FAQ

Frequently Asked Questions

Get answers to common questions about UniTeamsCloud's AI enterprise platform and services.

What are multi-level agent systems?

Multi-level agent systems are advanced artificial intelligence architectures composed of agents that

Operate in different areas of expertise

Are organized hierarchically

Communicate and collaborate with each other
Key Features

AI agents specialized at different layers

Coordinated operation within a hierarchical structure

Hierarchical decision-making mechanisms

Task automation aligned with each layer

Continuous learning and self-improvement
Agent Layers and Their Tasks

Manager Agents - Coordination and strategy

Expert Agents - Domain-specific expertise

Executor Agents - Task-oriented operations

Data Agents - Information gathering and processing

What are multi-level agent systems used for?

These systems enable the use of artificial intelligence not just at an individual level, but at an institutional level.

Multi-agent systems function like departments that operate 24/7, flawlessly executing their tasks and can be replicated as needed.e

According to research, starting from 2025, multi-agent systems will become indispensable teams that every company will want to have.

These teams can be used as accelerators within existing departments or can fully take over specific tasks.

By handling repetitive tasks within a company, they significantly increase employee productivity.

They are capable of managing complex human tasks.

Why are multi-level agent systems important?

They imitate humans in text and visual-based tasks. Their ability to read even handwritten documents provides a significant advantage.

They accelerate processes that traditionally require manual effort (e.g., automating customer service, generating leads).

They enable humans to focus on creative and strategic roles.

Since multi-agents are artificial intelligence, they can monitor, interpret, and report all internal company data. In doing so, they can serve as an early warning system.

Although it varies by industry, a general cost saving of 10% in the first 6 months, 20% in the second 6 months, and 30% in the following year can be targeted.

These increases in company efficiency positively impact competitiveness.

What are the main criteria for using artificial intelligence in corporate companies?

Full compliance with security and compliance regulations

Cost minimization

Reliable results and stability

Training and fine-tuned models

Ability to manage own workflows without the need for constant consulting

Easy and seamless updates

Authorization Capabilities

Traceability & Reporting

Usage of local LLM models

Do multi-agent processes generate definitive results?

Yes, definitive results are always achieved. Thanks to "Output Parser" tools, outputs are always standardized.

The result of a process is always checked by another Agent. If there is any inconsistency, the process is restarted. This continues until the controlling Agent accepts the process.

These processes should not be confused with "Chat". Instead of a simple "Chat", a chain of processes consisting of many sub-tasks is used to reach a conclusion.

An LLM specialized in the relevant field is used according to the suitability of the task. The "Temperature" (creativity) value of the Agent used is adjusted according to the content of the process.

In rare and critical situations, if desired, the results obtained after each step can be sent to the relevant person for approval.

Are multi-agent processes reliable in terms of information compliance?

Yes, reliability in this regard has been ensured.

It is sufficient for the company to specify which information cannot be disclosed by writing it in a simple "Word" document (e.g., financial information of the company, employee information, employee phone numbers, personal information cannot be disclosed, etc.).

The Agent responsible for control is trained (Fine-Tuning) with this information. This control is always carried out by a local Agent.

After any "Prompt" request, it is decided whether the information can be sent to global AI systems like ChatGPT or Google.

Based on the decision, the process is directed to either global or local AI systems for execution.

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