Core Insights - Companies are shifting from large language models (LLMs) to smaller, specialized micro agents for improved task handling speed and accuracy [1][3][4] Group 1: Limitations of LLMs - LLMs were initially seen as versatile systems for various tasks but showed limitations in compute power, latency, and performance consistency for industry-specific applications [3] - The broad nature of LLMs often resulted in uneven results and required significant resources during high-volume periods [3] Group 2: Advantages of Micro Agents - Micro agents focus on single tasks, trained on smaller datasets, leading to reduced output inconsistency and shorter inference times [4][5] - These agents are easier to maintain and update, allowing for modular adjustments without disrupting overall operations [5] Group 3: Case Studies - CrowdStrike implemented micro agents in its security platform, achieving over 98% accuracy from approximately 80% and reducing manual analyst workload by nearly 90% [6] - PayPal utilized micro agents for various internal operations, resulting in a 50% reduction in latency and increased developer productivity [9] - Synopsys integrated agent-based tools in semiconductor design, improving workflow efficiency and consistency in design evaluations [10][11]