Paul Sanglé-Ferrière, co-founder of cubic, discusses the importance of context engineering in AI code review, emphasizing how cubic helps companies like n8n and Granola improve code shipping speed by 28%. Context engineering involves managing the information an AI system accesses to make reliable decisions, crucial for handling large codebases when AI can only process limited tokens at a time. Sanglé-Ferrière shares insights on allowing AI to determine necessary context, selecting appropriate AI models and tools, and addressing production challenges such as rate limits and parallel execution. He highlights the necessity of fast iteration and observability for debugging and improving AI systems. cubic leverages Inngest for durable execution, flow control, and full observability, ensuring reliable context-pulling architecture in production, thus supporting efficient AI workflows and infrastructure critical for successful AI implementations.