In today’s AI landscape, AI agents have taken center stage for their ability to autonomously handle a wide range of tasks on behalf of users. These tasks can include making online purchases, conducting research, or booking travel arrangements. By allowing AI agents to move beyond chat interfaces and directly impact the real world, agentic AI represents a significant advancement in the capabilities of AI technology.
The development of agentic AI has been rapidly evolving, with key components like the model context protocol (MCP) being just a year old. Despite the fast-paced nature of this field, it is essential to understand the core components of an agentic AI system to cut through the noise and confusion. By breaking down these components, the mystery surrounding AI agents can be dispelled, making them more accessible and understandable.
To create an agentic ecosystem, several core components are necessary. These include frameworks for building agents, platforms to run AI models, infrastructure for running agentic code, mechanisms for translating text-based AI interactions into tool calls, short-term and long-term memory capabilities, and systems for tracing the performance of AI agents. By understanding how these components work together, developers can effectively create and deploy AI agents that meet the needs of users.
