Traditional software development relies on deterministic processes, whereas AI systems are probabilistic and deal with probabilities, patterns, and distributions rather than fixed rules. This shift in approach requires thinking about confidence levels and anticipating uncertainty when designing AI applications. The right tool for the job is crucial, as different functions suit themselves well to specific AI applications. Design, software architecture, and anything new should be left to people, while agents can autonomously execute complex tasks that traditionally require human oversight. Dagster's abstractions make building production-grade AI implementations more manageable by providing robust frameworks for ETL processes, model training, inference, versioning, testing, and local development. The platform's emphasis on lineage and graph-based thinking helps teams focus on outputs while maintaining visibility into the entire data pipeline.