AI agents and expert systems are both used to solve complex problems, but they differ in design, flexibility, and application. AI agents are autonomous systems that perceive their environment, process information, and take actions to achieve specific goals. They often use machine learning (ML) or reinforcement learning to adapt to new data or changing conditions. In contrast, expert systems are rule-based programs designed to emulate human expertise in a narrow domain. They rely on a predefined knowledge base and inference engine to apply logical rules, making decisions based on static, human-curated information.
A key distinction lies in adaptability. AI agents are built to learn and improve over time. For example, a recommendation system that adjusts its suggestions based on user interactions uses ML to refine its predictions. Expert systems, however, operate within fixed rules. A classic example is MYCIN, a 1970s medical diagnosis system that applied deterministic rules to identify bacterial infections. While effective in its domain, MYCIN couldn’t learn from new cases—its knowledge was limited to what human experts explicitly encoded. This rigidity makes expert systems less suited for dynamic environments where data patterns evolve, whereas AI agents excel in such scenarios.
Implementation approaches also differ. AI agents often rely on frameworks like TensorFlow or PyTorch for training models on large datasets, and they may integrate with APIs or sensors to interact with real-world environments (e.g., autonomous vehicles processing camera feeds). Expert systems, meanwhile, require meticulous knowledge engineering to codify domain expertise into rules. Tools like CLIPS or Drools are used to build rule-based logic, requiring developers to manually define all possible scenarios. For example, a tax calculation system might use rules to determine deductions but lacks the ability to handle novel tax scenarios outside its predefined logic. While AI agents prioritize autonomy and adaptability, expert systems prioritize precision and transparency within their predefined scope.