AI-Assisted Development for Teams: 7 Steps to Get Started Safely and Efficiently

Integrating AI-powered tools like GitHub Copilot or Cursor into your team's development workflow can be a game-changer for productivity.

By now, we've all seen cool demos showing how AI coding tools can create web apps in mere seconds. However, going beyond a small demo is often another story entirely.

If you're considering adopting AI development tools in an existing stack, you need to think twice. Without a clear plan, you're likely to encounter challenges related to security, quality, and long-term maintainability. Without the right approach, AI-generated code can introduce unforeseen risks, including hidden vulnerabilities and operational disruptions that could undermine your entire development process.

This checklist outlines seven key steps to help your organization adopt AI safely and efficiently, avoiding costly and embarrassing mistakes.

Step 1: Evaluate Your AI Readiness

Adopting AI without a assessing your current development practices and toolchain lead to costly missteps. Many organizations rush into AI integration without understanding the compatibility of their current systems and infrastructure, leading to inefficiencies and security loopholes.

• ? Review your current systems and frameworks and evaluate their AI compatibility.
• ??Assess your current development practices and toolchain to identify opportunities for AI adoption.
• ? Align AI adoption goals with broader business objectives.

Step 2: Select the Right AI Tools for Your Needs

Choosing the wrong AI tools can result in compatibility issues and unexpected security vulnerabilities. Without due diligence, you risk implementing tools that expose sensitive data or fail to scale with your business needs.

• ? Research AI-powered coding assistants and evaluate their capabilities.
• ? Ensure seamless integration with your current development workflows.
• ? Verify compliance with security and regulatory requirements.

Encore’s AI-native framework and cloud platform integrates with common AI-tools like GitHub Copilot to provide a seamless and safe workflow, from code to cloud.

Step 3: Implement Code Validation and Quality Assurance

AI-generated code can introduce hard-to-detect errors and vulnerabilities that only surface in production, leading to costly incidents. Failing to validate AI-assisted contributions can jeopardize your entire software ecosystem.

• ? Set up automated testing frameworks to catch issues early.
• ? Implement strict code review processes for AI-generated contributions.
• ? Continuously monitor for security vulnerabilities.

Encore’s AI-native framework provides framework-level validation and quality assurance of all services, APIs, and infrastructure integrations, ensuring AI-generated code is standardized, secure, and production-ready.

Step 4: Establish Comprehensive Documentation Practices

Lack of proper documentation can lead to confusion and operational chaos, especially when AI generates code faster than teams can document. If knowledge retention suffers, so does long-term project quality and productivity.

• ? Implement tools like Service Catalogs to keep track of AI-generated services and APIs.
• ? Encourage developers to annotate and review AI-generated code.
• ? Develop visual architecture diagrams to improve system clarity.

Encore automatically generates comprehensive API documentation, Service Catalogs, and architecture diagrams for all AI generated code—keeping your teams aligned and informed.

Step 5: Enhance Observability and Monitoring

Introducing AI-driven development without proper observability increases the risk of adding system complexity and bugs that can lead to critical performance issues. Without robust observability, minor problems can escalate into major outages before you even realize they exist.

• ? Set up logging and tracing to monitor system performance.
• ? Continuously optimize based on performance data.
• ? Configure alerts for potential issues and anomalies.

Encore provides built-in observability tools, including tracing, offering performance insights without additional setup.

Step 6: Utilize Preview Environments to increase productivity and avoid incidents

Deploying AI-generated code directly to production without rigorous testing can lead to catastrophic failures, and deploying it to shared staging environments can lead to decreased productivity if unnecessary bugs are introduced. Preview Environments are temporary testing environments for each pull request which help mitigate these risks.

• ? Create isolated preview environments for each new pull request.
• ? Automate environment provisioning to speed up testing cycles.
• ? Minimize disruptions by keeping experimental code isolated.

Encore Cloud offers automated preview environments without any manual setup required, allowing you to test with confidence and avoid disruptions.

Step 7: Automate Infrastructure Provisioning and Deployment

Manually provisioning infrastructure for AI generated services can be difficult and time-consuming as it requires developers or DevOps teams to understand any new requirements and manually set up the necessary infrastructure. This greatly increases the risk of human error and slows down deployment cycles. AI-assisted development often requires infrastructure automation to ensure productivity and quality from development to production.

• ? Leverage codified infrastructure solutions to streamline provisioning.
• ? Ensure cloud deployments comply with internal policies.
• ? Maintain control over data and infrastructure security

Encore Cloud automates infrastructure provisioning and deployment in your cloud on AWS or GCP, using the application code as the source of truth for infrastructure requirements. Ensuring quality and security, while you retain full control.

Conclusion

By following these steps, your organization can successfully integrate AI into existing development processes while ensuring security, maintainability, and scalability. Failing to address these key areas could lead to costly mistakes, inefficiencies, and security incidents.

Ready to take the next step? Use checklist to kickstart your AI-driven development journey today.

Or, let Encore handle the complexity for you—ensuring AI adoption in your development team that is efficient, secure, and scalable. Click here to book a demo