Mastra hosts agent eval loop masterclass

Mastra is making the eval loop feel like standard engineering hygiene for agents, connecting observability directly to testing and release workflows. Production failures become reusable regression cases instead of disappearing into logs. Dataset experiments compare prompts, models, and tools with per-item scores, errors, traces, timing, and token usage. CI gates can turn scorer thresholds into pass/fail checks before changes merge. The approach is strongest when teams combine deterministic checks with carefully designed custom scorers; noisy LLM judges alone can create false confidence. Keeping traces, datasets, evals, and agent code in one TypeScript ecosystem should reduce integration work for Mastra users.