Agentic AI
AI agents require capabilities far beyond a simple chat interface. They necessitate a functional environment where they can utilise applications, navigate the web, manage files, execute commands, and fulfil objectives. DaDesktop equips each agent with a dedicated desktop, providing the necessary tools and computing power to operate autonomously.
A desktop for the agent
Instead of restricting an agent to a text-based interface, grant it access to a browser, terminal, files, applications, and various other utilities.
Run local models
DaDesktop offers the GPU infrastructure required to execute models locally. This allows model processing to remain within the DaDesktop environment, avoiding the need to route every request to an external API.
Separate environment
Each agent is assigned its own isolated desktop. Should any issues arise, you can simply reset the environment and begin again.
Why use a local LLM for your agent
- Maintain data privacy: Prompts, files, and other agent-related data remain secured within the DaDesktop environment.
- Eliminate per-token costs: Execute the model on DaDesktop’s GPU rather than incurring charges for every API call.
- Enhance control: Select the specific model your agent utilises and manage its operational parameters.
- Operate without an external API: The agent can leverage its local model directly from the desktop environment.
What agents can do with a desktop
- Utilise applications: Interact with software directly, rather than being limited to text generation.
- Browse and research: Use a web browser to locate and process information.
- Manage files: Create, read, modify, and organise files within the desktop environment.
- Execute tasks: Employ terminals and other tools to perform multi-step workflows.
- Work with code: Edit projects, run commands, and test changes when coding is part of the requirement.
Running Hermes Agent
Hermes Agent is an AI entity capable of using a computer to complete tasks, including web browsing, application usage, file management, and command execution. It is also designed to learn from its actions, retain useful information, and refine its approach to future tasks, rather than starting from scratch every time.
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