TL;DR

• Agentic AI is a Goal-Oriented AI system that exhibits a high level of autonomy compared to generative AI, which only responds when prompted.
• Agentic AI transcends basic automation, which is inherently restricted by fixed programming and predefined workflows.
• Autonomy is the pillar that agentic AI is based upon, but it's more of a spectrum indicating when human intervention is needed.
• Adaptability enabled by Continuous Learning allows agents to evolve their strategies and performance, and achieve predefined Goals.
• Architecturally, the Reasoning Engine plays the role of the CPU in a computer, getting data from the Perception Module, and it makes decisions on what action to take next, often employing probabilistic reasoning and meta learning.
• An Action Model executes tasks, and the Knowledge Base is where the context/rules are stored, with past interactions and new learned experiences.
• There are multiple types of agents, ranging from those focused on accuracy to those focused on speed, to hybrid agents and even multi-agent systems.

Agentic AI - Definition

Agentic AI refers to sophisticated artificial intelligence systems that exhibit a high degree of autonomy, proactiveness, and adaptability [1]. Traditional models have predefined structures, but Agentic AI dynamically learns and adjusts its behaviour based on the feedback from its environment [1]. This capability extends far beyond simple automation, which is inherently limited by static programming and predefined workflows [4].

A fundamental distinction of Agentic AI lies in its goal-oriented nature. These systems are designed to pursue broad objectives and solve complex, multi-step problems autonomously, rather than merely executing isolated decisions or responding to prompts [2].

It's a transition from a system that only responds to user inputs to one that can self-manage with significantly less human oversight, and from just executing commands to strategizing and optimizing workflows.

Principles Of Agentic AI Architecture

1- Autonomy

Non-negotiable principle indicating the agent's capacity to operate independently, make decisions, and take actions without the explicit requirement for direct human instructions [1]. Autonomy enables agents to make real-time decisions and act independently [8]. However, human intervention remains crucial for guiding high-level goals and offering oversight in unforeseen circumstances [4].

2- Adaptability

The ability of an agent to modify its behavior given new data, feedback, and environmental changes [3]. Agents learn from interactions and refine their strategies over time [8].

3- Goal-Oriented Behaviour

Any action that the agent takes serves an objective [8], and well-structured agentic architecture can achieve both short-term and long-term tasks and objectives concurrently [8].

4- Continuous Learning

These systems are designed to learn continuously [8], based on new inputs, updates to the knowledge base, and strategy refinements are done; it's a feedback loop that enhances their accuracy and effectiveness [3]. This principle also drives adaptability and results in evolving and improving the performance of an agent over time.

Architectural Components

An Effective Agentic System should be based on a modular architecture, where components interact and collaborate to facilitate intelligent behaviour

• Perception Module: enables the agent to interpret its environment [8], gathering and processing raw data and turning them into structured information for other modules [1].
• Reasoning Engine / Cognitive Module: Analyses information and makes decisions based on predefined goals, constraints, and the available data [1].
• Action Module / Execution: Carries out the planned tasks and also facilitates coordination and synchronized decision-making among different AI agents [3].
• Memory and Knowledge Base: Maintains contexts, past interactions, learned experiences, and predefined rules [3].
• Learning Mechanism / Feedback loop: enables the agent to learn from its actions and the resulting feedback, and continuously updates the knowledge [1].
• Communication Interface: streamlines collaboration between AI systems and human operators, as well as facilitating coordination and interoperability [3].

This is not only best practice but a fundamental requirement for scalable Agentic Systems. This structure allows for easier updates and seamless integration of various models.

Types of AI Agents

Architectural variations result in distinctive types of AI agents tailored for specific applications.

• Reactive Agents: The most basic agents, characterized by a straightforward input-output response model, are fast but limited, and they also don't maintain internal memory [1].
• Deliberative Agents (Planning Agents): Possessing the ability to "think ahead" by simulating their environment internally, they engage in reasoning and planning before taking action [1].
• Hybrid Agents: Combines elements of both previous agent types to balance speed and accuracy [1].
• Learning Agents: Specifically designed to learn from data and continuously improve their performance over time [1].
• Multi-Agent Systems: Involves multiple specialized AI Agents collaborating to accomplish complex workflows [3].

References (Used & Read):

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