Artificial intelligence (AI) has taken on many shapes and forms since its inception. What started with basic pattern repetition is becoming a cornerstone in the technological landscape, powering software and hardware, enabling it to achieve more on our behalf.
The incorporation of AI in our technology has lead to rapid adoption. In a recent survey of businesses by McKinsey, 78% of respondents reported using AI in at least one business function, up from 72% in early 2024 and 55% in 2023.
For businesses wanting to stay ahead of the curve, being on the cutting edge of AI technology is a necessity. And if you want to be at the forefront of AI adoption, you need to know about agentic AI.
What is agentic AI?
Agentic AI is a term used to describe autonomous artificial intelligence systems that are capable of complex processes that result in decision-making or actions without human intervention.
When given a task, agentic AI can plan a multi-step workflow that learns and adapts to new information as it performs each step. This means that even if a workflow doesn’t go as planned, agentic AI uses problem-solving to find a solution.
These complex systems are typically built on large language models (or LLMs) to process information and provide context. It doesn’t matter if the environment is inconsistent; agentic AI processes the words and numbers within a system to understand what’s going on. It’s consistently learning, adapting, and iterating based on what it observes.
The end result is an AI that’s capable of dynamic, independent work without human input.
How does agentic AI work
Agentic AI systems perform complex tasks autonomously. They do so through the following traits and abilities.
1. Goal-oriented behavior
The foundation of agentic AI is an end goal that its constantly working towards. Their ability to plan and execute workflows allows users to provide high-level objectives as opposed to step-by-step guidance.
As an example, you could feed an agentic AI the high-level objective of maintaining accurate and up-to-date financial reporting. In response, it would learn to process invoices, update financial reports, and adjust both forecasts and budgets.
2. Use of large language models
Agentic AI maintains an understanding of context by processing new information through LLMs.
Think of LLMs as the “brain” of agentic AI: it takes in surrounding information, processes it, and turns it into appropriate responses. Even if situations change over the course of the agentic AI’s existence, it will learn, adapt, and incorporate new information through an LLM.
3. Integration of tools and databases
The multi-step process taken by an agentic AI can be executed across multiple platforms and using multiple databases. Unlike traditional AI, it’s capable of being integrated with external APIs, databases, and software to collect information and execute actions.
Leveraging these connections, agentic AI is capable of end-to-end work, never depending on human intervention to handle a single step.
4. Planning and execution
Before actioning on anything, agentic AI goes through a planning process, mapping out a step-by-step process to complete its end objective. This includes which tools and information it needs to use at each step, understanding the dependencies along the way.
If there’s an error or blocker, the agentic AI is capable of resetting and creating a new plan based on this information. Changing circumstances don’t break the agentic AI’s execution, it simply adapts.
5. Learning and iteration
Agentic AI is constantly improving over time. Based on feedback or new information, it changes approaches in pursuit of its end objective.
All of this information is kept in a memory bank that the agentic AI refers to. If a process is successful, it repeats it, ensuring consistency in its workflow. But if the process is unsuccessful, it attempts again, only internalizing the workflow if it leads to a successful outcome.
Key characteristics of agentic AI
There are several characteristics that separate agentic AI from other applications of AI technology.
Autonomy
The most integral feature of agentic AI is autonomy: the ability to operate independently and without human approval or oversight. It’s not put to work on individual tasks, rather it makes decisions and takes action on its own.
The autonomy of agentic AI allows it to tackle routine tasks, only escalating an issue to a human if a decision requires uniquely human judgment.
Proactivity
Building off of autonomy is the proactivity of agentic AI. By constantly running independently, agentic AI identifies opportunities or problems and takes action based on its end objective.
Think of an agentic AI used in financial recordkeeping. It could identify an anomalous transaction, check it against expense reports, and if it can’t validate the transaction, escalate it to the financial team.
Complex reasoning
Much of what we consider to be AI is based on conditional reasoning, an interwoven network of “if-then” logic. This means that if the AI observes a certain condition, it acts accordingly based on that “if-then” relationship.
By contrast, agentic AI incorporates complex reasoning, systems that consider multiple factors, and makes decisions based on its understanding of the context.
The result is something that doesn’t operate on black and white logic, but rather acts on the totality of information available.
Goal-driven
Agentic AI is always working towards a predetermined goal. Its evaluation and decision-making process is based on the potential impacts on the end-goal.
This aspect is a valuable trait of agentic AI, but it’s also one that requires some oversight and adjustment in the early days. Consider an agentic AI with an end-goal of profit maximization. It’s possible that it would push cash-cutting measures without understanding the nuance of what each cost is in service of.
