AI agents leverage a combination of technologies to perform tasks autonomously and intelligently. Machine learning, particularly deep learning, is at the core, enabling agents to recognize patterns, make decisions, and adapt to new data. Natural language processing (NLP) allows agents to understand and generate human language, which is essential for chatbots, virtual assistants, and customer service applications. Reinforcement learning is used for training agents in dynamic environments, such as robotics and gaming. Computer vision enables agents to process visual data for tasks like object recognition and navigation. Technologies like transformers, used in models such as GPT and BERT, have revolutionized NLP and multimodal AI capabilities. These technologies are often integrated with APIs, cloud computing, and edge devices to create scalable and efficient AI agents for various domains, including healthcare, finance, and customer support.
What AI technologies are used to power AI agents?

- The Definitive Guide to Building RAG Apps with LlamaIndex
- Exploring Vector Database Use Cases
- How to Pick the Right Vector Database for Your Use Case
- Information Retrieval 101
- Getting Started with Zilliz Cloud
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How does foreign key enforcement ensure consistency?
Foreign key enforcement is a critical feature in relational database management systems that ensures the integrity and c
What programming languages are commonly used with AutoML tools?
AutoML, or Automated Machine Learning, simplifies the process of applying machine learning models to datasets. Several p
How does AutoML determine stopping criteria for training?
AutoML systems determine stopping criteria for training based on several factors, notably performance metrics, convergen