Artificial intelligence (AI) is rapidly transforming our day-to–day life. AI is built into our phones with Apple’s Siri and Google Assistant, and there’s a good chance you may have used an AI model to send an email you had long put off writing.
Just as AI is reshaping our lives as individuals, it’s changing how businesses operate.
AI is already being used to automate simple workflows, saving employees time and freeing them up to handle complex, high-level tasks that generate real value for businesses. But this isn’t the full extent of what AI is capable of.
Now, there’s an opportunity for AI to perform complex tasks, incorporate wider ranges of data across an organization, and inform decision-making in ways that even the Jetsons would doubt possible. At the core of this change are AI agents.
What are AI agents?
AI agents are software systems that are designed to operate autonomously, taking information from various sources to execute complex, multi-step tasks with minimal human oversight.
Unlike chatbots or virtual assistants, AI agents are adaptable. They are capable of leveraging tools, processing multimodal media (like text, audio, and video), and their own memory to learn and iterate on their processes.
An AI agent operates like a digital employee: if you give it a complex task, it’s capable of breaking it down into steps and finding the information it needs to complete the work. This means less handholding and more autonomous work.
Key elements of AI agents
An AI agent is generally defined as having the following traits:
- Autonomy: An AI agent can operate independently without human assistance. If given a specific task or outcome, it’s capable of both figuring out and executing the procedure to achieve the result, only relying on human intervention in ambiguous situations.
- Memory: Previous conversations, tasks, workflows, and data are all retained by an AI agent for ease of access. This guarantees continuity and consistency in results.
- Learning and adaptation: AI agents used retained information to repeat patterns. This also means it learns, adapts, and improves over time based on feedback that’s provided.
- Goal-oriented behavior: Give the AI agent an objective, and it will work towards it without relying on micromanagement or responding to prompts. It doesn’t rely on directions to get to a destination; it’s capable of planning the most optimal directions itself.
- Tool use: AI agents are capable of integrating with various tools and software to execute their work across platforms. It could access data from one platform, calculate and compile data, and then draft it into an email, all in one workflow.
- Logic and planning: When faced with a problem, an AI agent leverages past information and logic to plan its work before executing. It doesn’t need step-by-step instructions to complete a task, it’s capable of planning the steps itself.
- Environmental understanding: AI agents take in information from a wide array of sources and compile it to get the full picture before executing their work. They’re capable of processing text, audio, and video into structured data, like referencing an email and a logged phone call when drafting a scope of work.
- Iterative and unique: Given that an AI agent is constantly operating off of your data and adapting based on feedback, it ultimately becomes tailored to your needs purely through repetition.
How AI agents work
Getting the most value from an AI agent requires a fundamental understanding of how AI agents work. To explain how an AI agent works, let’s break down its functionality into nine core principles.
Persona
The persona of an AI agent defines its communication and behaviors. This is how the agent interacts with other systems (e.g., its permissions) and presents information to its end user.
Think of persona as how the AI agent is tailored to its users. For example, a customer service agent is empathetic and understanding, while a financial analyst agent is factual, analytical, and to-the-point.
Memory
Anything that an AI agent processes is retained in a knowledge repository and accessible in future requests. Past interactions, accessed information, and contextual details are all incorporated in an AI agent’s planning and execution of a request.
The use of memory maintains continuity and consistency in the AI agent’s output. If you’re requesting similar workflows on a repeated basis, it will only become better and more efficient at completing that request.
Model
The “model” of an AI agent refers to the AI system it's built on. Some models are better suited for certain tasks, with unique strengths in specific types of reasoning.
The first step you should take in building or choosing an AI agent is defining the use case of the AI agent. For example, a number-crunching AI wouldn’t need the language capabilities of an AI agent used for drafting emails.
Tools
AI agents are capable of accessing external resources to complete their work. With proper integrations, an AI agent could pull information from your accounting platform, verify it against an employee expense report, and compile its findings in an email to the head of finance, all without a human’s intervention.