Examples of agentic AI in practice
To illustrate how agentic AI is already in use, these are a few examples of agentic AI applications across industries.
Software development
The world of software development has shifted with agentic AI that can create, test, and troubleshoot code.
The AI agents can process requests and requirements as end goals and build something in service of them. This includes testing its output and iterating based on the results.
This advancement is invaluable to human engineers who can pass off the menial, routine coding tasks and focus on bigger picture responsibilities.
Customer support
You’ve likely already encountered chatbots in a customer service environment. But chatbots operate off of rudimentary conditional logic, sending customers to specific pages or solutions based on keywords mentioned in the conversation.
Agentic AI elevates the use of AI in customer service through the interpretation of the message, context, and accessible information to provide solutions. While a chatbot could pick up that someone wants to make a return and send them to the return policy, an AI agent could access the order information, apply the return policy, and give actionable next steps.
Cybersecurity
Your technology and infrastructure need to be protected at all hours as an attack could come at any time. And if a human is watching, there’s still the lag time between identifying the threat and acting on it.
With agentic AI, you have a 24/7 watcher, constantly monitoring for threats and suspicious activity. If something comes up, it acts in real time to adapt the defences and mitigate damage.
If trends emerge, the agentic AI identifies them and formulates solutions to prevent that type of attack or activity.
Business intelligence
To understand your business, you need to compile data from multiple sources, analyze the information, identify trends, and turn it into insights and recommendations. This is complex work, not to mention all the menial tasks required to simply synthesize data and check multiple sources.
But if you use agentic AI, all of this is automated, transforming raw data into valuable insights and actionable recommendations without the need of data scientists or analysts.
Agentic AI vs traditional AI
Traditional AI systems are typically niched and specialized at specific tasks. They’re built to excel at one specific thing, often operating off of prompts or triggers.
Think about an AI that’s built to process invoices. When an invoice is received into its system, it may scan information using optical character recognition (OCR) and input that data. It’s purpose-built to complete that and related tasks.
Agentic AI is built to be independent and complete complex workflows. It uses a variety of tools and interconnected databases to complete its work.
Building off of our invoice AI example, agentic AI could process the invoice, update the accounting software, notify the necessary parties by email, and update financial forecasts. It’s constantly operating in the background, not depending on prompts or cues to get started.
Advantages of agentic AI
Businesses are finding new ways to implement and utilize agentic AI because of these benefits.
Increased efficiency
While traditional AI is capable of handling one-off tasks, agentic AI can handle end-to-end workflows without human intervention. The ability to operate independently means work is constantly being completed without the risk of a bottleneck from human oversight.
For ongoing tasks like accounts payable and accounts receivable, this means a smooth, continuous processing of invoices and payments as well as increased fraud protection.
Enhanced decision-making
Sometimes the greatest difficulty in making a decision isn’t the decision itself, but the collection and analysis of all information that guides the decision.
With the ability to process large swathes of data across disparate sources, agentic AI streamlines decision-making by having the information needed readily accessible at all times.
This leads to agile organizations, making data-backed decisions on the fly, operating off of insights that may have easily been missed. This is prudent for activities like procurement, where changing market conditions and supplier performance need to be considered to keep costs down and operations running smoothly.
Automation of repetitive tasks
Menial tasks like data entry, informational audits, and report generation bog down teams, preventing them from dedicating sufficient time to high-value, impactful work. Agentic AI doesn’t just make these processes faster, it fully automates them from beginning to end.
This allows employees to focus on strategic work, relationship management, and complex problem-solving, things that truly move the needle for businesses trying to maximize their efficiency. Think of an accounting team that spends less time processing reports on the dollars and cents, and more time on identifying where to allocate that money.
Challenges and considerations of agentic AI
Agentic AI is capable of reshaping how organizations and individual teams operate. But incorporating agentic AI is not without some risk.
Data privacy and security
Agentic AI is given access to information across databases, some of which may be sensitive. Whether it’s customer data, security information, or organizational secrets, agentic AI can access and store this information, putting it to use in its processes.
This opens up a new lane of risk for businesses to consider. If this information is to be accessed, to what extent can it be used within the agentic AI’s work?
The business should have its own internal policy that dictates how agentic AI accesses and uses information. But it also needs to consider compliance with regulations such as the General Data Protection Regulation (GDPR) in the EU.
If an organizations agentic AI misuses or shares sensitive data, it could result in serious fines or penalties.
