What are AI Agents?

AI agents can be defined as autonomous units that utilise artificial intelligence to perform tasks, make decisions, and interact with their environment. They are designed to operate independently and can handle increasingly complex tasks with minimal human intervention, by leveraging custom-built algorithms and Large Language Models to analyse data, learn from previous experiences, and execute actions based on predefined goals.

Components of AI Agent

AI agents are sophisticated systems that leverage various components to perform complex tasks effectively with minimal human intervention. The components that comprise an AI Agent are as follows,

1. Agent Core

The agent core is the central processing unit of an AI agent, it typically consists of a Large Language Model like Llama 3.1 or GPT 4o or any other equivalent model.

The core interprets user inputs, extracting intent and context. (Natural Language Understanding). It generates coherent and contextually relevant responses or creates a plan of action based on the input and internal logic. (Natural Language Generation)

The quality and capabilities of the agent core directly influence the agent's performance, accuracy, and ability to engage in meaningful conversations.

2. Memory Module

The memory module allows the agent to retain information across interactions, enhancing its contextual awareness and personalisation.

There are two types of memory

• Short-term Memory: Temporarily holds context from ongoing interactions, enabling the agent to maintain a coherent dialogue.
• Long-term Memory: Stores significant information from past interactions, user preferences, and learned knowledge, which can be referenced in future interactions.

Memory helps the agent remember user preferences and previous conversations, leading to more personalised interactions. It also allows the agent to improve its responses and strategies over time based on past experiences.

3. Tools

Tools are external functionalities that AI agents can invoke to perform specific tasks or retrieve information.

Tools can be of various types like,

• APIs: Interfaces that allow the agent to access external data sources, such as weather services, databases, or web scraping tools.
• Execution Environments: Environments where the agent can run code or scripts to perform calculations or data processing.
• Custom Built Algorithm: Tools integrating custom business logic and rule-based code, specifically tailored to meet the unique needs of a particular use case.

Tools enable the agent to go beyond text generation, allowing it to execute tasks like retrieving real-time data, performing calculations, or interacting with other software. Integrating various tools makes the agent adaptable to different domains, from finance to healthcare.

4. Reasoning Loop

The reasoning loop consists of a series of prompts that give the large language model detailed instructions, enabling it to make well-informed decisions.

The Reasoning Loop is divided into 4 components:

• Role: Agents can assume various roles, each uniquely contributing to the goals at hand. For example, a Researcher focuses on gathering and analysing information, while a Writer creates content based on the research. A Customer Support agent engages with users to address inquiries and provide assistance. Each role is essential, working together to achieve the overall objectives efficiently.
• Backstory: The backstory provides details about an agent's knowledge base, which includes general information, domain-specific expertise, its identity, expertise, and its purpose.
• Planning: The planning module determines the best actions for an agent to achieve its goals, especially in complex tasks. It breaks down large, multi-step tasks into smaller, manageable components for easier sequential execution. By establishing clear objectives based on user requests, the module identifies the optimal path to reach those goals.
• Reflection/Self-Review: After completing a task or interaction, the agent engages itself in self-reflection, by analysing the effectiveness of the chosen strategy and identifying areas for improvement. This allows the agent to adapt its strategies based on changing circumstances, user feedback, and new information, ensuring that the agent remains relevant and effective over time.