What are AI agents, and how do they work?

An AI agent is a program that can perform tasks on behalf of a user, often with a level of autonomy and intelligence that mimics human-like behavior. Equipped with the capacity to analyze data and make decisions, these agents can learn from interactions and experiences, leading to improvements in performance over time. As they become more advanced, AI agents are increasingly integrated into various sectors. These sectors include customer service, healthcare, finance, and more, streamlining processes and enhancing efficiency.

The development of AI agents involves complex algorithms that enable them to process vast amounts of information rapidly. These algorithms are based on various fields of study such as machine learning, natural language processing, and neural networks. This allows AI agents to understand and respond to human language, recognize patterns in data, and even predict future outcomes based on historical information. As a result, they are becoming an indispensable tool for businesses and individuals looking to optimize their operations, offering tailor-made solutions and support.

Understanding the capabilities and limitations of AI agents is crucial for their effective implementation. While they offer numerous advantages, it is essential to recognize that they are not a panacea for all problems. Nevertheless, with ongoing advancements in technology, AI agents are poised to become even more intelligent and versatile, paving the way for new possibilities in automation and artificial intelligence.

Understanding AI agents

AI agents are systems that can perform tasks autonomously by interpreting data from the environment, making decisions based on that data, and executing actions to achieve set goals. They operate under predefined algorithms and machine learning models that enable them to learn and adapt over time.

Characteristics of AI agents:

• Autonomy: They operate without constant human intervention.
• Reactivity: They perceive their environment and respond in a timely fashion to changes.
• Proactivity: They take initiative and perform tasks towards their objectives.
• Social ability: They communicate with other agents or humans when necessary.

Types of AI agents:

1. Simple reflex agents: Act only on current perception.
2. Model-based reflex agents: Utilize an internal model to handle partially observable scenarios.
3. Goal-based agents: Act to achieve their goals.
4. Utility-based agents: Optimize their performance to maximize a utility function.‍
5. Learning agents: Adapt their actions based on past experiences and learning.

<table class="rich-text-table_component">
 <thead class="rich-text-table_head">
   <tr class="rich-text-table_row">
     <th class="rich-text-table_header">&nbsp;Agent type</th>
     <th class="rich-text-table_header">&nbsp;Example usage</th>
   </tr>
 </thead>
 <tbody class="rich-text-table_body">
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">&nbsp;Simple reflex</td>
     <td class="rich-text-table_cell is-text-center">
       &nbsp;Meeting scheduler
     </td>
   </tr>
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">
       &nbsp;Model-based reflex
     </td>
     <td class="rich-text-table_cell is-text-center">
       &nbsp;Identifying security breaches
     </td>
   </tr>
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">&nbsp;Goal-based</td>
     <td class="rich-text-table_cell is-text-center">
       &nbsp;Managing and monitoring projects for set objectives
     </td>
   </tr>
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">&nbsp;Utility-based</td>
     <td class="rich-text-table_cell is-text-center">
       &nbsp;Investment analysis tools
     </td>
   </tr>
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">&nbsp;Learning</td>
     <td class="rich-text-table_cell is-text-center">
       &nbsp;Personalized recommendation systems
     </td>
   </tr>
 </tbody>
</table>

As technology advances, AI agents are poised to become more integrated into daily activities, revolutionizing the ways tasks are performed and enhancing human capabilities.

AI agent functionality

AI agents are designed to perform tasks autonomously with a degree of intelligence, simulating the decision-making capabilities of humans within certain contexts.

How do AI agents work

AI agents operate based on a combination of algorithms and data inputs. They process information using machine learning models to interpret and react to their environment. Key functional components include:

• Data acquisition: AI agents acquire data through sensors or data intake mechanisms. This data serves as the foundation for all subsequent operations.
  
  
• Processing and analysis: Utilizing machine learning and artificial intelligence algorithms, the agent examines and draws insights from the data.
  
  
• Decision making: They make decisions based on the analysis, which can involve complex algorithms, rule-based logic, or predictive models.
  
  
• Action execution: Once a decision is made, the agent executes an action, which can be anything from updating a database to controlling a physical robot.

The workflow for an AI agent is often structured as follows:

1. Receive data: Obtain new information from the environment or a user.
2. Analyze data: Contextualize and interpret the information using AI models.
3. Decide on action: Determine the best course of action.
4. Act: Implement the decision through a response or a change in the environment.

An example of AI agent functionality in practice might be a customer service chatbot:

• Intake: Receives a customer query.
• Process: Understands the query using natural language processing.
• Decide: Choose an appropriate response based on the query context.
• Respond: Replies to the customer with information or further questions.

