llm/9b2efe03-4d9e-4db2-a79a-13cee83b17d6/topic-13-86dcb7e6-3ac8-4d06-b622-073e479f2195-output.json
Instead of drowning LLMs in raw logs, a more efficient strategy involves converting database results into in-memory parquet dataframes paired with token-optimized summary views. This approach allows agents to intelligently "drill down" into massive datasets without excessive iterative query pressure, resulting in faster reasoning and interactive, notebook-style outputs for incident response. Furthermore, there is a compelling push to shift data handling in protocols like MCP from standard text to high-performance binary formats like Apache Arrow. By adopting these optimized structures, agentic harnesses can more effectively manage the scale and complexity of modern observability tasks.