What are AI agents?

AI agents are autonomous software tools that use artificial intelligence to perform tasks, make decisions, and adapt based on real-time feedback. They can operate independently or within a larger system, learning and evolving from data.

What makes AI agents different from other AI technologies?
AI agents stand out for their autonomy, unlike other AI models that rely on constant human input.
They can take action, make decisions based on set goals, and adapt to new information in real time.
This independence makes them valuable in dynamic environments like software development.

How AI agents work
AI agents use a combination of advanced algorithms, machine learning techniques, and decision-making processes.
Here are the three components that intelligent agents share:

1. Architecture and algorithms. AI agents are built on complex systems that let them process a lot of data and make informed decisions. Machine learning helps these agents learn from experience and improve over time.
2. Workflow and processes. An AI agent’s workflow usually starts with a specific task or goal. It then creates a plan of action,
executes the necessary steps, and adapts based on feedback. This process keeps AI agents continually improving their performance.
3. Autonomous actions. AI agents can perform tasks without human intervention, making them ideal for automating repetitive processes
in software development like code reviews or vulnerability detection.

Types of AI agents
AI agents come in various forms, each suited to different applications:

– Simple reflex agents. These agents act solely based on the current environment’s state, making decisions through a set of
predefined rules.

– Model-based reflex agents. Unlike simple reflex agents, these agents maintain an internal model of the world,
allowing them to consider past actions and predict future states.

– Goal-based agents. These agents work with specific goals in mind, making decisions that move them closer to achieving these goals.

– Utility-based agents. These agents consider different outcomes and how likely they are to happen, ultimately choosing to take the
actions that’ll make the most of their utility or benefit.

– Learning agents. These agents can improve their performance over time by learning from their environment and experiences.

Credit : https://github.com/resources/articles/ai/what-are-ai-agents