Build AI Agent

Understanding AI Agents and Their Levels of Autonomy

AI agents interact with their environment, process information, and take action. Traditional AI agents, like vacuum robots, use sensors to perceive their surroundings and actuators to perform tasks. More recent AI agents, such as LLM-based systems, process text input and generate responses.

Levels of Agentic AI

Different AI systems require different levels of autonomy. The more control given to an AI system, the more “agentic” it becomes.

• Code: Full control over decisions by explicitly coding every step. Best for critical tasks like deleting user records.

• LLM Call: Single-step responses using an LLM. Works well for FAQ chatbots.

• Chain: Multi-step processes in a fixed sequence. Ideal for structured workflows like text cleaning and summarization.

• Router: Multi-step tasks with branching decisions but no loops. Useful for customer support systems that classify queries.

• State Machines: Workflows with loops and decision-making based on prior steps. Suitable for iterative tasks like content refinement.

• Autonomous: Fully independent systems that act without user input. Best for safe, automated tasks like supply tracking and reordering.

Choosing the Right Approach

More autonomy increases complexity in deployment and maintenance. Selecting the right agentic level ensures efficiency while balancing control and automation.