Operations | Monitoring | ITSM | DevOps | Cloud

Fabrix.ai, a pioneer in enterprise-ready agentic AI solutions, successfully hosted its highly anticipated Agentic AI Demo Day yesterday, bringing together IT operations, NOC operations, and AI operations professionals for a comprehensive showcase of its Purpose-built Agentic AI Operational Intelligence Platform.

There is a strong push for adopting Kubernetes’ base Agentic Framework, given the adoption for containers and the similarities between containers and agents ( agents are stateful though).

In the world of large language models (LLMs), two approaches have dominated how we adapt AI to specific use cases: Retrieval-Augmented Generation (RAG) and Fine-Tuning. But the landscape is rapidly evolving with advanced techniques like MoE, LoRA, and GRPO. Let’s explore how these approaches compare and combine to create more powerful AI systems.

Several open-source standards have emerged for AI agents and agentic frameworks, aiming to improve interoperability and standardization in the rapidly evolving field of AI.

GRPO is a new reinforcement learning technique that replaces traditional methods like Proximal Policy Optimization (PPO) DeepSeek’s Group Relative Policy Optimization (GRPO) represents a paradigm shift in reinforcement learning (RL) for large language models, addressing key limitations of Proximal Policy Optimization (PPO) through innovative simplifications and efficiency gains. Here’s why GRPO stands out.