Is your tech stack AI ready?

Integrating AI capabilities into existing software systems fundamentally changes how we approach software engineering. This talk explores the engineering practices, architectural considerations, and operational challenges that development teams face when their tech stacks need to support AI-powered features.

Drawing from real-world experiences at CultureAmp and Appear, this session will cover the practical engineering challenges of preparing existing systems for AI integration:

Engineering Practice Evolution:

• How Model Context Protocols (MCPs) change service integration patterns - their security models, access scoping, API design for LLM consumption, and documentation requirements
• Adapting traditional software engineering practices for non-deterministic systems
• New testing and evaluation frameworks needed for AI-integrated applications
• Code review processes when dealing with prompt engineering and model interactions

Operational Engineering Challenges:

• Observability patterns for agentic systems - monitoring, logging, and debugging non-deterministic behaviors
• Durability and reliability engineering when dealing with external AI services
• Performance engineering considerations for latency-sensitive AI integrations
• Error handling and graceful degradation patterns

Architectural Impact:

• How existing microservices architectures adapt to support AI workflows
• Data pipeline engineering for model feeding and result processing
• Security engineering practices for AI-integrated systems

This talk focuses on the engineering discipline changes rather than product development, examining how traditional software engineering practices evolve when AI becomes a core component of your technical architecture. Attendees will learn practical strategies for evolving their engineering practices and technical systems to successfully integrate AI capabilities.