NVIDIA has launched the Gemma 4 model family, designed to operate efficiently across a wide range of hardware, from data centers to edge devices like Jetson. This new generation includes the first Gemma MoE model and supports over 140 languages, enabling advanced capabilities like reasoning, code generation, and multimodal input.
Developers can fine-tune and deploy Gemma 4 using tools like NeMo Automodel and NVIDIA NIM, with commercial licensing available. The models are optimized for local deployment with frameworks such as vLLM, Ollama, and llama.cpp, offering flexibility for various use cases, including robotics, smart machines, and secure on-premise applications.
Developers can fine-tune and deploy Gemma 4 using tools like NeMo Automodel and NVIDIA NIM, with commercial licensing available. The models are optimized for local deployment with frameworks such as vLLM, Ollama, and llama.cpp, offering flexibility for various use cases, including robotics, smart machines, and secure on-premise applications.
This GitHub repository, "agentic-ai-prompt-research" by Leonxlnx, contains a collection of prompts designed for use with agentic AI systems. The repository is organized into a series of markdown files, each representing a different prompt or prompt component.
Prompts cover a range of functionalities, including system prompts, simple modes, agent coordination, cyber risk instructions, and various skills like memory management, proactive behavior, and tool usage.
The prompts are likely intended for researchers and developers exploring and experimenting with the capabilities of autonomous AI agents. The collection aims to provide a resource for building more effective and robust agentic systems.
Prompts cover a range of functionalities, including system prompts, simple modes, agent coordination, cyber risk instructions, and various skills like memory management, proactive behavior, and tool usage.
The prompts are likely intended for researchers and developers exploring and experimenting with the capabilities of autonomous AI agents. The collection aims to provide a resource for building more effective and robust agentic systems.
The future of work is rapidly evolving, and a new skill set is emerging as highly valuable: building and managing "agent workflows." These workflows involve leveraging AI agents – autonomous software entities – to automate tasks and processes. This isn't simply about AI replacing jobs, but rather about augmenting human capabilities and creating new efficiencies.
The article highlights how professionals who can orchestrate these agents, defining their goals, providing necessary data, and monitoring their performance, will be in high demand. This requires a shift in thinking from traditional task execution to workflow design and management. The ability to do so is becoming a key differentiator in the job market, essentially becoming a "career currency."
The article highlights how professionals who can orchestrate these agents, defining their goals, providing necessary data, and monitoring their performance, will be in high demand. This requires a shift in thinking from traditional task execution to workflow design and management. The ability to do so is becoming a key differentiator in the job market, essentially becoming a "career currency."
This paper introduces Natural-Language Agent Harnesses (NLAHs) – a new approach to AI agent harness design. NLAHs use editable natural language, improving portability and study, unlike traditional code-embedded harnesses. The authors also present the Intelligent Harness Runtime (IHR) and demonstrate viability through coding/computer-use benchmarks.
CAID is a new multi-agent framework for software engineering tasks. It improves accuracy and speed by using a central planner, isolated workspaces for concurrent work, and test-based verification—inspired by human developer collaboration with tools like Git. Evaluations show CAID significantly outperforms single-agent approaches.
This article provides a hands-on coding guide to explore nanobot, a lightweight personal AI agent framework. It details recreating core subsystems like the agent loop, tool execution, memory persistence, skills loading, session management, subagent spawning, and cron scheduling. The tutorial uses OpenAI’s gpt-4o-mini and demonstrates building a multi-step research pipeline capable of file operations, long-term memory storage, and concurrent background tasks. The goal is to understand not just how to *use* nanobot, but how to *extend* it with custom tools and architectures.
A-Evolve, a new framework developed by Amazon researchers, aims to revolutionize the development of agentic AI systems. It addresses the current bottleneck of manual tuning by introducing an automated evolution process. Described as a potential "PyTorch moment" for agentic AI, A-Evolve moves away from hand-tuned prompts towards a scalable system where agents improve their code and logic iteratively.
The framework centers around an ‘Agent Workspace’ with components like manifest files, prompts, skills, tools, and memory. A five-stage loop—Solve, Observe, Evolve, Gate, and Reload—ensures stable improvements. A-Evolve is modular, allowing for "Bring Your Own" approaches to agents, environments, and algorithms, and has demonstrated State-of-the-Art performance on benchmarks like MCP-Atlas and SWE-bench Verified.
The framework centers around an ‘Agent Workspace’ with components like manifest files, prompts, skills, tools, and memory. A five-stage loop—Solve, Observe, Evolve, Gate, and Reload—ensures stable improvements. A-Evolve is modular, allowing for "Bring Your Own" approaches to agents, environments, and algorithms, and has demonstrated State-of-the-Art performance on benchmarks like MCP-Atlas and SWE-bench Verified.
OpenAI has expanded its Responses API to facilitate the development of agentic workflows. This includes support for a shell tool, an agent execution loop, a hosted container workspace, context compaction, and reusable agent skills. The new features aim to offload the complexities of building execution environments from developers, providing a managed infrastructure for handling tasks like file management, prompt optimization, secure network access, and handling timeouts.
A core component is the agent execution loop, where the model proposes actions (running commands, querying data) that are executed in a controlled environment, with the results fed back to refine the process. Skills allow for the creation of reusable task patterns.
A core component is the agent execution loop, where the model proposes actions (running commands, querying data) that are executed in a controlled environment, with the results fed back to refine the process. Skills allow for the creation of reusable task patterns.
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.
OpenShell is a safe, private runtime environment designed for autonomous AI agents. It provides sandboxed execution with declarative YAML policies to control file access, data exfiltration, and network activity. Built with an agent-first approach, OpenShell offers pre-built skills for tasks like cluster debugging and policy generation.
Currently in alpha, it focuses on single-player mode and aims to expand to multi-tenant enterprise deployments. OpenShell uses a containerized K3s Kubernetes cluster for isolation and enforces security across filesystem, network, process, and inference layers. It supports agents like Claude, OpenCode, and Copilot, managing credentials securely.
Currently in alpha, it focuses on single-player mode and aims to expand to multi-tenant enterprise deployments. OpenShell uses a containerized K3s Kubernetes cluster for isolation and enforces security across filesystem, network, process, and inference layers. It supports agents like Claude, OpenCode, and Copilot, managing credentials securely.
