What is Agentic AI?
Turkish: Agentic AI (Ajansal Yapay Zeka)
Agentic AI is an AI approach where systems plan tasks, use tools, and adjust their next steps instead of producing a single reply.
What is Agentic AI?
Agentic AI goes beyond a one-shot chat response. The system receives a goal, derives actionable steps, calls tools, evaluates the result, and changes its plan when the situation requires it.
In this pattern, an AI agent is often the working unit; agentic AI describes the wider approach around planning, permissions, feedback, and coordination. Multiple agents can also cooperate: one reads documents, another checks customer data, and another prepares the final response.
Core Components
Agentic systems combine task definition, tool access, memory, state tracking, and safety boundaries. Protocols such as MCP help agents connect to external tools through a more standard interface.
Not every step should be fully automatic. Refunds, inventory changes, or customer record deletion need human approval, role-based permissions, and audit logs.
Business Use
Agentic AI is useful for quote collection, product data enrichment, accounting document checks, internal knowledge assistants, and multi-step integration workflows. It becomes more relevant when an application must pull data from several systems and move a process forward, not just answer a question.
Good design starts with narrow goals, verified tool outputs, and regular testing of LLM behavior.
Related Terms
An AI agent is a software component that uses an LLM, tools, and data sources to plan steps and complete a defined goal.
LLM (Large Language Model)An LLM is a model trained on large text datasets that can understand and generate natural language, forming the basis of tools like ChatGPT.
Model Context Protocol (MCP)Model Context Protocol lets AI applications connect to tools, files, and external systems through a shared context interface.