Evaluating and Monitoring LLM Agents: Tools, Metrics, and Best Practices

Author(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 approach

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Imagine 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. It’s 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.

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Published via Towards AI