What are AI agents?

What Are AI Agents?

Artificial Intelligence (AI) agents are systems or programs capable of autonomously performing tasks by designing workflows and leveraging available tools on behalf of users or other systems. These agents go beyond natural language processing, incorporating functionalities like decision-making, problem-solving, environmental interaction, and action execution.

AI agents are versatile and find applications across diverse domains, including software design, IT automation, code generation, and conversational assistance. By employing advanced natural language processing techniques powered by large language models (LLMs), these agents process user inputs step-by-step and determine when to utilize external tools for better task execution.

How AI Agents Work

At the heart of AI agents are LLMs, often referred to as LLM agents. Unlike traditional LLMs—such as IBM® Granite™ models—that generate responses based on their training data, agentic technology enhances capabilities by using backend tool calls. respell – run your business with agentic ai workflows. This approach retrieves up-to-date information, optimizes workflows, and autonomously creates subtasks to achieve complex objectives.

Through this process, AI agents adapt to user expectations over time. They store past interactions in memory, plan future actions, and deliver personalized and comprehensive responses without requiring human intervention. Here’s how AI agents typically operate:

1. Goal Initialization and Planning

Although autonomous in decision-making, AI agents need human-defined goals and environments to function. Key influences on agent behavior include:

• Developers who design and train the system.
• Deployers who make the system accessible.
• Users who provide specific goals and establish available tools.

Given the user’s input, the AI agent decomposes tasks into smaller subtasks for improved performance. While simple tasks may not require planning, complex goals benefit from detailed task decomposition and iterative refinement.

2. Reasoning with Available Tools

AI agents act based on perceived information. When lacking sufficient knowledge, they utilize external tools like datasets, web searches, APIs, or even other agents to gather missing information. This iterative process allows the agent to reassess and adjust its actions as needed.

Example Use Case:
A user planning a surfing trip tasks an AI agent with predicting the best week for high tides and sunny weather in Greece. Since the LLM lacks expertise in weather patterns, it retrieves historical weather data. For further refinement, the agent consults a specialized surfing agent to identify optimal conditions. Finally, the combined insights allow the agent to recommend the best week for the trip.

3. Learning and Reflection

AI agents improve over time through feedback mechanisms, such as other AI agents or human-in-the-loop (HITL) systems. Feedback enhances reasoning and accuracy, a process known as iterative refinement.

Example Use Case Continued:
The surfing agent stores user feedback and learned insights for future interactions. If other agents contributed to achieving the goal, their feedback is also incorporated, minimizing the need for repetitive human direction. This cumulative knowledge is stored in a knowledge base to avoid repeating mistakes and improve long-term performance.

AI agents excel at adapting to user preferences, solving complex tasks, and continuously refining their performance through learning and reflection, setting them apart from traditional AI systems.

Types of AI Agents

AI agents come with varying levels of complexity, depending on their capabilities and use cases. For simpler tasks, basic agents may be sufficient to avoid unnecessary computational demands. The five primary types of AI agents, ranked from simplest to most advanced, include:

1. Simple Reflex Agents

Simple reflex agents base their actions solely on current perceptions. They lack memory and do not interact with other agents or tools to gather missing information. These agents operate on predefined condition-action rules and are effective only in fully observable environments.

Example: A thermostat programmed to activate heating at 8 PM every night based on a simple rule: if the time is 8 PM, then turn on heating.

2. Model-Based Reflex Agents

Model-based reflex agents combine current perceptions with memory, enabling them to maintain an internal model of the world. This allows them to adapt to partially observable and dynamic environments. However, their decision-making remains guided by preset rules.

Example: A robot vacuum cleaner that navigates a room while avoiding obstacles, such as furniture. It stores a map of cleaned areas in memory to avoid redundant cleaning.

3. Goal-Based Agents

Goal-based agents incorporate an internal model of the world and are designed to achieve specific goals. They plan action sequences to reach their objectives, making them more effective than reflex-based agents.

Example: A navigation system that determines the fastest route to a destination. It evaluates multiple paths and adjusts recommendations if a quicker route is identified.

4. Utility-Based Agents

Utility-based agents not only achieve goals but also maximize utility or rewards by selecting the optimal course of action based on a utility function. This function assigns a value to each action based on criteria such as efficiency, cost, or time.

Example: A navigation system that factors in fuel efficiency, traffic, and toll costs to recommend the most favorable route to a destination.

5. Learning Agents

Learning agents have all the capabilities of the other agent types but are distinguished by their ability to learn autonomously. They continuously update their knowledge base through new experiences, improving their adaptability and performance over time. Learning agents typically include four components:

• Learning Element: Updates the agent’s knowledge based on its environment.
• Critic: Provides feedback on performance quality.
• Performance Element: Executes actions based on learned knowledge.
• Problem Generator: Proposes new actions for exploration and improvement.

Example: E-commerce recommendation systems that track user activity and preferences to suggest personalized products. Each interaction refines the agent’s recommendations, enhancing accuracy over time.

Use Cases of AI Agents

1. Customer Experience:
   AI agents enhance websites and apps by serving as virtual assistants, offering mental health support, simulating interviews, and more. No-code templates make it easy to implement these solutions.
2. Healthcare:
   AI agents assist in patient treatment planning, drug management, and emergency department workflows, saving time for medical professionals to focus on critical tasks.
3. Emergency Response:
   During natural disasters, AI agents use deep learning to locate individuals in need of rescue through social media data, mapping their locations to aid emergency services.

Benefits of AI Agents

1. Task Automation:
   AI agents can handle complex tasks autonomously, reducing costs, time, and the need for human intervention.
2. Enhanced Performance:
   Multi-agent frameworks leverage collaboration among agents specializing in different domains, improving learning and decision-making processes.
3. Improved Response Quality:
   AI agents provide accurate, comprehensive, and personalized responses by integrating information from external tools and updating their knowledge base.

Risks and Limitations

1. Multi-Agent Dependencies:
   Dependencies between agents can lead to system-wide failures if shared vulnerabilities are exploited.
2. Infinite Feedback Loops:
   Agents may repeatedly call the same tools when unable to resolve a task, creating redundancies.
3. Computational Complexity:
   Developing high-performance AI agents is resource-intensive and can take significant time for training and task execution.

Best Practices

1. Activity Logs:
   Maintain logs of agent actions to provide transparency and facilitate error identification.
2. Interruptibility:
   Implement mechanisms to allow users to interrupt agents gracefully, especially in cases of malfunction or infinite loops.
3. Unique Identifiers:
   Assign unique identifiers to agents for traceability and accountability, reducing the risk of malicious use.
4. Human Supervision:
   Incorporate occasional human feedback to help agents learn effectively and require human approval for high-stakes decisions.

By following these practices, AI agents can operate safely and effectively, delivering significant benefits across industries.

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