Kamya Shah

Kamya Shah

7 Key features of Agent Observability

7 Key features of Agent Observability

TL;DR Agent observability is essential for building reliable, high-quality AI applications. This blog explores seven foundational features that empower engineering and product teams to monitor, debug, and optimize AI agents at scale. From distributed tracing to automated evaluations, discover how Maxim AI’s observability suite delivers actionable insights and

LLM-as-a-Judge in Agentic Applications: Ensuring Reliable and Efficient AI Evaluation

LLM-as-a-Judge in Agentic Applications: Ensuring Reliable and Efficient AI Evaluation

TLDR LLM-as-a-Judge is an automated evaluation technique that uses large language models to assess and score the outputs of other models. This scalable approach enables nuanced, and rapid evaluations, outperforming traditional metrics and manual review in both speed and depth with scale by reading, reasoning about, and justifying scores across

Session‑Level Observability: Tracking Multi‑Turn Conversations at Scale

Session‑Level Observability: Tracking Multi‑Turn Conversations at Scale

TL;DR Session-level observability is essential for tracking multi-turn conversations in modern AI applications. By monitoring interactions at the session level, teams can pinpoint issues, improve agent reliability, and ensure high-quality user experiences. Maxim AI offers comprehensive tools for session-level observability, enabling technical teams to monitor, evaluate, and optimize multi-turn