AI Agents are Useless Without Observability and Cost Controls

AI agents are the shiny new toy, promising to automate everything from coding to customer service. But behind the hype lies a harsh reality: without robust observability and strict cost controls , these agents are more likely to become expensive headaches than productive assets. The dream of multi-agent workflows often crashes against the rocks of missing structure and non-deterministic behavior. You can't monitor these systems like traditional software. Inputs are infinite. Quality is subjective, residing in the nuances of conversation. As Github points out, a lack of structured engineering patterns is a prime cause of failure. Observability isn't just about logging errors. It's about understanding how your agent reasons. It's about capturing production traces to fuel continuous improvement. You need to see the entire chain of thought, the decisions made at each step, and the context that influenced those decisions. Without this level of granular insight, debugging is a nightmare, and