How to Create Custom AI Agents for Your Specific Codebase

Generic AI tools are great, but custom AI agents trained on your specific codebase can be even better. They understand your patterns, conventions, and domain logic.

This guide shows you how to create custom AI agents.

Why Custom Agents?

Generic AI Limitations

* Doesn't know your internal APIs
* Doesn't understand your business logic
* Suggests patterns that don't match your style
* Requires constant context reminders

Custom Agent Benefits

* Understands your codebase structure
* Knows your internal conventions
* Suggests code that matches your patterns
* Requires less prompting

Approach 1: Enhanced `.cursorrules`

The simplest “custom agent” is a comprehensive `.cursorrules` file.

Example: E-Commerce Platform

“`markdown

Project: ShopFlow E-Commerce Platform

Architecture

– Microservices: Auth, Products, Orders, Payments, Notifications
– Event-driven communication via RabbitMQ
– PostgreSQL for data storage
– Redis for caching
– Next.js frontend

Internal APIs

– Auth Service: `POST /api/auth/login`, `POST /api/auth/register`
– Product Service: `GET /api/products`, `GET /api/products/:id`
– Order Service: `POST /api/orders`, `GET /api/orders/:userId`

Coding Conventions

– Use TypeScript strict mode
– All API responses follow: `{ success: boolean, data?: any, error?: string }`
– Use Zod for validation
– Use Prisma for database access
– Use tRPC for type-safe API calls

Business Logic

– Orders can only be placed if product is in stock
– Payment must be processed before order is confirmed
– Inventory is reserved when order is placed, deducted when confirmed
– Users can cancel orders within 24 hours

Common Patterns

Creating a new API endpoint

1. Define Zod schema for validation
2. Create tRPC procedure
3. Implement business logic
4. Add error handling
5. Write tests

Adding a new database table

1. Update Prisma schema
2. Run migration
3. Update TypeScript types
4. Create repository functions
5. Write tests
“`

With this file, the AI understands your entire system.

Approach 2: RAG (Retrieval-Augmented Generation)

RAG allows the AI to search your documentation and codebase dynamically.

Tools for RAG

* Cursor's `@Codebase`: Built-in RAG
* Windsurf's Cascade: Deep context awareness
* Google Antigravity: Massive context window

Setting Up RAG

1. Create Documentation:
“`markdown

docs/architecture.md

docs/api-reference.md

docs/coding-standards.md

docs/deployment.md

“`

2. Reference in Prompts:
> “@docs/architecture.md I need to add a new microservice for recommendations. Follow the existing architecture pattern.”

Approach 3: Fine-Tuning (Advanced)

For ultimate customization, fine-tune a model on your codebase.

When to Fine-Tune

* Large codebase (100,000+ lines)
* Unique domain (finance, healthcare, etc.)
* Proprietary patterns
* Budget for training ($1,000+)

Process

1. Collect Training Data:
Extract code examples from your repo:
“`bash

Get all TypeScript files

find . -name “.ts” -not -path “/node_modules/*” > training_data.txt
“`

2. Format for Training:
Create prompt-completion pairs:
“`json
{
“prompt”: “Create a user authentication endpoint”,
“completion”: “[Your actual auth code]”
}
“`

3. Fine-Tune:
Use OpenAI's fine-tuning API or similar.

4. Deploy:
Use the fine-tuned model in your IDE.

Approach 4: Custom GPTs (OpenAI)

Create a custom GPT with your documentation.

Steps

1. Go to chat.openai.com
2. Click “Explore GPTs” > “Create a GPT”
3. Upload your documentation
4. Configure instructions:

“`
You are a coding assistant for the ShopFlow e-commerce platform.

You have access to:
– Architecture documentation
– API reference
– Coding standards

When generating code:
– Follow the patterns in the documentation
– Use TypeScript with strict mode
– Follow the API response format
– Include error handling
“`

5. Use in your workflow

Approach 5: Local LLMs (Privacy-Focused)

For sensitive codebases, run AI locally.

Tools

* Ollama: Run LLMs locally
* LM Studio: GUI for local models
* Continue.dev: VS Code extension for local AI

Setup with Ollama

“`bash

Install Ollama

curl https://ollama.ai/install.sh | sh

Download a model

ollama pull codellama

Run

ollama run codellama
“`

Integrate with Cursor

Cursor can connect to local models via API.

Real-World Example: Building a Custom Agent

Scenario

A fintech company with:
* Complex regulatory requirements
* Proprietary trading algorithms
* Strict security standards

Solution

1. Create Comprehensive Docs:
“`markdown

docs/regulations.md

– All financial calculations must use Decimal type (not float)
– All transactions must be logged for audit
– PII must be encrypted at rest

docs/algorithms.md

– Trading algorithm patterns
– Risk calculation formulas
– Portfolio optimization methods
“`

2. Enhanced `.cursorrules`:
“`markdown
– Never use float for money calculations
– Always log transactions to audit table
– Encrypt PII fields using our encryption library
– Follow FINRA regulations for trading
“`

3. Custom Validation:
Create a pre-commit hook that checks AI-generated code:
“`bash

Check for float usage in financial code

if grep -r “parseFloat\|Number(” src/trading/; then
echo “Error: Use Decimal type for financial calculations”
exit 1
fi
“`

Measuring Custom Agent Effectiveness

Metrics

1. Accuracy: Does it generate correct code?
2. Consistency: Does it follow your patterns?
3. Speed: Does it reduce prompting time?
4. Adoption: Do developers use it?

A/B Testing

Run an experiment:
* Group A: Uses generic AI
* Group B: Uses custom agent

Measure:
* Time to complete tasks
* Code quality
* Developer satisfaction

Conclusion

Custom AI agents are the next evolution of Vibe Coding. They transform AI from a generic assistant into a team member who understands your specific context.

At BYS Marketing, we build custom AI agents for our clients. This allows us to deliver code that matches their exact standards and patterns.

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Want a custom AI agent for your codebase?
Contact BYS Marketing. We'll build an AI assistant tailored to your needs.