Evaluating and Monitoring LLM Agents: Tools, Metrics, and Best Practices 0 like November 17, 2024Share this postAuthor(s): Chinmay Bhalerao Originally published on Towards AI. This blog includes the tools that you can use to monitor and assess the performance of the Agentic approachThis member-only story is on us. Upgrade to access all of Medium.Image created by author, Background image by Hollywood reporterImagine a team of virtual assistants collaborating to handle customer support queries seamlessly. Each assistant specializes in a specific task, ensuring accurate, efficient, and optimized responses. This is the essence of the agentic approach in LLMs.RAG or Retrieval-Augmented Generation pipelines are now integral parts of LLM applications. There are tools like Arize Phoenix, ragas, TrueLens, etc. that use a wide variety of metrics for the evaluation of RAGs. After the advancements in RAG pipelines, the Agentic approach has become a new approach for developing LLM applications. Everyone is eager to convert their existing or new products into agentic workflows. Its exciting to see fully capable LLMs who can interact with each other, engage in proper group chats, and collaboratively arrive at optimized and comprehensive solutions, with or without human intervention.Agents are orchestration platforms or tools in LLMs, designed to combine multiple LLMs or even with no LLMs to perform tasks with little to no human intervention. Each agent works autonomously on individual tasks but also can discuss, ask, brainstorm, and refine their work. We can use any LLM to create an Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post