Microservices
-
Enhancing Multi-Agent System Reliability
Read Full Article: Enhancing Multi-Agent System Reliability
Managing multi-agent systems effectively requires moving beyond simple chatroom-style collaborations, which can lead to issues like politeness loops and non-deterministic behavior. Treating agents as microservices with a deterministic orchestration layer can improve reliability, especially in local setups. Implementing hub-and-spoke routing, rigid state machines, and a standard Agent Manifest can help streamline interactions and reduce errors. These strategies aim to enhance the efficiency and reliability of complex workflows involving multiple specialized agents. Understanding and implementing such structures is crucial for improving the scalability and predictability of multi-agent systems.
-
Autoscaling RAG Components on Kubernetes
Read Full Article: Autoscaling RAG Components on KubernetesRetrieval-augmented generation (RAG) systems enhance the accuracy of AI agents by using a knowledge base to provide context to large language models (LLMs). The NVIDIA RAG Blueprint facilitates RAG deployment in enterprise settings, offering modular components for ingestion, vectorization, retrieval, and generation, along with options for metadata filtering and multimodal embedding. RAG workloads can be unpredictable, requiring autoscaling to manage resource allocation efficiently during peak and off-peak times. By leveraging Kubernetes Horizontal Pod Autoscaling (HPA), organizations can autoscale NVIDIA NIM microservices like Nemotron LLM, Rerank, and Embed based on custom metrics, ensuring performance meets service level agreements (SLAs) even during demand surges. Understanding and implementing autoscaling in RAG systems is crucial for maintaining efficient resource use and optimal service performance.