What are AI Agents?
An artificial intelligence (AI) agent is a software program that can interact with its environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals. Humans set goals, but an AI agent independently chooses the best actions it needs to perform to achieve those goals. For example, consider a contact center AI agent that wants to resolves customer queries. The agent will automatically ask the customer different questions, look up information in internal documents, and respond with a solution. Based on the customer responses, it determines if it can resolve the query itself or pass it on to a human.
What are the key principles that define AI agents?
All software autonomously completes different tasks as determined by the software developer. So, what makes AI or intelligent agents special?
AI agents are rational agents. They make rational decisions based on their perceptions and data to produce optimal performance and results. An AI agent senses its environment with physical or software interfaces.
For example, a robotic agent collects sensor data, and a chatbot uses customer queries as input. Then, the AI agent applies the data to make an informed decision. It analyzes the collected data to predict the best outcomes that support predetermined goals. The agent also uses the results to formulate the next action that it should take. For example, self-driving cars navigate around obstacles on the road based on data from multiple sensors.
What are the benefits of using AI agents?
AI agents can improve your business operations and your customers' experiences.
Improved productivity
AI agents are autonomous intelligent systems performing specific tasks without human intervention. Organizations use AI agents to achieve specific goals and more efficient business outcomes. Business teams are more productive when they delegate repetitive tasks to AI agents. This way, they can divert their attention to mission-critical or creative activities, adding more value to their organization.
Reduced costs
Businesses can use intelligent agents to reduce unnecessary costs arising from process inefficiencies, human errors, and manual processes. You can confidently perform complex tasks because autonomous agents follow a consistent model that adapts to changing environments.
Informed decision-making
Advanced intelligent agents use machine learning (ML) to gather and process massive amounts of real-time data. This allows business managers to make better predictions at pace when strategizing their next move. For example, you can use AI agents to analyze product demands in different market segments when running an ad campaign.
Improved customer experience
Customers seek engaging and personalized experiences when interacting with businesses. Integrating AI agents allows businesses to personalize product recommendations, provide prompt responses, and innovate to improve customer engagement, conversion, and loyalty.
What are the key components of AI agent architecture?
Agents in artificial intelligence may operate in different environments to accomplish unique purposes. However, all functional agents share these components.
Architecture
Architecture is the base the agent operates from. The architecture can be a physical structure, a software program, or a combination. For example, a robotic AI agent consists of actuators, sensors, motors, and robotic arms. Meanwhile, an architecture that hosts an AI software agent may use a text prompt, API, and databases to enable autonomous operations.
Agent function
The agent function describes how the data collected is translated into actions that support the agent’s objective. When designing the agent function, developers consider the type of information, AI capabilities, knowledge base, feedback mechanism, and other technologies required.
Agent program
An agent program is the implementation of the agent function. It involves developing, training, and deploying the AI agent on the designated architecture. The agent program aligns the agent’s business logic, technical requirements, and performance elements.
How does an AI agent work?
AI agents work by simplifying and automating complex tasks. Most autonomous agents follow a specific workflow when performing assigned tasks.
Determine goals
The AI agent receives a specific instruction or goal from the user. It uses the goal to plan tasks that make the final outcome relevant and useful to the user. Then, the agent breaks down the goal into several smaller actionable tasks. To achieve the goal, the agent performs those tasks based on specific orders or conditions.
Acquire information
AI agents need information to act on tasks they have planned successfully. For example, the agent must extract conversation logs to analyze customer sentiments. As such, AI agents might access the internet to search for and retrieve the information they need. In some applications, an intelligent agent can interact with other agents or machine learning models to access or exchange information.
Implement tasks
With sufficient data, the AI agent methodically implements the task at hand. Once it accomplishes a task, the agent removes it from the list and proceeds to the next one. In between task completions, the agent evaluates if it has achieved the designated goal by seeking external feedback and inspecting its own logs. During this process, the agent might create and act on more tasks to reach the final outcome.
What are the challenges of using AI agents?
