Kamya Shah

Kamya Shah

Scneraio-based Testing: Maxim's Test Suite for Reliable, Production-Ready AI Agents

Scenario-Based Testing: Maxim’s Test Suite for Reliable, Production-Ready AI Agents

TL;DR Scenario-based testing makes AI agents reliable by validating behavior across realistic, multi-turn conversations, diverse personas, tools, and context sources. Traditional testing falls short because agents are non-deterministic, and multi-turn conversational. Maxim AI provides an end-to-end platform: Prompt IDE, agent simulations, unified evaluators, and enterprise observability to scale evaluations,

Best framework for RAG Observability

Best framework for RAG Observability

TL;DR Maxim AI offers the most comprehensive framework for RAG observability, combining end-to-end tracing, evaluation, and monitoring for AI agents. Its full-stack platform enables technical teams to debug, monitor, and optimize retrieval-augmented generation (RAG) systems with reliability, and speed. Sign up for Maxim AI to experience best-in-class RAG observability.

AI Agent Quality Assurance with Maxim: A Comprehensive Guide

AI Agent Quality Assurance with Maxim: A Comprehensive Guide

TL;DR Maxim AI delivers a full-stack platform for AI agent quality assurance, combining simulation, evaluation, and observability to help engineering and product teams ship reliable, high-performing agentic applications. This guide explains how Maxim’s tools enable robust agent monitoring, debugging, and continuous improvement, with actionable insights and best practices

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