Agentic AI, at its core, is about creating autonomous systems that can operate independently to achieve specific goals. These systems, or agents, are designed to perceive their environment, process information, make decisions, and take actions. This is a significant step beyond traditional AI, which is often limited to specific tasks and requires human intervention to operate.

‍

The term "agentic" comes from the concept of agency, the capacity to act independently and make choices. Agentic AI systems possess this capacity in the digital realm. They can set their own sub-goals, choose their own strategies, and adapt their behavior based on feedback from their environment.

‍

This autonomy distinguishes agentic AI from other forms of AI. A traditional machine learning model might be excellent at predicting customer churn, but it cannot take action to prevent it. An agentic AI system, on the other hand, could not only predict churn but also automatically reach out to at-risk customers with personalized retention offers, adjust pricing, or escalate to a human agent when appropriate.

An AI agent is composed of several key components that work together to enable its autonomous operation. These components provide the agent with the ability to perceive its environment, remember past interactions, plan future actions, and execute those actions.

 Profile: Defining Purpose and Identity

The profile defines the agent's identity, purpose, and goals. It provides the agent with a clear understanding of its role and the context in which it operates. The profile is like the agent's DNA, encoding its fundamental characteristics and capabilities.

‍

A customer service agent's profile might specify that its purpose is to resolve customer inquiries quickly and satisfactorily, that it has access to the company's knowledge base and order management system, and that it should escalate complex issues to human agents. A research agent's profile might specify that its purpose is to gather and synthesize information on specific topics, that it has access to academic databases and web search, and that it should prioritize authoritative sources.

‍

The profile also defines the agent's constraints and boundaries. What actions is it allowed to take? What information can it access? What are the limits of its authority? These constraints are essential for ensuring that agents operate safely and within acceptable parameters.

Memory: Learning from Experience

The memory component allows the agent to store and retrieve information. This includes both short-term memory for immediate tasks and long-term memory for retaining knowledge and experiences. Memory is what enables an agent to learn from past interactions and improve its performance over time.

‍

Short-term memory, also known as working memory, holds information that is relevant to the current task. When you're having a conversation with an AI agent, its short-term memory keeps track of what you've said, what it has said, and the current state of the conversation. This allows the agent to maintain context and provide coherent responses.

‍

Long-term memory stores information that persists across multiple interactions. This can include facts about the world, learned patterns and relationships, successful strategies from past tasks, and episodic memories of specific events. An agent that has helped you book travel in the past might remember your preferences for aisle seats and vegetarian meals, allowing it to provide more personalized service in future interactions.

‍

Modern agentic systems often use vector databases and semantic search to implement memory systems. This allows agents to quickly retrieve relevant information from large knowledge bases based on meaning rather than exact keyword matches.

Planning: Strategic Thinking

The planning component is where the agent formulates strategies and breaks down complex tasks into smaller, manageable steps. This is where the agent's reasoning and decision-making capabilities come to the forefront. Planning is what allows agents to tackle complex, multi-step problems that require strategic thinking.

‍

Different planning approaches are suited to different types of tasks. For well-defined problems with clear goals, classical planning algorithms can decompose the problem into a sequence of actions that will achieve the goal. For more open-ended tasks, agents might use heuristic search, exploring different possible approaches and selecting the most promising one.

‍

Large language models have introduced new planning capabilities through techniques like chain-of-thought reasoning, where the agent explicitly reasons through a problem step by step. This approach has proven particularly effective for complex reasoning tasks.

Some agents use reinforcement learning to learn planning strategies through trial and error. By receiving feedback on the outcomes of their actions, these agents can learn which strategies work best in different situations.

Action: Interacting with the World

The action component is how the agent interacts with its environment. This can include a wide range of actions, from sending messages and making API calls to controlling physical robots. The action component is where the agent's plans become reality.

‍

In software-based agents, actions typically involve interacting with APIs, databases, and other digital systems. An agent might search the web, query a database, send an email, or call a function to perform a calculation. The key is that the agent can select and execute these actions autonomously based on its goals and plans.

‍

For physical agents such as robots, the action component also includes motor control systems that allow the agent to manipulate objects and navigate through space. This requires additional considerations such as sensor feedback, real-time control, and safety mechanisms.

‍

The action component often includes a tool-use capability, where the agent has access to a set of predefined tools or functions that it can invoke to accomplish specific tasks. IBM's research on agentic AI emphasizes the importance of tool use in enabling agents to interact effectively with their environment.