Agentic AI agents "learn" primarily through mechanisms rooted in machine learning and artificial intelligence, particularly through methods that allow these systems to improve from experience and adapt to new information. Here are the key learning strategies commonly used in Agentic AI:
- Supervised Learning: In supervised learning, Agentic AI systems are trained on a labelled dataset where the input data (such as images, text, or sensor data) is paired with the correct output (such as labels or decisions). The AI learns to map inputs to outputs based on this training data, adjusting its internal parameters to minimise errors. This method is often used for tasks like classification and regression.
- Unsupervised Learning: This involves training AI systems using data that has not been labelled, allowing the AI to identify patterns and relationships in the data on its own. Techniques such as clustering or dimensionality reduction are common. Agentic AI might use this to understand and segment user behaviours or to detect anomalies.
- Reinforcement Learning (RL): Particularly relevant to Agentic AI, RL involves learning to make sequences of decisions by interacting with an environment. The AI agent performs actions and receives feedback in the form of rewards or penalties. Over time, the agent learns to take actions that maximise cumulative rewards. This method is crucial for agents required to operate in dynamic and uncertain environments where the consequences of actions inform the learning process.
- Deep Learning: Using neural networks with many layers, deep learning enables Agentic AI agents to learn from vast amounts of unstructured data. These models excel in tasks such as speech recognition, natural language understanding, and image recognition, enabling agents to perform more complex tasks that require a deeper understanding of the content.
- Transfer Learning: Transfer learning involves taking a model that has been trained on one task and fine-tuning it for a different but related task. This is particularly useful for Agentic AI agents, allowing them to leverage pre-existing knowledge and adapt quickly to new tasks with minimal data.
- Active Learning: Some Agentic AI systems use active learning, where the agent actively queries a user or an oracle to label new data points that are most beneficial for learning. This is useful in scenarios where unlabelled data is plentiful, but labelled data is scarce or expensive to obtain.
- Continuous and Online Learning: Agentic AI agents may also be designed to continually update their models and policies based on new data collected during operation, rather than only learning during a distinct training phase. This allows them to adapt to changes over time and improve their performance as they gain more experience.