Automated tests miss the subtle quality issues that make AI deployments dangerous. Knight Capital lost hundreds of millions in 45 minutes from one deployment bug. Here is how to build LLM deployment pipelines that combine automated safety checks with human judgment, using golden datasets and canary deployments to prevent production disasters.
LLMOps success depends more on proven operations discipline than AI-specific tooling. With over 40% of agentic AI projects facing cancellation by 2027, the teams that survive apply Google SRE principles to LLM infrastructure rather than treating it as something that needs special handling.
Production AI systems fail when prompts lack version control. Teams building reliable AI use Git workflows with tools like Helicone and Braintrust for automated prompt testing, code review, and staged deployment. HBR reported that more than 40% of agentic AI projects risk cancellation partly due to undisciplined prompt management.