# Overview Instead of relying solely on rigid intent rules and predefined paths, this model introduces an **orchestration layer powered by LLM-based reasoning and governed decision logic.** The result is a more flexible architecture that enables fluid conversations, clearer separation of responsibilities, and scalable problem-solving across specialized capabilities. This does not remove structure. On the contrary, Agentic architecture depends on structured governance. Interactions are evaluated, classified, and routed through a controlled orchestration model before execution begins. In this model, intelligence exists not only in response generation, but also in how the system determines which component should handle each request. ## What is an AI Agent? An AI Agent is a specialized execution unit designed to handle a defined set of tasks within a broader conversational system. Unlike traditional virtual agents that often depend on rigid scripts and static intent structures, AI Agents are built to operate with more contextual understanding, specialized responsibilities, and goal-oriented behavior. Each Agent is configured to perform within a specific domain, using its role, goal, instructions, and available skills to generate responses or execute actions within its scope. This creates a more modular architecture, where complex requests can be handled through specialized components instead of forcing a single assistant to do everything. **The model is closer to how high-performing teams operate:** different specialists handle different responsibilities, while a central orchestration layer ensures control, consistency, and correct delegation.

AI Agents core anatomy

## New approach Rather than designing the experience primarily around intent classification and rule-based execution, the architecture defines a Project containing a Supervisor and one or more specialized Agents. Within this model, the Supervisor processes user input, evaluates the request against governance rules, and selects the appropriate handling path. Once selected, the Agent becomes the executor of the task within its assigned scope. This distinction is critical: * The **Supervisor** controls orchestration; * The **Agent** executes specialized work; * Routing is governed; * Execution is delegated. {% hint style="info" %} The Supervisor does not function as a free-form assistant and does not perform the Agent’s task **except** in controlled scenarios such as **fallback** or **chit-chat**. Its responsibility is to evaluate eligibility, follow the defined decision hierarchy, and route the interaction accordingly. {% endhint %} Instead of relying solely on rigid intent rules and predefined paths, this model introduces an **orchestration layer powered by LLM-based reasoning and governed decision logic.** The result is a more flexible architecture that enables fluid conversations, clearer separation of responsibilities, and scalable problem-solving across specialized capabilities. This does not remove structure. On the contrary, Agentic architecture depends on structured governance. Interactions are evaluated, classified, and routed through a controlled orchestration model before execution begins. In this model, intelligence exists not only in response generation, but also in how the system determines which component should handle each request. ### What is an AI Agent? An AI Agent is a specialized execution unit designed to handle a defined set of tasks within a broader conversational system. Unlike traditional virtual agents that often depend on rigid scripts and static intent structures, AI Agents are built to operate with more contextual understanding, specialized responsibilities, and goal-oriented behavior. Each Agent is configured to perform within a specific domain, using its role, goal, instructions, and available skills to generate responses or execute actions within its scope. This creates a more modular architecture, where complex requests can be handled through specialized components instead of forcing a single assistant to do everything. **The model is closer to how high-performing teams operate:** different specialists handle different responsibilities, while a central orchestration layer ensures control, consistency, and correct delegation.

AI Agents core anatomy

### New approach Rather than designing the experience primarily around intent classification and rule-based execution, the architecture defines a Project containing a Supervisor and one or more specialized Agents. Within this model, the Supervisor processes user input, evaluates the request against governance rules, and selects the appropriate handling path. Once selected, the AI Agent becomes the executor of the task within its assigned scope. This distinction is critical: * The **Supervisor** controls orchestration; * The **Agent** executes specialized work; * Routing is **governed**; * Execution is **delegated**. {% hint style="info" %} The Supervisor does not function as a free-form assistant and does not perform the Agent’s task **except** in controlled scenarios such as **Fallback** or **Chit-Chat**. Its responsibility is to evaluate eligibility, follow the defined decision hierarchy, and route the interaction accordingly. {% endhint %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.conversational-ai.syntphony.com/user-guide/ai-agents/overview.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.