Agent Drift in Multi-Agent LLM Systems: Why Your AI Teams Fail Over Time & How to Fix It
1 months ago 高效码农Agent Drift in Multi-Agent LLM Systems: Why Performance Degrades Over Extended Interactions Core question this article answers: Why do multi-agent large language model (LLM) systems gradually lose behavioral stability as interactions accumulate, even without any changes to the underlying models, and how severe can this “agent drift” become in real-world deployments? Multi-agent LLM systems—built on frameworks like LangGraph, AutoGen, and CrewAI—are transforming enterprise workflows by breaking down complex tasks across specialized agents that collaborate seamlessly. These systems excel at code generation, research synthesis, and automation. However, a recent study highlights a critical, often overlooked issue: agent drift, the progressive degradation …
DeepEval: Revolutionizing LLM Evaluation Frameworks with Open-Source Precision
8 months ago 高效码农DeepEval: Your Ultimate Open-Source Framework for Large Language Model Evaluation In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) are becoming increasingly powerful and versatile. However, with this advancement comes the critical need for robust evaluation frameworks to ensure these models meet the desired standards of accuracy, relevance, and safety. DeepEval emerges as a simple-to-use, open-source evaluation framework specifically designed for LLMs, offering a comprehensive suite of metrics and features to thoroughly assess LLM systems. DeepEval is akin to Pytest but is specialized for unit testing LLM outputs. It leverages the latest research to evaluate LLM outputs …