Integrating with existing systems
Setting up an agentic AI is complex and time-consuming. You need to connect it to your existing systems, databases, and tools for it to function to its full potential.
In doing so, you may realize that your existing systems don’t have modern APIs or integration abilities, preventing the AI from accessing the data or tool.
It may also bring to attention some flaws in your existing tech stack or setup, some that may need to be addressed before implementing the agentic AI. Businesses should scope the project of agentic AI implementation before beginning the process, developing integration strategies and documenting potential costs to creating an ecosystem for the AI to operate smoothly.
Ethical implications of agentic AI
Agentic AI operates independently, processing information and making complex decisions. But this raises an important question: if the AI makes a problematic decision, who is responsible?
The impacts of agentic AI decision-making can spill over to customers, employees, and stakeholders throughout the organization. The business needs a policy on how the agentic AI can operate, minimize the risk of problematic decisions, and an accountability structure to ensure it doesn’t go unchecked.
There’s an additional consideration of how AI is trained on historical data, which may contain unethical biases. This is something that must be tested for and addressed if it continues to perpetuate biases in its decision-making.
Getting started with agentic AI
Ready to take advantage of agentic AI? Approach the process strategically with these three considerations.
Scope out organizational needs
Start by identifying the scope of work the agentic AI will be completing on an ongoing basis. Look for workflows that are repetitive and straining your teams as currently structured. In particular, any processes that benefit from 24/7 operation or are currently bottlenecked by human inputs are prime for automation.
You then need to define what the minimum viable level of agentic AI is needed for the workflows. This includes the information that it needs to access, the potential roadblocks in its workflows, or potential difficulties that could hinder the process. Despite agentic AI’s ability to adapt and work towards an end goal, you still need to ensure the path is as painless as possible for the best results.
Once you have identified all of these points, you’ll have an idea of what the agentic AI solution may look like.
Choose the right solution
There currently isn’t an out-of-the-box option to implement agentic AI. In most cases, agentic AI is developed within an organization or adapted from existing technology for a more robust solution.
The solution you need might not be a full-scale, agentic AI. Some platforms already incorporate autonomous AI in its existing technology, saving you the headache of having to figure it out for yourself.
For example, you can still automate financial processes with a platform like BILL and its AI automation.
If a full-scale agentic AI is the desired solution, it will need to be developed internally or with a trusted vendor that offers purpose-built agentic AI designed specifically for an organization’s needs.
Follow known best practices
It’s high risk to roll out an agentic AI without a thorough testing and vetting of its output. Instead of launching a full-scale agentic AI, start with a pilot project in a contained environment where it can be tested and monitored with minimal impact.
You should also have clearly defined success criteria or standards that the agentic AI is being held to. The initial feedback period is invaluable to guide the agentic AI to the right processes and outcomes.
Interfacing with the agentic AI may not be intuitive, and employees will need training on how to work with it. After all, they’ll be the ones providing oversight and guiding the learning process.
Finally, remember that agentic AI is constantly learning, adapting, and evolving. Where it may be months down the road could be a far cry from what it was at launch. You should be reviewing, testing, and vetting the results of an agentic AI on a regular basis to ensure it hasn’t veered from the desired path.
The future of agentic AI in the workplace
Agentic AI as it exists today already presents a wealth of opportunity, fundamentally reshaping work and the ways businesses operate. The ability to operate independently and automate workflows at all times is invaluable in a globalized economy where businesses operate on a near 24-hour basis.
These benefits are substantial, but they take effort to realize. Implementing agentic AI effectively takes time and effort, potentially exposing flaws or inadequacies in your existing tech stack. There are also regulatory considerations, as data and informational safety are paramount.
This puts businesses in a position of opportunity, but one that must be approached with caution and care. Scope out the work, identify risk, and start with tests in small, controlled environments.
The more care you put in implementing agentic AI, the better the results. And by dedicating time to the feedback loop, assisting it through its learning phase, you’ll have a system that’s scalable and effective.
AI and automating financial workflows
You don’t need to build an agentic AI to start taking advantage of the powers of AI and automation. There are already platforms with built-in AI that empowers organizations to automate workflows, saving time and fueling growth.
Enter BILL, a full suite of financial management tools with mindfully integrated AI to give you powerful, intelligent automation. For accounts payable, accounts receivable, expense management, and beyond, you have AI streamlining your workflows and mitigating risk.
Want to see how BILL’s AI and automation can help your organization? Reach out to schedule a demo and see our platform in action.
