Distributed tracing is crucial for modern observability, offering richer context than logs. However, the volume of tracing data can be overwhelming. Sampling addresses this by selectively retaining data, with two main approaches: head sampling (deciding upfront) and tail sampling (deciding after collecting all spans). Head sampling is simpler but can miss localized issues. Tail sampling, while more accurate, is complex to implement at scale, requiring buffering, stateful processing, and potentially impacting system resilience. Furthermore, sampling inherently affects the accuracy of RED metrics (request rate, error rate, duration), necessitating metric materialization *before* sampling.
This article introduces agentic TRACE, an open-source framework designed to build LLM-powered data analysis agents that eliminate data hallucinations. TRACE shifts the LLM's role from analyst to orchestrator, ensuring the LLM never directly touches the data. All computations are deterministic and executed by code, using the database as the single source of truth. The framework emphasizes auditability, security, and the ability to run effectively on inexpensive models. The author provides examples and a quick start guide for implementing TRACE, highlighting its potential for building verifiable agents across various data domains.
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