Goal-orientation
The goal provided to an AI agent serves as a guide for all its work. Whether the goal is explicitly provided for the task or otherwise established as an organizational objective, the agent operates independently to achieve it, using judgment to determine the best approach.
Perception
Perception refers to the agent’s ability to gather information across the sources it has access to in order to complete its work. Even if not explicitly stated, the AI agent may use environmental context to execute a workflow.
How the AI agent perceives and interacts with its environment is stored in its memory repository, meaning it knows where to find information specific to a request, even if not explicitly stated.
Decision-making & planning
AI agents are capable of processing complex tasks because of their ability to weigh out all possible options and select the optimal approach. They can process options based on risk, trade-offs, and the context of individual aspects of the workflow, developing a multi-step process throughout its planning.
Action
Once the planning phase is complete, the AI agent moves into the action phase, executing its plan and providing the deliverables. Throughout, it will access the tools as needed to complete the work as requested.
Learning
Once the work is complete, the AI agent registers the work and its outcome in its memory repository. It notes where the work succeeded or failed in order to adjust its process.
There’s also an opportunity for human feedback, registering how successful the workflow was. Positive feedback reinforces the process, while negative feedback pushes the AI agent to reevaluate its work and take a different approach with future requests.
Differences between AI agents, assistants, and bots
AI agents, assistants, and bots are terms often used interchangeably, but represent different applications of AI technology.
AI agents are autonomous and independent. When given a complex task, an AI agent can access tools and information across the organization to plan and execute its workflow. This means they require less maintenance and micromanagement, always learning and adapting to process open-ended tasks better.
AI assistants are designed to help with simple tasks with explicit, conversational instructions. These are increasingly common in smartphones and home devices, with examples like Siri, Alexa, and Google Assistant. They can answer questions or complete one-time tasks like setting a timer or adding an event to a calendar.
Bots are the simplest form of AI technology, relying on rule-based programming to execute the function it was designed for. Using prescriptive decision trees, it executes a workflow, but may break or provide faulty outputs if the work doesn’t match its design perfectly.
You can think of bots, assistants, and AI agents as being on a spectrum. Bots occupy one side, with simple programming and an inability to learn or reason. On the other end are AI agents, which have high-functioning memory and reasoning used to complete tasks without explicit direction.
Types of AI agents
AI agents are categorized based on their foundational model, operating abilities, and intelligence. There are five main buckets that AI agents are categorized into:
- Single agents operate independently, working in a silo to complete their work. They do not interact with any other AI entities in their workflows and only require human intervention in ambiguous situations.
- Multi-agent systems use multiple AI agents that coordinate and collaborate to achieve a result. Each agent may be specialized, with tasks divvied up based on their specialization, learning and adapting within their niche.
- Reactive agents operate on a stimulus-response framework, only taking action if something triggers them. An example is an AI agent that reconciles transactions, flagging any expenses that could be fraudulent.
- Deliberative agents maintain an understanding of an environment, using it as the framework to plan and strategize. They hold “beliefs” about their environment, which inform their planning to accomplish their goals. This includes thinking ahead and understanding knock-on effects within the environment.
- Hybrid agents use a combination of reactive and deliberative approaches, depending on what the situation calls for. They may use deliberative abilities for complex modeling and reactive abilities for fast action when necessary.
Each of these AI agent types is best used in different situations. For example, you may want to use AI agents to process transactions, develop financial forecasts, and set budgets based on their predictions. A reactive agent is best suited to process transactions and flag potential fraud, while a deliberative agent is better suited for modeling and building a plan based on its prediction.
Examples of AI agent use cases
To better illustrate how different types of AI agents can be leveraged in your operations, let’s go over four specific use cases already being used by businesses today.
Customer service agents
From processing simple requests to in-depth conversations, customer service agents handle requests and inquiries from your customer base. They pull information from a variety of internal databases to find answers to questions, treating each prompt as an executable.
