An artificial intelligence (AI) agent is a system or program capable of autonomously performing tasks on behalf of a user or another system, designing its workflow and utilizing the available tools.

AI agents encompass a wide range of functionalities that go beyond natural language processing, including decision-making, problem-solving, interacting with external environments, and executing actions.

These agents can be deployed in various applications to solve complex tasks in business contexts, from software design and IT automation to code generation tools and conversational assistants. They use advanced natural language processing techniques from large language models (LLMs) to understand and respond to user inputs step by step and determine when to call external tools.

How AI Agents Work

At the core of AI agents are large language models (LLMs). For this reason, AI agents are often referred to as LLM agents. Traditional LLMs, such as IBM® Granite™ models, generate responses based on the data used to train them and are limited by knowledge and reasoning constraints. In contrast, agentic technology uses backend tool calls to obtain up-to-date information, optimize workflow, and autonomously create subtasks to achieve complex goals.

Stages of the AI Agent Process

1. Initialization and Goal Planning: While AI agents are autonomous in their decision-making processes, they require human-defined goals and environments. The key factors influencing an autonomous agent’s behavior are:
   • The team of developers who design and train the agentic AI system.
   • The team that deploys the agent and provides user access.
   • The user who sets specific goals for the AI agent and defines the tools available for use.
     Given the user’s goal and the tools available to the agent, it performs task decomposition to improve performance, creating a plan of tasks and subtasks to achieve the complex objective.
2. Reasoning Using Available Tools: AI agents base their actions on the information they perceive. Often, they do not have the complete knowledge base required to address all the subtasks within a complex objective. To remedy this, AI agents use available tools such as external datasets, web searches, APIs, and even other agents. Once the missing information is retrieved, the agent can update its knowledge base and adjust its action plan.
3. Learning and Reflection: AI agents use feedback mechanisms, such as other AI agents and human-in-the-loop (HITL) intervention, to improve the accuracy of their responses. After forming their response to the user, the agent stores the learned information along with user feedback to enhance its performance and adapt to the user’s preferences for future goals. These feedback mechanisms improve the AI agent’s reasoning and accuracy, in a process known as iterative refinement.

AI Agents vs. Non-Agentic Chatbots

AI chatbots use conversational AI techniques, such as natural language processing (NLP), to understand user queries and automate responses. Non-agentic chatbots lack tools, memory, and reasoning. They can only achieve short-term goals and cannot plan for the future. On the other hand, agentic chatbots learn to adapt to user expectations over time, providing a more personalized experience and more comprehensive responses.

Types of AI Agents Simple Reflex Agents

1. Simple Reflex Agents: These simplest agents base their actions on current perception and have no memory. They operate with a set of reflexes or pre-programmed rules.
2. Model-Based Reflex Agents: These agents use both current perception and memory to maintain an internal model of the world. They can operate in partially observable and changing environments.
3. Goal-Based Agents: They have an internal model of the world and a set of goals. They seek action sequences to achieve these goals and plan actions before acting.
4. Utility-Based Agents: These agents select the action sequence that achieves the goal and maximizes utility or reward, calculated using a utility function.
5. Learning Agents: In addition to the capabilities above, learning agents can learn from new experiences and improve their ability to operate in unfamiliar environments.

AI Agent Use Cases

1. Customer Experience: Embedded in websites and applications, AI agents can act as virtual assistants, provide mental health support, simulate interviews, and perform other related tasks.
2. Healthcare: AI agents can be used in real-world health applications, from treatment planning to medication process management.
3. Emergency Response: In natural disasters, AI agents can use deep learning algorithms to retrieve information from social media users who need rescue, mapping their locations to assist rescue services.

Benefits of AI Agents

1. Task Automation: With advances in generative AI, AI agents can efficiently automate complex tasks, achieving goals quickly and at scale.
2. Increased Performance: Multi-agent frameworks often outperform individual agents, allowing for greater collaboration and learning, improving information synthesis.
3. Response Quality: AI agents provide more comprehensive, accurate, and personalized responses than traditional AI models.

Risks and Limitations

1. Multi-Agent Dependencies: Multi-agent systems can experience shared failures and vulnerabilities if not properly implemented.
2. Infinite Feedback Loops: Agents that cannot create a comprehensive plan may repeatedly call the same tools, leading to infinite feedback loops.
3. Computational Complexity: Building AI agents from scratch can be time-consuming and resource-intensive.

AI agents are revolutionizing automation by offering advanced capabilities for reasoning, learning, and adapting. While they present challenges such as computational complexity and multi-agent management, practices like human oversight and record-keeping can mitigate these risks. In summary, with proper implementation, AI agents can transform processes and improve efficiency across multiple sectors, blending the best of technology with a touch of human control.