| Definition | A broader concept where AI systems exhibit goal-directed, autonomous behavior | Software entities designed to perform specific tasks autonomously |
| Scope | Encompasses multiple agents or systems working toward complex objectives | Typically focused on a single task or domain |
| Autonomy Level | High – can plan, reason, and adapt across tasks and environments | Moderate – operates within predefined rules and boundaries |
| Examples | Multi-agent systems, autonomous research assistants, self-improving models | Chatbots, recommendation engines, robotic process automation (RPA) bots |
| Goal Orientation | Long-term, strategic goals with dynamic planning | Short-term, task-specific goals |
| Learning Capability | Often includes self-learning and continuous improvement | May include learning, but often rule-based or supervised |
| Interaction Style | Can collaborate with other agents or humans to achieve complex outcomes | Usually interacts with users or systems in a limited, predefined way |
| Use Cases | Scientific discovery, autonomous business operations, complex simulations | Customer support, scheduling, data entry, personalized recommendations |
| Complexity | High – involves multiple layers of reasoning and coordination | Lower – focused on executing specific tasks efficiently |
| Technology Stack | Combines LLMs, planning algorithms, reinforcement learning, and memory systems | Often built using APIs, ML models, and rule-based engines |