As with all AI agents, when an answer isn’t possible, customer service agents can raise ambiguous situations to human reps who intervene to provide the support needed.
Business process automation
Business process automation refers to the ability of an AI agent to process complete workflows from start to finish, without human intervention. For example, an AI agent could streamline employee expense reimbursement, forwarding requests to necessary stakeholders before reconciling the transaction on the books and organizing a payout.
Data analysis
For analysis that requires information across different platforms and databases, AI agents are capable of independently processing requests or repeated tasks to organize, present, and analyze data. Taken a step further, they can identify trends and flag potential disruptions so the business knows it needs to adapt.
Research and development
Expedite innovation by using an AI agent to perform continuous reviews of competitors, literature, and other sources to generate new hypotheses and opportunities for development. Working alongside human researchers, they’re capable of executing complex tasks for faster findings and iteration.
Benefits of using AI agents
Organizations are taking advantage of AI agents in their operations because of the following benefits they provide.
Efficiency and productivity
AI agents are the ultimate tool for streamlining workflows, working alongside or in place of humans to handle complex tasks at scale. Their ability to provide deliverables, both simple and complex, frees up employees to focus on higher-level tasks they’re better suited to manage.
For example, AI in procurement or accounts payable helps finance teams process payments at scale without increasing headcount.
Beyond that, AI agents work continuously, giving you peace of mind that work is being completed in the off-hours, like evenings or holidays.
Improved decision-making
While humans can weigh different options and considerations in decision-making, it takes time and effort to find those considerations in the first place.
AI agents can consolidate information across multiple sources, presenting it in a simplified form for quick consideration. They may also identify patterns that would otherwise be missed or simulate scenarios to provide potential outcomes. This leads to better-informed, data-backed decision-making.
Given AI’s strength in pattern recognition, AI in payment processing helps identify fraud and prevent unwanted transactions from going through.
Continuity and consistency
In human-managed workflows, there’s always the chance of simple errors like typos, misremembering, or forgetfulness.
Comparatively, AI agents maintain large memory repositories, constantly learning and adapting based on past work and feedback cycles. The more it does a specific type of work, the more refined the output becomes, ultimately providing consistency and quality beyond what a human can provide.
This is seen in the use of AI in accounts payable where document matching and invoice processing is automated and consistent, reducing the chances of typos throwing off the accounting.
Reliable support and communication
Does your business operate online or across multiple time zones? Do you have employees or customers who may require support at off-hours? Then you could benefit from an AI agent to handle those requests.
Whether it’s a customer needing support with a purchase or a new hire needing clarification on a task or policy, AI agents can field those questions at all times, so no one is left alone when help is needed.
Challenges with using AI agents
The potential with AI agents is high, with long-term benefits and increased capabilities. But this doesn’t come without risk or challenge. Be mindful of these potential pitfalls when considering the use of AI agents.
Data privacy and security
An AI agent is capable of accessing data throughout the business. But some of the information it requires may be sensitive business and customer data.
Do you trust an AI agent with confidential information like design documents and trade secrets? Do you have concerns about a customer’s information being misused? These are potential outcomes you need to be prepared for.
Think about how much access you’re willing to give an AI agent before deciding on its use. Missing out on its efficiencies may be worth protecting otherwise sensitive information.
Lack of contextual understanding
AI agents have environmental understanding. But that understanding is informed by the information it has access to, missing out on important context and considerations. It may not understand the emotional, human impact of a decision, or the social context of its work.
Remember that AI agents are strong at mathematical or logic-based decision-making and that not all decisions abide by a black and white logic.
Complex setup and integration
It takes time to set up an AI agent, connecting it to your existing systems to provide it with the information it needs. Beyond that, your existing software or hardware may have incompatibilities, limiting the AI agent’s functionality.
Once the AI agent is set up, it still needs time to learn and iterate. Don’t expect an AI agent to be effective right off the bat; it’s a long-term investment that will take time to pay off.
Compliance and regulatory risks
AI is an emerging technology, and as it develops, so too do the rules and regulations that surround it. You must stay up-to-date on how these regulations develop such that you’re remaining compliant.
