Microservices architecture has become a popular approach for building scalable, flexible, and cloud-native applications. However, adopting microservices is not just about breaking a monolithic system into smaller services—it requires careful architectural thinking, strong governance, and a clear understanding of trade-offs. With the rise of Artificial Intelligence (AI), a new dimension is being added to how microservices are designed, operated, and optimized.

This session provides a practical, experience-driven exploration of microservices architecture, enhanced by AI-driven capabilities. It covers essential best practices such as defining clear service boundaries, implementing API-first design, ensuring data ownership, and building resilient, observable systems. The session also shows how AI can enhance these practices through intelligent design recommendations, automated code generation, anomaly detection, and predictive scaling.

The talk highlights common anti-patterns—including distributed monoliths, excessive service communication, shared databases, and over-engineering—and demonstrates how AI-powered insights can help detect and mitigate these issues early. It also explores key trade-offs such as scalability versus complexity, autonomy versus governance, and consistency versus performance, while showing how AI supports data-driven architectural decisions.

Through real-world examples and enterprise use cases, attendees will gain insights into integrating AI into microservices ecosystems and improving operational efficiency. The session will also address considerations such as data quality, model reliability, and governance in AI-enabled architectures.

By the end of this session, participants will have a clear and forward-looking understanding of microservices, enabling them to design intelligent, scalable, and adaptive distributed systems.