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
- Getting Started with Milvus
- Optimizing Your RAG Applications: Strategies and Methods
- Natural Language Processing (NLP) Advanced Guide
- AI & Machine Learning
- 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
What is the role of regularization in deep learning?
Regularization in deep learning serves as a technique to prevent models from overfitting to the training data. Overfitti
How does speech recognition handle rare or technical terms?
Speech recognition systems handle rare or technical terms through several strategies, including the use of specialized v
How do you implement auditing in a document database?
Implementing auditing in a document database involves tracking changes made to documents over time. This can include cre