What Makes Something an AI Agent?

Artificial intelligence (AI) has become mainstream over the past few years. Before becoming mainstream, AI felt more like a science fiction concept that we used to watch in movies and imagine how future technology might be, rather than a practical business tool. That has changed. AI tools are widely available for millions to use for everyday tasks, while companies embed these intelligent tools into their operations.

However, AI tools like ChatGPT can only do so much; such AI tools are great for spitting out text, but they lack the ability to perform tasks on users' behalf. That is where autonomous AI agents come into action with their ability to reason and act for task completion. Autonomous AI agents aren't just glorified chatbots; they can perform complex tasks, automate workflows, and collaborate with human teams to deliver tangible results.

What Makes Something an AI Agent?

Now, the question is, what exactly makes something an "AI agent"? It comes down to 3 main capabilities that allow them to function with a degree of autonomy.

• First, they need tools to interact with the world and take action.

• Second, they require memory to learn from past interactions and maintain context.

• Third, they must have planning abilities to break down large goals into manageable steps and see them through to completion.

We are still in the early days of this agentic technology. However, it's clear that these three pillars, tool use, memory, and planning, are the foundation for creating reliable and genuinely useful AI agents that can act as more than just assistants but as autonomous digital workers.

Key Components of Modern AI Agents:

• Tool Use: This is the action-oriented part of an AI agent. It's how they go from processing information to actually doing things.

• Memory: An effective agent remembers past conversations, user preferences, and the context of previous tasks, unlike large language model (LLM) based AI chatbots. This allows them to become more efficient and personalized over time, learning from experience to improve their performance.

• Planning: This is what separates a simple bot from an autonomous system. Planning allows an AI agent to take a large, complex goal and break it down into a series of smaller, sequential sub-tasks. It's the cognitive engine that allows them to handle multi-step problems without constant human guidance.

Why These Capabilities Matter

• Consistency and Reliability: Tool use with strict protocols reduces errors when agents interface with internal systems.

• Context Awareness: Memory layers mean agents don't ask you to repeat yourself and can adapt responses based on your history.

• Autonomous Collaboration: Planning allows agents to take charge of multi-step workflows, coordinate resources, and even hand off tasks to other agents.

How Agents Use Tools

For an AI agent to be more than an AI chatbot, it needs the ability to interact with its environment. This is where tools come in. Think of tools as the agent's hands, allowing it to perform tasks in the digital world.

• APIs (Application Programming Interfaces): These are the direct way agents connect with other software and data sources. An agent can be taught to call an API to retrieve real-time stock data, book a flight, or update a customer record in a CRM system. This structured interaction is efficient and reliable, providing the agent with precise data and the ability to trigger defined actions.

• Functions: These are self-contained modules of code that an agent can execute to perform a specific task. For example, an agent could run a Python function to perform a complex calculation or to format data in a particular way, allowing developers to extend an agent's capabilities with custom logic.

• Data Stores: Agents often need to access information that goes past their initial training data. This is where data stores, such as vector databases, come into play. An agent can pull information from these databases to answer questions, get context, or access specialized knowledge.

Real-world examples of tool use are becoming more common. AI agents are now being used in e-commerce to track orders and send cart reminders, in finance to analyze market data, and in supply chain management to optimize logistics by analyzing real-time traffic and weather data.

The Importance of Memory

A key factor that makes an AI agent feel intelligent is its ability to remember. Without memory, every interaction would start from scratch. Like the human brain, an agent's memory can be separated into 2 main types: short-term and long-term.

Short-term Memory: For immediate context.

• Working Memory: This holds real-time information that is being actively used and operated, like remembering the last question in a conversation to provide a coherent answer.

• Cache Memory: This stores frequently accessed information that the agent can quickly reuse, reducing latency and improving efficiency.

Long-term Memory: Allows an agent to learn and grow over time.

• Episodic Memory: This is the agent's recollection of past interactions and experiences, similar to how humans remember specific events.

• Procedural Memory: This stores the processes for performing tasks, such as the steps needed to use a specific tool or the system prompts that guide its behavior.

• Semantic Memory: This is the agent's repository of external knowledge about the world, like facts and concepts.

By combining these memory types, an agent can maintain context in conversations, learn user preferences, and build upon past interactions to provide a more personalized and effective experience.

The Power of Planning

Planning is the cognitive function that allows an AI agent to tackle complex, multi-step goals with autonomy. Instead of reacting to one prompt at a time, a planning agent can plan a sequence of actions to achieve a larger objective.

Here are some of the methods agents use for planning:

• Task Decomposition: This is the fundamental step of breaking a large, complicated goal into smaller, more manageable sub-tasks. For example, a request to "plan a trip" would be broken down into booking flights, finding a hotel, and creating an itinerary.

• Multi-plan Selection: For a given task, an AI agent might generate several possible plans and then evaluate them to select the most efficient or effective one.

• External Planner: In some cases, specialized models trained specifically on planning data can develop a plan and then pass it to an agent for execution.

• Reflection and Refinement: This is a repetitive process where the agent can generate a plan, receive feedback (either from its own evaluation or a human), and then refine the plan, allowing the AI agent to learn from its mistakes and improve its planning capabilities over time.

• Memory Augmented Planning: This involves supplementing the planning process with information from the agent's long-term memory, allowing it to draw on past experiences to create better plans.

Through these planning methods, an AI tool becomes an autonomous AI agent capable of acting as a true problem solver, navigating complex workflows, and achieving goals with minimal human intervention.

Conclusion

What makes something an AI agent? There is no one answer to the question. Tool use, memory, and planning, all three combined, turn an AI tool into an AI agent. Yes, it is true that agentic AI technology is still developing and slowly improving; the above-mentioned 3 foundational components of tool use, memory, and planning are providing a clear path toward more capable and autonomous systems. AI agents have the potential to not only increase efficiency but also to become collaborative partners that enrich human capabilities. AI agents and agentic AI are our new reality.