This article details how HPE is addressing operational fatigue and burnout in IT teams through the introduction of agentic AI operations. HPE's new system utilizes skills-based AI agents that work alongside human operators to reduce alert noise, improve response times, and cut root cause analysis time by at least half, according to early adopters.
The focus is on augmenting human capabilities rather than replacing them, with a strong emphasis on auditability, transparency, and human oversight in AI-driven actions. The system aims to break down data silos and provide proactive insights to prevent issues before they escalate.
The focus is on augmenting human capabilities rather than replacing them, with a strong emphasis on auditability, transparency, and human oversight in AI-driven actions. The system aims to break down data silos and provide proactive insights to prevent issues before they escalate.
This article introduces agentic TRACE, an open-source framework designed to build LLM-powered data analysis agents that eliminate data hallucinations. TRACE shifts the LLM's role from analyst to orchestrator, ensuring all computations are deterministic and data-driven. The framework achieves this by having the LLM work with metadata instead of raw data, relying on the database as the source of truth, and providing a complete audit trail. Example use cases demonstrate the system's ability to deliver verifiable results on inexpensive models like Gemini 3.1 Flash Lite. The author provides a quick start guide and encourages contributions to the project.
This article introduces agentic TRACE, an open-source framework designed to build LLM-powered data analysis agents that eliminate data hallucinations. TRACE shifts the LLM's role from analyst to orchestrator, ensuring the LLM never directly touches the data. All computations are deterministic and executed by code, using the database as the single source of truth. The framework emphasizes auditability, security, and the ability to run effectively on inexpensive models. The author provides examples and a quick start guide for implementing TRACE, highlighting its potential for building verifiable agents across various data domains.
Sarvam AI is releasing Sarvam 30B and Sarvam 105B as open-source models, trained from scratch on large-scale, high-quality datasets. These models demonstrate strong reasoning, programming, and agentic capabilities, with optimizations for efficient deployment across various hardware. Sarvam 30B powers Samvaad, while Sarvam 105B powers Indus. The release includes details on the model architecture, training process, benchmark results, and inference optimizations. The models are available on AI Kosh and Hugging Face, and the article details their performance across benchmarks and in real-world applications like webpage generation, JEE problem solving, and conversational agents.
This article explores how agentic AI can revolutionize deep learning experimentation by automating tasks like hyperparameter tuning, architecture search, and data augmentation. It delves into the core concepts, benefits, and practical considerations of using agentic systems to accelerate and improve the deep learning workflow.
