There are several different types of agentic AI, all serving different purposes and bringing new capabilities to the table. First, observability software allows for AI agents to be transparent, traceable and trustworthy. Especially as this software becomes more autonomous, it is especially important for humans to understand the what and why of its actions. Agent Frameworks are the backbone of building smarter agents, supporting more complex systems moving forward. The memory sector helps develop how agents learn, remember and understand the context of an input.
Tool libraries are used to give agents the ability to interact with external systems and databases, making them more versatile and useful. Sandboxes are used to execute new code and test ideas, effectively letting humans play around with new capabilities in a harmless environment. Model serving describes the second brain of the AI agent, following data input. This sector controls where decisions are made in an AI model. Finally, storage is paramount to AI development, allowing this software to keep long-term history, knowledge and state.
These groupings of AI operations work cohesively to allow ever-evolving AI systems to remain useful, efficient and under control. Learn more about the AI agent tech stack in the infographic below:
infographic by: brightdata.com
