What is an AI agent? What should I know about them?
You might see the terms “AI Agent,” “Agentive Process Automation,” “Artificial Intelligence Agent,” or even some other term. What does it all mean?
An AI agent is a software program that autonomously gathers data and carries out tasks using this information, independently or on behalf of another system or person. These agents can perform a variety of functions, such as:
- Making decisions
- Solving problems
- Interacting with external environments
- Taking actions
In essence, the agent carries out tasks independently to achieve set goals.
Let’s take a deeper look at AI agents.
What makes something an AI agent?
AI agents are rational systems that make decisions based on their perceptions and data to achieve optimal outcomes. They interact with their environment through physical or software interfaces.
An AI agent analyzes the data it collects to predict the optimal outcome, allowing it to make informed decisions that align with predefined goals.
What is an example of an AI agent?
AI agents can be used for a variety of tasks in many different industries. For example, a chatbot in a credit card portal might ask the customer if they are looking for information about paying their bill, a charge, or increasing their credit line.
By asking them questions and then determining the next question based on the previous reply and gathering information from customer documents and profiles, the bot can provide a solution to the customer. It may pass it on to a human if it cannot resolve the problem using known methods.
Another example might be in healthcare. Let’s assume that you have models trained to find the signs of specific diseases by looking at images from ultrasounds or scans. Using an AI agent, scans from a new patient can be reviewed by the model and a summary of the findings can be provided for the doctors to review and make recommendations on therapies and medications.
You might see an AI agent that analyzes a patient’s history, illnesses, symptoms, and even hereditary diseases to provide some initial insight for the physicians in treatment plans or possible diagnoses for the patient.
AI agents are also great in financial services for fraud detection, prevention, and credit risk assessment tasks. With proper AI agents, your organization can uncover abnormalities and alert someone to possible fraud, reducing financial losses. Similarly, for high-risk credit applicants, AI agents can help to make that determination and can even continuously monitor existing customers for credit risk.
Even your smart home devices are AI agents. All AI agents help make decisions, provide information, and take action based on the data they have collected to help in that decision-making.
Why should we use AI agents?
With the right AI agents, you can improve the customer user experience, improve your decision-making, reduce costs, increase productivity, automate repetitive tasks, error reduction, and scalability.
AI agents can run continuously without breaks, unlike humans. This makes them very beneficial for businesses that require 24/7 operations like customer support and monitoring. Adding this sophisticated technology to your business provides scalability and consistency to your processes because AI agents don’t get fatigued and run without variation, ensuring all tasks are completed the same way every time. Deploying AI agents also frees your staff to work on more critical tasks.
With the addition of AI to your business, you can also gather significant insights into your business by identifying patterns and trends. These insights can help support strategic planning initiatives, support decision-making, and provide predictive analytics. These things are further enhanced because AI agents can learn from their experiences and improve over time, becoming increasingly effective.
How does an AI agent work?
An AI agent simplifies and automates more complex tasks or processes. They are designed to take inputs, make decisions, and then take actions to meet the goals outlined for the agent.
When implementing AI agents, you need to:
- Determine the goal of the agent
You will want to determine the goal of the agent. For example, is the AI agent intended to return a credit score, provide a list of possible locations, or possibly trigger a fraud investigation? Once you know the designed goal of the agent, you can begin to focus on the implementation of that agent. - Determine how the AI agent will obtain information to accomplish this goal
Many AI agents take input from various sources and media types including images, scanned documents, electronic documents, audio, etc. Part of implementing the AI agent includes determining the input sources and how the AI agent will receive or access this information.
For example, suppose the AI agent to return a credit score is used in a loan application process. In that case, the information required to make that determination is likely stored in process variables and possibly documents like proof of income, W2, bank statements, etc. - Define how the AI agent will implement the task(s) using the information provided
Now you need to define how the information provided will be used to return the desired result. This may include accessing rules or machine learning models.
Using these predetermined options for decision-making, you can then determine the actions to take and how to return the results. This might be updating a process variable or updating another application. - Determine if feedback is required
Consider allowing your AI agent to provide learning feedback in addition to acting on data and providing a result. This is very useful when you’re taking advantage of machine learning models as you want to be sure that the model can continuously learn and adapt responses.
Don’t get hung up on terminology
AI agents are just programs that run autonomously to complete a specific task or set of tasks using AI. So, an AI agent might be as simple as the program written for your smart device that provides a weather report when you ask “What’s my weather today?” Alternatively, it might be a decision-making agent that uses predetermined rules to provide a decision based on incoming information.
It can also be more sophisticated and run predictive models based on incoming data to determine if an individual is a valid credit risk for a loan or might run sentiment analysis to determine how to rate the feedback provided by a customer for specific interaction.
The biggest thing to remember is that AI agents run autonomously and are often used to automate previously tedious or human tasks that may be slow or menial so that you can streamline your process and let your staff focus on more business-critical work.
How Camunda can help
Camunda provides a wide range of out-of-the-box AI agents and the ability to build your own. For example, as displayed below, you may have an agent that reviews your CRM and other customer data and creates the proper profile in a Know Your Customer (KYC) AI agent.
You may also deploy an AI agent to review incoming information for intelligent routing of your process as shown with the Intelligent Routing (AI) agent in the process below.
In this KYC example, a manual review step is used if a customer is not validated. This can be removed or replaced with automation to make the AI agent completely autonomous.
With Camunda, you can orchestrate a process by incorporating AI agents into your BPMN-modeled processes to enhance, streamline, and improve your process orchestrations. Camunda provides a composable automated platform with embedded intelligence allowing your organization to pick and choose the best AI agents for a task resulting in optimal business automation.
The bottom line
Don’t get confused by fancy marketing terms and slogans. AI agents have been part of the Camunda stack for quite some time. With our offering, you can orchestrate your processes with the best AI agent for the task at hand resulting in streamlined, flexible orchestration to meet the challenges of a changing market.
If you want to review some examples of using AI agents, please check out the following:
- How Decision Modeling and AI Help with Risk Assessment
- Accessing Machine Learning Models with Camunda’s Hugging Face Connector for Business Improvement
- Demo: How to Streamline Processes and Automate Human Tasks with Machine Learning Models
Want to give it a try?
Learn more about how Camunda can help you implement AI agents in your processes by reviewing your process diagram development needs with a free trial. Camunda is a powerful and flexible process orchestration platform that can help you automate your underwriting processes and drive lasting value.
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