What are best development practices in AI automation and how do you incorporate them into your AI automation workflow development?

Best development practices, as we understand them, emphasize efficiency, reliability, and security.

Best development practices in AI automation workflows are very similar to those in traditional software development. They include defining clear objectives, identifying suitable use cases, developing well-documented processes, ensuring data security and privacy, automated testing, automated quality gates and deployemnts, training and upskilling employees, and monitoring and evaluating the performance of the workflows. Organizations should also be prepared to adapt and refine their workflows based on feedback and changing requirements.

• We adopt an Agile methodology, breaking down complex projects into smaller, manageable tasks and iterating on them quickly. This allows us to deliver value to our clients faster and adapt to changing requirements. This keeps the project scope focused to ensure timely and effective delivery.

• We avoid "reinventing the wheel" by leveraging existing, proven technologies and solutions where appropriate.

• We prioritize maintainablity and scalability, ensuring that our AI automation workflows can be easily updated and expanded as needed. This includes using modular design principles, documenting our code and processes thoroughly, ensuring code is clean and readable, and striving to keep our solutions simple and flexible.

• We do not write code unnecessarily. We utilize low-code/no-code tools and pre-built components when suitable to speed up development and reduce the risk of errors.

• We believe in keeping a "human in the loop" for critical decision-making and oversight, especially for sensitive processes. This helps ensure that our AI automation workflows remain ethical, compliant, and aligned with business goals, and helps employees understand and trust the technology.

• A continuous "feedback loop" is essential for improvement and learning, allowing us to refine our processes and the AI agents we build. An example of this is when the AI agent saves a database record of any mistakes or errors, and then checks the database for similar mistakes before making decisions. The buildup of these records over time allows the AI agent to learn from its mistakes and improve its performance. These learnings can be consolidated and shared with other AI agents to improve their performance as well.

• Finally, "security first" is a paramount principle, influencing every stage of our development process, from data handling to deployment. See more on our security practices.