SimpleOne explained the architecture of AI agents in its enterprise GenAI platform

The SimpleOne team published on July 4, 2026, on Habr a comprehensive technical breakdown of AI-agent architecture in its own GenAI-platform: what components they consist of, how they integrate into corporate processes, and what they can actually do independently.

Why Assistants Are No Longer Enough

A corporate AI assistant operates on one scheme: receive a question — generate an answer. This is useful, but limited. In real work scenarios, you don't just need to explain how to create a task — you need to create it. Not retell what happened with an incident — but gather data from multiple systems. Not suggest sending a digest — but send it.

This is where the assistant hits a ceiling: it generates text, but doesn't interact with infrastructure directly.

An AI agent is structured differently. Upon receiving a request, it doesn't just formulate an answer — it analyzes available tools and independently decides in what sequence to call them to achieve the goal. There is no rigid script: there is a description of the agent's capabilities, a set of methods, and an end goal. This is the key distinction: a chatbot follows a predefined script, an agent builds one itself.

How an Agent Works Inside the Platform

In the architecture that SimpleOne describes, several key components stand out. The first is a set of tools: functions, API calls, data operations that the agent is allowed to use. The second is a planning mechanism: the agent evaluates the request, builds a chain of necessary actions, and iteratively executes it. The third is a boundary of constraints that determines what data and operations the agent has access to, and which require explicit human confirmation.

Practical scenarios covered in the material:

• Creating tasks and tickets from incoming requests without manual operator involvement
• Gathering incident data from multiple interconnected systems
• Forming and automatically sending digests
• Finding related objects and entities within the corporate platform

A fundamental difference from classical chatbots: the agent doesn't wait for the next user command. Upon receiving a goal, it moves toward it through a sequence of tool calls — until it gets a result or reaches the boundary of its authority.

"Upon receiving a request, an AI agent evaluates available tools and independently decides in what sequence to call them.

There is no rigid script, there is a set of methods and an instruction," — the SimpleOne team formulates the principle.

Security as a Fundamental Condition for Implementation

A corporate agent, unlike an experimental prototype, works with real data and production systems. This makes the question of constraints fundamental — not an architectural option, but a condition for approval of implementation.

SimpleOne devotes a separate section to the security topic: how agent permissions are defined, how the platform controls the scope of its actions, and how it prevents going beyond permitted operations. In a corporate environment, you cannot give an agent broad rights and expect careful work — you need a clear boundary between what the agent does independently and what requires explicit human involvement.

What This Means

The corporate AI market is shifting from "smart document search" to agent systems that actually take on operational routine work. SimpleOne's publication is one of the few detailed technical breakdowns of this architecture from a Russian B2B vendor. For teams choosing a platform for corporate deployment, transparency in describing the component model and approach to security becomes a significant argument when making a decision.