Observability

Understanding Agentic RAG, Choosing the Right tool for RAG Observability and Why Maxim Leads

Understanding Agentic RAG, Choosing the Right tool for RAG Observability and Why Maxim Leads

TL;DR Agentic RAG adds autonomous reasoning to retrieval-augmented generation, and it demands rigorous observability: Maxim AI leads with unified simulations, RAG evaluation, and observability for AI reliability in production. Agentic RAG blends retrieval-augmented generation with ai agents that plan, call tools, and adapt based on outcomes. The challenge is

Top 5 Tools for AI Agent Observability in 2025

Top 5 Tools for AI Agent Observability in 2025

TL;DR * Maxim AI: End-to-end platform for simulations, evals, and observability; built for cross-functional teams. Maxim AI * LangSmith: Tracing, evals, and prompt iteration; works with or without LangChain. LangSmith * Arize: Enterprise-grade evaluation and OTEL-powered tracing with online evals and dashboards. Arize platform * Langfuse: Open-source LLM observability with multi-modal tracing and

Top 5 Tools to Ensure RAG Observability

Top 5 Tools to Ensure RAG Observability

What is RAG Observability and why is it important? RAG observability is the practice of monitoring and debugging retrieval-augmented generation systems across the entire flow: query intake, retrievals, LLM generations, and final responses. It matters because RAG pipelines combine external context with model outputs, making failures harder to diagnose than

7 Important metrics to track for AI Agent Observability

7 Metrics You Should Track for AI Agent Observability

TL;DR This articles talks about the seven core metrics you should track for AI Agent Observability: Step Completion, Step Utility, Task Success, Tool Selection, Toxicity, Faithfulness, and Context Relevance at session, trace, and span levels. Instrument distributed tracing, attach automated evals with human review where needed, and correlate quality

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.

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

How to Observe Your RAG Applications in Production: A Comprehensive Guide with Code Examples

How to Observe Your RAG Applications in Production: A Comprehensive Guide with Code Examples

Introduction Retrieval-Augmented Generation (RAG) systems have become foundational in enterprise AI, combining retrieval from proprietary knowledge-bases with large language model generation to deliver grounded, contextual answers to queries. As these systems become mission critical and deeply embedded in enterprise workflows, the challenge shifts from just building functional systems to ensuring