnerabilities based on your team's security standards.

• Architecture agents enforce layer boundaries and dependency rules specific to your system.

• Code quality agents validate formatting, naming, and organizational patterns.

This multi-agent approach catches violations that single-model AI assistants miss.

Setting up custom agents in cubic

cubic provides a straightforward process for configuring team-specific agents.

Creating your first custom agent

Each repository supports up to 5 custom agents. Start with the pattern that causes the most review friction.

Step 1: Identify the problem

What conventions do you constantly correct during code review? Common examples:

• Error handling format inconsistencies.

• Architectural boundary violations.

• Security patterns are not being followed.

• Database access patterns.

Step 2: Write the agent description

Use plain English to describe what code should follow:

Name: React Component Standards Description: Functional components only, no class components. Use hooks for state and effects. Props interfaces must be defined with TypeScript. Event handlers should be prefixed with 'handle' (handleClick, handleSubmit). Components over 200 lines should be split into smaller components.

Step 3: Test the agent

Open a pull request that violates the rule. Verify the agent catches it and provides clear feedback.

Step 4: Refine based on feedback

If the agent creates false positives, refine the description. If it misses violations, add more specific guidance.

Importing rules from Cursor

Teams using Cursor already have .cursorrules files defining standards. cubic can sync these directly.

Click "Sync Cursor Rules" and cubic imports your existing conventions. This maintains consistency between what AI suggests in your IDE and what gets enforced during code review.

Example Cursor rule that syncs:

# API Design Standards

- All API endpoints must return errors in the format: { error: { code: string, message: string } }

- Use dependency injection for all service classes

This becomes a cubic agent that enforces the same standards during PR review.

Using community agents

cubic maintains a library of proven agents from successful teams. Instead of writing rules from scratch, adopt patterns that work.

Popular community agents:

• React best practices.

• TypeScript strict patterns.

• API security standards.

• Database query optimization.

• Test coverage requirements.

You can adopt these as-is or customize them for your specific needs.

Configuring agents for different scenarios

Not all code needs the same level of enforcement. Configure agents based on context.

Production code vs experimental code

Production repositories:

• Enable all 5 custom agents.

• Strict enforcement of security and architecture rules.

• Block merges on violations.

Experimental repositories:

• 2-3 core agents for critical patterns.

• Warning-only mode for most violations.

• Allow team discretion on enforcement.

Critical paths vs general code

Configure stricter agents for code touching:

• Authentication and authorization.

• Payment processing.

• Data privacy and PII handling.

• External API integrations.

Example critical path agent:

Name: Payment Code Security Validator Description: Code in /services/payment must validate all monetary amounts for overflow. Use the Decimal type for currency; never float. All payment operations must be wrapped in database transactions. Log all payment events with an audit trail. Never log credit card numbers or CVV codes.

Framework-specific patterns

Different frameworks need different agents.

React projects:

Name: React Hooks Pattern Validator Description: Use useState for component state. Use useEffect for side effects with proper cleanup. Custom hooks must start with 'use'. Never mutate state directly; always use setter functions.

Python projects:

Name: Python Type Hint Enforcer Description: All public functions must have type hints for parameters and return values. Use Optional[T] for optional parameters. Import types from the typing module. Use dataclasses for data structures instead of dictionaries.

Learning from team code review patterns

The most powerful configuration evolves based on actual team feedback.

How feedback reduces false positives

When developers mark an agent's finding as "not an issue," the system learns. cubic reduces false positives by 51% compared to earlier tools through this feedback loop.

Example learning cycle:

1. Agent flags a 40-line function as too long.

2. The reviewer marks it as acceptable for this specific module.

3. System learns that longer functions in this module type are okay.

4. Future reviews account for this context.

Team-specific preferences vs hard rules

Configuration files define rules. Learning systems discover preferences.

Hard rule: "Use dependency injection." Learned preference: "In /scripts directory, direct instantiation is acceptable for one-off utilities."

The agent learns these nuanced preferences from how your team actually reviews code.

Multi-repository configuration

Apply the same standards across all your team's repositories automatically.

Organization-wide agents

Security standards, coding conventions, and architectural patterns that apply company-wide can be configured once and applied across the company.

Example organization agent:

Name: Company Security Standards Description: Never commit API keys, passwords, or tokens. Use environment variables for secrets. All external API calls must include timeout handling. Log security events to the central audit system.

This agent runs on every repository in your organization, preventing each team from developing different security practices.

Repository-specific customization

While organization agents enforce company standards, repository-specific agents handle project conventions.

A microservice might have strict API versioning rules. A CLI tool might have different patterns. Each repository adds 2-3 custom agents for its specific needs while inheriting organization-wide agents.

Integrating with CI/CD for enforcement

Configuration means nothing without enforcement. Integrating into CI/CD makes standards automatic.

Automated enforcement

Configure whether violations block merges or just warn:

Blocking enforcement: Security agents, architecture boundary violations, critical patterns Warning enforcement: Code quality suggestions, optimization opportunities, refactoring recommendations

Quality gates

Set thresholds for automated enforcement:

• Block merge if security violations > 0.

• Warn if code complexity exceeds team standards.

• Require manual approval override for architectural changes.

This ensures configured standards are followed, not just suggested.

Measuring the configuration effectiveness of coding agents

Track metrics to know if your configuration works.

1. False positive rate

How often do agents flag code that's actually fine? Target below 15%.

If false positives exceed 20%, your agent descriptions are too vague or overly strict.

2. Code review cycle time

Configured agents should reduce review cycles by automatically catching issues.

Target: 30-40% reduction in review cycles after configuration.

3. Standards compliance

What percentage of merged code follows team standards?

Track before and after configuring agents. Target: 80%+ compliance with key standards.

Making coding agents work for your team

Generic AI is powerful but generic. Custom agents transform them into team-specific tools that understand your conventions.

Effective configuration combines plain English agent descriptions, syncing with existing tools like Cursor, adopting community-proven patterns, learning from team feedback, and multi-repository management

Choosing the right LLM matters less than proper configuration. Well-configured agents using older models outperform poorly configured tools using the latest models.

Start with core standards that cause the most review friction. Add agents incrementally. Measure effectiveness. Iterate based on team feedback.

Ready to configure agents that enforce your team's standards? 

Book a demo of cubic and watch custom agents validate every pull request against your architecture, security, and coding conventions.