This paper discusses an approach centered on leveraging high-precision time synchronization to improve profiling and debugging in AI clusters. Standard profiling techniques in distributed settings often struggle with temporal inconsistencies across different nodes. Debugging servers can often be an arduous process, requiring meticulous observation and manual intervention. Batuta leverages the ReAct loop, a structured approach of thinking, acting, observing, and repeating, to autonomously. AI agent debugging tools capture, trace, and analyze every decision an autonomous agent makes, from initial input through tool selection, API calls, reasoning steps, and final output. This server enables any AI model (even those without built-in vision capabilities) to visually inspect web pages, find UI bugs, test user workflows, and validate. Modern AI models are data-hungry, computation-heavy beasts that need specialized hardware just to function, let alone perform at their best. That's the job of an AI server—a custom-built system that keeps AI applications fast, scalable, and efficient. Latency spikes become SLA breaches. And if your monitoring and observability game is weak? You're flying blind into a storm. Get Your Head Around the Core.