Artificial intelligence is rapidly evolving beyond simple prompts and responses. Increasingly, organizations are exploring agentic AI workflows - systems where multiple AI models,tools, and agents can work together to complete more complex, multi-step tasks with minimal manual intervention. Rather than relying on a single LLM or model, these workflows combine specialized AI capabilities such as reasoning, coding, speech recognition, visual understanding, and automation into coordinated systems designed around real-world needs.
The Acer Veriton GN100 AI Mini Workstation, based on the NVIDIA DGX Spark™ platform, is designed to support these emerging AI workflows locally. Powered by the NVIDIA Grace Blackwell GB10 Superchip, the GN100 delivers up to 1 petaFLOP of FP4 AI performance and 128 GB of unified memory in a compact mini workstation form factor. With NVIDIA ConnectX-7 networking technology, this workstation is also scalable to up to four connected systems further providing up to 512 GB of memory and up to 700 billion parameters to run larger models. It provides the compute resources needed to support large language models, multimodal AI systems, and connected AI agents directly on local infrastructure.
Part of what makes these workflows practical is the recently announced and increasingly popular NVIDIA NemoClaw. NemoClaw is an open source framework that demonstrates how to add privacy and security controls to AI agents like OpenClaw. With one command, anyone can run always-on, self-evolving agents on their Acer Veriton GN100 AI Mini Workstation. And, when combined with the tools available on the NVIDIA’s DGX Spark playbook, this enables reasoning models, coding assistants, and other enterprise tools to work together on the GN100 as coordinated systems tailored to specific needs within a more secure environment.
Taken together with AI-assisted development tools like Claude Code and Cursor, these modular workflows allow organizations to build always-on AI agents capable of processing voice commands, analyzing visual content, assisting with coding tasks, and automate portions of complex workflows locally. This gives developers, educators, researchers, and enterprises greater flexibility to experiment with advanced AI systems while maintaining stronger control over deployment, performance, security, and data privacy within local infrastructure.
Understanding NVIDIA NemoClaw and Agentic AI Workflows
As organizations increasingly build integrated AI systems that combine multiple models, agents, and tools together, workflows are becoming ever more capable and autonomous. Rather than relying on a single AI assistant, these environments coordinate multiple systems together within governed workflows designed to meet specific organizational needs.
Built around NVIDIA Agent Toolkit software, NemoClaw installs NVIDIA OpenShell to enforce policy-based privacy and security guardrails that help define how agents like OpenClaw, Hermes, Claude Code, or Codex behave, access data, and interact with approved tools during execution with zero code changes necessary. This provides organizations with greater control over AI agent behavior while helping reduce risks such as unauthorized access, uncontrolled tool execution, or unintended data exposure.
Organizations can use a variety of coding agents and AI tools while deploying workflows locally or across broader infrastructure environments depending on operational needs. This architecture becomes increasingly important as AI agents and systems interact more directly with sensitive internal documentation, research data, source code, or systems. Because these workflows can remain locally deployed on platforms such as the Acer Veriton GN100, organizations maintain greater control over privacy, infrastructure, and operational costs while still enabling more advanced AI-assisted workflows.
The NVIDIA Agent Toolkit with OpenShell, and open models such as NVIDIA Nemotron, support AI systems that are not only modular and scalable, but also continuously governed during execution - helping organizations build more capable AI agents while maintaining stronger oversight, control, and improved cost efficiency.
AI-Assisted Development and Customizable Model Workflows
Building on this governed AI environment, the next step is how organizations actually assemble and refine these systems in practice. Rather than deploying fixed AI setups, teams are increasingly designing workflows by combining different specialized models and tools to match specific development and operational needs.
In this model-centric approach, the previously mentioned DGX Spark playbook highlights how AI systems can be constructed from modular components rather than a single, monolithic model. For example, vision-language models (VLMs) can be used to interpret images, diagrams, or visual content, while automatic speech recognition (ASR) models can be used to enable voice commands and natural interactions.
Rather than treating these as standalone components, the focus shifts toward building task-driven agent workflows, where each component contributes a specific capability within an orchestrated process. This may look like a workflow beginning with the ASR model capturing a spoken input or command, followed by the VLM model interpreting related visual context, before passing structured outputs into a reasoning agent that determines the next action or response. Within the NemoClaw and OpenShell framework, these workflows can execute with defined permissions, tool access boundaries, and controlled interactions between components.
This modularity also supports ongoing experimentation. Instead of committing to a single AI configuration, organizations can iterate on different combinations of models to optimize for accuracy, performance, or task specialization. Over time, workflows can evolve as new models become available or as requirements change.
To support this level of concurrent AI processing, Acer Sense Pro provides system-level visibility and control over CPU and GPU performance, memory usage, storage, and resource allocation when multiple models and agents are running simultaneously. This helps teams better understand how workloads are distributed across the system and maintain stability when testing more complex multi-model pipelines.
To further support model evaluation and optimization, Acer Sense Pro also includes an LLM Benchmark Tool that enables quick multi-model comparison. It evaluates key performance metrics such as Tokens Per Second and Time to First Token, helping teams identify and select the most suitable model for AI inferencing based on real workload requirements. In addition, an integrated AI agent with embedded product documentation and system manuals helps teams troubleshoot, configure, and optimize their workflows quickly and easily.
By giving clearer insight into system behavior during intensive AI tasks, Acer Sense Pro becomes a practical layer for managing experimentation before scaling workflows into production environments.
In addition, Acer Sense Pro also streamlines the setup for agentic AI environments through one-click NemoClaw deployment. By selecting “NemoClaw” within Acer Sense Pro, the system automatically executes and completes the deployment process. Once installed, selecting “NemoClaw” again provides direct access to the NemoClaw dashboard, enabling users to quickly begin configuring and managing agent workflows without going through a complex manual setup.
Conclusion
These increasingly modular and agent-driven AI workflows mark a shift toward more customizable and task-specific AI agents. By combining specialized models, developer tools such as OpenClaw with its ready-built modules and agent components, orchestration frameworks like NemoClaw, and tools designed for real-world development workflows, organizations can build AI systems that better reflect their own operational needs within a safe and secure environment.
With platforms like the Acer Veriton GN100 AI Mini Workstation providing the local compute foundation, and Acer Sense Pro supporting visibility and optimization, teams can more easily experiment, refine, and scale these workflows with greater control and flexibility.
