The core components of Agentic AI are goals, reasoning, tools, memory, and control logic. Goals define what the agent is trying to achieve. Reasoning determines how the agent interprets the goal and current state. Tools allow the agent to take actions in the external world. Memory stores relevant past information. Control logic ties everything together by deciding when to act, when to stop, and when to escalate.
Reasoning is typically handled by a language model, but it is constrained by prompts, schemas, and system rules. Tools are implemented as functions or APIs with explicit contracts. Memory is often split into short-term (current session context) and long-term (persisted knowledge and experience). Long-term memory is where vector databases such as Milvus or Zilliz Cloud come in, enabling semantic retrieval of relevant past events, documents, or decisions. Control logic is written in code and enforces limits like maximum steps, allowed actions, and safety checks.
What makes these components “agentic” is how they interact. The agent reasons about which tools to use, retrieves relevant memory to inform decisions, updates memory with outcomes, and loops until the goal is satisfied. Removing any one of these components reduces the system to a simpler automation or chatbot. Agentic AI emerges when all of them work together under explicit constraints.