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The query works fine in development with 1,000 rows. In production with 50 million rows it locks up the database for 3 minutes. One missing WHERE clause, one implicit type cast, one function wrapping an indexed column — and PostgreSQL ignores your index entirely.
Use shadow mode, statistical significance testing, and gradual rollouts to confidently replace your LLM models and prompts.
Deep dive into core agent patterns: ReAct loops, Plan-Execute-Observe, reflection mechanisms, and preventing infinite loops with real TypeScript implementations.
Master error detection, reflection prompting, alternative tool selection, human-in-the-loop escalation, and graceful degradation for production agents.
Master agent evaluation: trajectory analysis, tool accuracy, task completion rates, efficiency scoring, and LLM-as-judge evaluation frameworks.