AI agents are helpful software technologies to automate business workflows for better outcomes. That being said, organizations should address the following concerns when deploying autonomous AI agents for business use cases.
Data privacy concerns
Developing and operating advanced AI agents requires acquiring, storing, and moving massive volumes of data. Organizations should be aware of data privacy requirements and employ necessary measures to improve data security posture.
Ethical challenges
In certain circumstances, deep learning models may produce unfair, biased, or inaccurate results. Applying safeguards, such as human reviews, ensures customers receive helpful and fair responses from the agents deployed.
Technical complexities
Implementing advanced AI agents requires specialized experience and knowledge of machine learning technologies. Developers must be able to integrate machine learning libraries with software applications and train the agent with enterprise-specific data.
Limited compute resources
Training and deploying deep learning AI agents requires substantial computing resources. When organizations implement these agents on-premise, they must invest in and maintain costly infrastructure that is not easily scalable.
What are the types of AI agents?
Organizations create and deploy different types of intelligent agents. We share some examples below.
Simple reflex agents
A simple reflex agent operates strictly based on predefined rules and its immediate data. It will not respond to situations beyond a given event condition action rule. Hence, these agents are suitable for simple tasks that don’t require extensive training. For example, you can use a simple reflex agent to reset passwords by detecting specific keywords in a user’s conversation.
Model-based reflex agents
A model-based agent is similar to simple reflex agents, except the former has a more advanced decision-making mechanism. Rather than merely following a specific rule, a model-based agent evaluates probable outcomes and consequences before deciding. Using supporting data, it builds an internal model of the world it perceives and uses that to support its decisions.
Goal-based agents
Goal-based agents, or rule-based agents, are AI agents with more robust reasoning capabilities. Besides evaluating the environment data, the agent compares different approaches to help it achieve the desired outcome. Goal-based agents always choose the most efficient path. They are suitable for performing complex tasks, such as natural language processing (NLP) and robotics applications.
Utility-based agents
A utility-based agent uses a complex reasoning algorithm to help users maximize the outcome they desire. The agent compares different scenarios and their respective utility values or benefits. Then, it chooses one that provides users with the most rewards. For example, customers can use a utility-based agent to search for flight tickets with minimum traveling time, irrespective of the price.
Learning agents
A learning agent continuously learns from previous experiences to improve its results. Using sensory input and feedback mechanisms, the agent adapts its learning element over time to meet specific standards. On top of that, it uses a problem generator to design new tasks to train itself from collected data and past results.
Hierarchical agents
Hierarchical agents are an organized group of intelligent agents arranged in tiers. The higher-level agents deconstruct complex tasks into smaller ones and assign them to lower-level agents. Each agent runs independently and submits a progress report to its supervising agent. The higher-level agent collects the results and coordinates subordinate agents to ensure they collectively achieve goals.
How can AWS help with your AI agent requirements?
Amazon Connect Contact Lens is an autonomous AI agent your organization can use to manage and generate real-time contact center analytics. You can automatically create contact summaries and uncover customer analytics trends. Here's how:
- Amazon Connect Contact Lens automatically detects and redacts sensitive customer data in customer conversations to improve compliance efforts
- Supervisors can automatically review human agents from the conversational analytics that Amazon Connect Contact Lens generates
- The agent uses NLP technologies to capture and analyze customer sentiments from the words they use
Organizations can also use generative artificial intelligence (generative AI) and other Amazon Web Services (AWS) AI services to build their own AI agents. AWS helps you overcome technical, infrastructure, and compliance challenges by providing you managed tools to build, integrate, and scale autonomous agents. For example:
- Amazon Bedrock provides easy access to industry-leading generative AI models, such as Claude, Llama 2, and Amazon Titan
- Amazon SageMaker allows you to experiment with, build, test, and deploy AI agents with ready-to-deploy and customizable ML algorithms
- Train, operate, and scale your AI agents on AWS Trainium, a purpose-built ML learning accelerator for deep learning models
Get started with AI agents on AWS by creating an account today.
