An experiment tested an AI agent's ability to optimize a slow PostgreSQL query across three different database services: Supabase, Neon, and Postgres MCP Pro. The agent, powered by Claude Code on Claude Opus 5, was tasked with making a 34-second query at least 10 times faster. While the agent eventually produced an optimized query that ran in under a fifth of a second, the process was inconsistent and inefficient, involving multiple runs of the original slow query, unnecessary index creation, and extensive result verification. The use of a tool called Baton, which records the agent's intents, proved crucial in understanding the agent's decision-making process and identifying these inefficiencies. AI
IMPACT Understanding AI agent behavior and decision-making is crucial for improving their reliability and efficiency in complex tasks.
RANK_REASON The article details the use and effectiveness of a specific tool (Baton) for observing and understanding AI agent behavior in a practical application.
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