Through these mechanisms, AI agents are integral to automating complex tasks that require adaptability and learning capability.

AI agents in the enterprise

Within the enterprise environment, AI agents are transforming business operations by automating processes and providing insights that were previously unattainable.

Benefits in enterprise

1. Increased efficiency: AI agents can handle repetitive and time-consuming tasks, which allows employees to focus on more strategic work. For instance, chatbots can manage customer service inquiries, reducing the workload on human agents.

2. Enhanced decision-making: They analyze vast amounts of data to uncover trends and patterns that inform business decision-making. A company could use AI to forecast sales more accurately, leading to better stock management.

3. Cost reduction: By automating tasks, AI helps to reduce labor costs and minimize errors. For example, AI-powered predictive maintenance can anticipate equipment failures before they happen, thus saving on repair costs and downtime.

4. Scalability: AI agents are capable of scaling operations efficiently to meet changing business demands without the need for proportional increases in human resources.

5. Competitive advantage: Enterprises leveraging AI can stay ahead of the competition by adopting new technologies quickly and innovating their product offerings or operational processes.

• Improved customer experiences: Through personalized recommendations and swift customer service, AI agents are key in providing a tailored customer experience.
• Risk management: Analyzing potential risks and providing solutions or preemptive measures is another area where AI proves invaluable.

Classifying AI agents

In artificial intelligence, classification of AI agents is crucial for understanding their capabilities and the environments they are designed for.

Types of AI agents

AI agents can be categorized based on their operational sophistication and interaction with the environment. The following table provides a clear classification:

<table class="rich-text-table_component">
 <thead class="rich-text-table_head">
   <tr class="rich-text-table_row">
     <th class="rich-text-table_header">Type</th>
     <th class="rich-text-table_header">Reactivity</th>
     <th class="rich-text-table_header">Autonomy</th>
     <th class="rich-text-table_header">Capability</th>
   </tr>
 </thead>
 <tbody class="rich-text-table_body">
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">Simple Reflex</td>
     <td class="rich-text-table_cell is-text-center">
       Responds to current perceptions only.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Limited; follows preprogrammed rules.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Basic tasks; low adaptability.
     </td>
   </tr>
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">Model-based Reflex</td>
     <td class="rich-text-table_cell is-text-center">
       Considers internal states for decision making.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Some internal state handling ability.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Can handle partially observable environments.
     </td>
   </tr>
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">Goal-based</td>
     <td class="rich-text-table_cell is-text-center">
       Actions are taken to achieve specific goals.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Able to evaluate different action paths.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Better planning; deals with complex tasks.
     </td>
   </tr>
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">Utility-based</td>
     <td class="rich-text-table_cell is-text-center">
       Decisions are made to maximize utility.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Chooses actions that yield the highest benefit.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Balances task success with cost.
     </td>
   </tr>
   <tr class="rich-text-table_row">
     <td class="rich-text-table_cell is-text-center">Learning Agent</td>
     <td class="rich-text-table_cell is-text-center">
       Learns from experience to improve performance.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Adapts to new situations over time.
     </td>
     <td class="rich-text-table_cell is-text-center">
       Very adaptable; can become highly proficient.
     </td>
   </tr>
 </tbody>
</table>

Agents are also distinguished by the degree of their learning capabilities, from those that cannot learn, to those that learn from their interactions with the environment, continually improving their performance.

How can Glean, as an AI agent, help enterprises boost productivity?

Streamlining information retrieval

One of the primary benefits of an enterprise search platform is its ability to streamline information retrieval. Instead of searching through multiple databases or applications, employees can utilize a single search interface to access all relevant data. This saves time and minimizes the risk of errors or omissions.

Facilitating collaboration and knowledge sharing

Another significant advantage of enterprise search platforms is their ability to facilitate collaboration and knowledge sharing. By offering a centralized repository of information, employees can easily share knowledge and collaborate on projects. This fosters the breakdown of silos and enhances communication across departments and teams.

Personalized user experience

Furthermore, enterprise search platforms provide a personalized user experience. By employing machine learning algorithms to comprehend each user's search behavior and preferences, the platform can deliver more pertinent search results and suggestions. This aids employees in finding the required information swiftly and also enhances their engagement with the platform.

Customized generative AI experiences

Glean Apps and Actions empowers everyday users to create no-code custom generative AI agents and assistants tailored to specific business needs. Configurable entirely with natural language, build topic-specific agents, chatbots, and copilots that can also directly take action on a user's behalf to perform specific tasks. Learn more in our latest blog. 

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