This is especially true in sensitive industries like healthcare, legal, and financial services. Any time there’s sensitive data or subjective assessment that could impact someone’s well-being, the use of AI agents is heavily scrutinized.
Bias and fairness
AI agents are capable of processing large swaths of historical information, learning from the patterns, and repeating those patterns in their future workflows.
What’s potentially problematic with that is when the historical data contains human bias; consider an AI agent used in hiring or lending that repeats discriminatory practices based on identity.
In the early days of using an AI agent, you have to monitor its outputs and provide feedback, testing for and correcting any bias that may arise.
Cost and resource requirement
Currently, there are a select few marketplaces for AI agents where you can browse existing builds that may work within your business. But if you have a specific build in mind, the AI agent should be built internally or purpose-made by a third party.
This comes at no small cost. The labor and resources required to build an AI agent are lofty, requiring skilled personnel to complete the work and test the results. You need confidence that the long-term payoff of an AI agent offsets the short-term cost.
Over-reliance and lack of oversight
It’s a common mistake to become dependent on AI without checking its work. This opens up potential risk when leveraging AI agents in critical decision-making.
Understand that AI agents are made to provide results, and occasionally, this causes AI agents to make mistakes or provide incorrect conclusions in the service of the end goal. Even if an AI agent has been operating smoothly for a long period of time, routine tests and maintenance are best to ensure accurate results.
Continuous learning and maintenance
As a thought experiment, think about putting an AI agent in a room with specific furniture, appliances, and layout. Each aspect of the room is a part of your business environment that your AI agent learns to operate around, mapping out routes, and processes based on the layout.
Now, change the layout, swap out a piece of furniture, or add a new appliance.
All of a sudden, the AI’s learned behavior is broken. The path from one side of the room to another may be obstructed, and furniture may get in the way of its usual path.
This is what happens when you make changes to your business environment. The AI agent requires maintenance, direction, and learning to adapt to any changes you make, taking time to regain effectiveness.
Future trends in AI agents
The underlying technology and capabilities of AI are constantly evolving. Predicting what AI may become is nearly impossible, as innovative minds are finding new ways to train and develop AI agents in directions previously unthinkable.
However, there are emerging trends that give us an indication of where AI agents could be headed.
AI models themselves are becoming more sophisticated and robust, processing increasingly complex requests with nuanced decision-making. In particular, multi-agent models incorporate the strengths of single agents to create enterprise solutions for organization-wide challenges.
There’s also the emergence of multimodal processing, empowering AI to take in information from all types of media, including text, audio, image, and video. This means seamless processing of information, no matter the medium, providing more context that informs the AI’s perception of its environment.
One lingering question is how these innovations may reshape the workplace. If AI is performing increasingly complex tasks and workflows, then what’s left for existing employees? How will their responsibilities change if their usual work is no longer needed?
And if AI is becoming more of a staple of the workplace, how will rules and regulations be shaped to ensure consistent, socially acceptable results? To what extent will we allow AI to access sensitive information or make decisions that impact the lives of people?
Ultimately, we’re at an inflection point where AI agents could become an essential part of business operations, but with a sensitivity to how they’re conducting their work and the conclusions they come to. It will pay to be up-to-date, taking advantage of this rapidly developing technology, but with an understanding of the risks and rules.
Leveraging existing AI technology in your financial processes
You don’t need to build complex AI agents to start benefiting from AI. Many out-of-the-box solutions exist that leverage AI to improve the efficiency and quality of work being done. Take, for example, your financial operations. What if you could save time on processing, auditing, and reviewing financial data, payments, and reporting?
Enter BILL, a financial operations platform. Through AI and automation, you can save time on accounts payable, expense reporting, budgeting, forecasting, and more. Cut down on errors, win back time, and let your team focus on the high-impact work that really matters.
Want to see how BILL can revamp your operations? Reach out for a demo or sign up to see it in action.
