Best practices for building API integrations with AI agents

Since starting Nango, our team has built hundreds of API integrations using AI agents like Cursor and Claude Code. We've learned what works, what doesn't, and how to avoid common pitfalls when leveraging AI for integration development.

This guide shares our hard-won insights to help you build integrations faster and more reliably with AI assistance.

Where AI excels with integrations

AI agents have become powerful allies for integration development, but they excel in specific areas while struggling in others. Understanding these strengths is crucial for effective collaboration.

Endpoint discovery and assembly

AI performs exceptionally well at discovering and assembling API endpoints, especially for popular APIs with comprehensive documentation. When working with well-documented platforms like Salesforce, HubSpot, or Stripe, AI can quickly identify the correct endpoints and assemble the necessary API calls.

For example, when building a Salesforce contact sync, AI can efficiently navigate the Salesforce API documentation to find the /services/data/v63.0/sobjects/Contact/ endpoint and understand the required parameters.

Boilerplate code generation

AI excels at generating the foundational code structure for integrations. It can quickly create the basic scaffolding for API requests, response handling, and data processing logic. This includes setting up authentication headers, constructing request payloads, and implementing basic error handling patterns.

The generated boilerplate typically includes proper types, async/await patterns, and standard HTTP client configurations that form the foundation of any integration.

Data mapping and transformation

For straightforward data mappings where most properties exist on both the source and target schemas, AI performs well. It can identify common field patterns and create basic transformation logic between different data models.

Simple mappings like converting a created_at timestamp to a createdDate field or transforming a boolean is_active to a string status are handled effectively by AI agents.

Iterative improvement

AI works best when it can iteratively improve code based on feedback. By providing clear examples of expected output and a way to test the generated code, you enable the AI to refine its implementation through multiple iterations.

This is where tools like Nango's dryrun command become invaluable, allowing the AI to test its code against real API responses and adjust accordingly.

Where AI struggles with integrations

Despite its strengths, AI has significant limitations that require human oversight and intervention.

Endpoint hallucination

With less popular or poorly documented APIs, AI often struggles to predict the correct endpoints. It may suggest non-existent endpoints or use deprecated API versions. This is particularly problematic for niche tools or newer APIs with limited documentation.

Solution: Always provide specific endpoint documentation or links to the API's official docs in your prompts. Instruct the AI to reference the latest documentation rather than relying on its training data.

Subtle implementation errors

AI frequently makes small but critical mistakes that can break integrations. These include typos in field names, incorrect data type assumptions, missing required parameters, or improper error handling. The code may look correct but fail in production.

Solution:

Provide the AI with a way to run and test the integration against real API responses. Use tools like nango dryrun to validate the implementation and catch these subtle errors early.

Complex authentication and infrastructure

AI struggles with the nuanced details of OAuth flows, token refresh logic, and handling complex authentication scenarios. It also has difficulty designing systems that can handle concurrency, global rate limits, and other infrastructure concerns.

Solution: Use established libraries and frameworks like Nango that handle these complexities. Focus the AI on the business logic rather than the underlying infrastructure.

Human-AI collaboration strategies

The most effective approach treats AI as a highly capable assistant rather than a replacement for human judgment. Here's how to structure this collaboration effectively.

Breaking down complex integrations

Start by decomposing complex integrations into smaller, focused tasks. Instead of asking AI to build a complete CRM sync, break it down into individual operations like "sync contacts every 10 minutes" or "create opportunities when deals are updated."

AI performs best with tightly scoped tasks that have clear inputs and outputs. Use type interfaces to define these boundaries and enable the AI to validate its implementation against expected output.

Effective prompt engineering

Structure your prompts to provide maximum context and clarity.

For example, if you are using Nango for your integrations:

I need to build a Nango sync for [API_NAME] called [INTEGRATION_NAME].
## Context
- Provider: [api-name]
- Integration: [integration-name]
- Type: [sync/action]
- Test connection ID: [connection-id]
## Requirements
- **Output schema**: [describe expected data structure]
- **Field mapping**: 
  - `api_field_name` → `nango_field_name`
  - Date transformations: [specific requirements]
- **API Documentation**: [link-to-docs]
- **Data source**: [specific API endpoint(s)]
- **Error handling**: [specific requirements]
Please implement this step by step and use `nango dryrun ` to test.
  

Key elements to include:

• Clear, detailed description of business logic to implement
• Ideally, clear description how to handle known edge cases
• Detailed data model specifications
• Field mapping requirements
• Test connection ID for validation
• Links to official API documentation

Iterative development process

Work with AI through a structured iteration cycle:

1. Start simple: Begin with basic data fetching
2. Test immediately: Use `nango dryrun` to validate the implementation
3. Add complexity gradually: Introduce data transformation and validation
4. Implement error handling: Add comprehensive error management
5. Add pagination: Handle large datasets appropriately
6. Final validation: Complete end-to-end testing

Tools and setup best practices

The right tools and configuration can dramatically improve your AI-assisted development experience.

Cursor rules configuration

Implement these best practices in your Cursor rules:

# Integration Development Rules
- Avoid parallelizing API requests (prevents race conditions with rate limits)
- Persist data incrementally (don't keep large datasets in memory)
- Use dedicated mapping functions for schema transformations
- Implement comprehensive error handling for all API calls
- Validate all inputs and outputs with TypeScript types
- Test with real API responses before finalizing
  

Specialized tools for specific tasks

Leverage specialized tools to enhance AI capabilities:

• Documentation MCPs: Many platforms now offer MCP servers that allow AI to efficiently search their documentation
• JSON-to-TypeScript generators: Use dedicated libraries for type generation rather than relying on AI
• Linting tools: Provide access to linters to catch errors the AI might miss
• API testing tools: Enable the AI to make real API requests and observe responses

Integration infrastructure

Consider using purpose-built integration platform like Nango that provide:

• Pre-built API authentication
• Infrastructure to scale integrations to high data volumes & user counts
• Built-in testing tools like `dryrun` commands
• Comprehensive error handling and retry logic
• Observability and debugging capabilities
• Cursor rules with integration-specific best practices

Since Nango is open source with hundreds of reference implementations, many AI models have built-in knowledge about its patterns and best practices.

Final QA checklist

Before deploying any AI-generated integration, verify these critical elements:

Technical validation

• API endpoints are correctly implemented and tested
• Proper query parameters are used (not client-side filtering)
• All debugging code and artifacts are removed
• Input and output validation is implemented
• Error handling covers common failure scenarios
• Pagination is implemented correctly for large datasets
• Data models match the expected schema
• `dryrun` passes successfully without errors

Code quality

• TypeScript types are properly defined
• No hardcoded values or credentials
• Proper async/await patterns are used
• Memory usage is optimized for large datasets
• Rate limiting is respected

Conclusion

AI agents have transformed how we build integrations, but they work best as powerful assistants rather than autonomous developers. By understanding their strengths and limitations, providing clear context and feedback, and maintaining human oversight throughout the process, you can significantly accelerate your integration development while maintaining high quality standards.

The key is to leverage AI for what it does well: Discovering endpoints, generating boilerplate, and iterating on implementations, while handling complex infrastructure, authentication, and quality assurance through established tools and human expertise.

We hope AI will help you as much with your API integrations as it did for us.

Helpful resources

How to build integrations you and your customers love

How to consistently ship integrations fast

Why product integration infrastructure is harder than you think

Nango's official guide to leveraging AI agents