AI Server for agents
Infrastructure for AI agents, tools, APIs and integration with enterprise systems.
What it is
AI agents are not only a model. They are an orchestration layer, a toolset, access to enterprise systems, permission control and action audit. Agent infrastructure is designed around these requirements — not only around GPUs.
When it fits
- Agents handling multi-step business tasks
- AI integrated with ERP, CRM, ECM, BPM
- Agents with human approval for significant decisions
- Multi-agent scenarios with role separation
What to keep in mind
- Agents need enterprise context — not just a fast GPU
- Orchestration and permissions require a dedicated engineering layer
- Agents with tools generate more tokens and demand more resources than chat
- Without audit and policy, agents quickly become a source of risk
Frequent questions
How many agents can run on one server?+
It depends on models, task complexity and load. A multi-GPU server can serve dozens of scenarios concurrently. Final sizing follows process analysis.
How does an agent differ from a chatbot?+
A chatbot answers requests. An agent takes a goal, builds a plan, uses tools, performs actions and can continue without constant human involvement. That is a fundamentally different infrastructure load.
Is human-in-the-loop mandatory?+
For significant decisions — yes. We design agents so that actions requiring responsibility always go through human approval. It is an architectural principle, not an option.
Let’s discuss your task
Tell us what the AI contour needs to solve — we will match the configuration, infrastructure and next step.