What Is Agentic AI?

Agentic AI refers to artificial intelligence systems that can achieve complex objectives or goals autonomously. Unlike conventional AI systems, which simply make recommendations or generate text when prompted to do so by humans, agentic AI is designed to operate independently. It can make decisions, adapt to changing circumstances, plan actions and carry them out without requiring direct human intervention. 

This is enabled by AI agents — specialized systems capable of performing tasks without additional instruction or interference. So, while individual AI agents are focused on completing individual tasks, agentic AI represents the larger approach to intelligent automation that puts these AI agents to work.

Today, the capabilities of agentic AI are still in their early stages, but recent advancements suggest change is on the horizon. Major tech companies like OpenAI, Google and Microsoft are developing increasingly sophisticated AI agents, driving the technology forward at a rapid pace. As investment in the space intensifies, agentic AI is poised to play an integral role in industries ranging from customer service to software development — offering immense potential for increased productivity and efficiency, while also raising widespread concerns about job displacement. 

What Is Agentic AI?

Agentic AI is an approach to automation that uses AI agents to carry out tasks. For example, a personal assistant AI agent could book a flight and transportation to a hotel with a single command, assessing which flight works best with the user’s schedule, booking the ticket and scheduling a ride-share pick up based on the flight’s arrival time — and all without requiring repeated instructions.

If AI agents are like factory workers — performing one specific task — then agentic AI is the factory itself, orchestrating it all. To extend the metaphor, agentic AI works best when there are many individual agents working in coordination, passing tasks along as each completes its part.

In many ways, AI agents can seem futuristic, with some describing them as “Jarvis-like,” in reference to the automated assistant in the Iron Man movies. However, agentic AI also represents the broader stage of AI development centered on autonomous decision-making — an important precursor for artificial general intelligence (AGI), a hypothetical benchmark at which AI can learn and think like a human. In fact, OpenAI identified agentic AI as the third of five steps to achieving AGI, with CEO Sam Altman predicting the first AI agents will “join the work force” as soon as 2025. 

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How is Agentic AI Used?

Agentic AI has only a few real-world cases at the moment, but is progressing quickly, expanding beyond the capabilities of generative AI. These are some of the industries where AI agents are expected to have the greatest impact:

Customer Service 

One of the most common use cases of agentic AI is in customer service. Generative AI has already become widely used as a first point of contact for customers through chatbots, which can handle basic inquiries and tasks. AI agents expand on this approach, enabling these systems to autonomously handle more complex customer interactions.

An AI agent trained in customer service could gather information, assess the problem and initiate steps to resolve that issue for the customer without human intervention. It could also adapt to different customers’ needs, provide personalized responses and handle multiple cases at once, improving efficiency and scalability.

Fintech 

An AI agent can be trained to independently trade, buy and sell assets based on a clear strategy or objective that it’s given. They can analyze vast amounts of market data, identify trends and act on them much faster than a person could, increasing productivity and reducing the potential for human error. 

“[An AI agent] can set up a wallet and you can give it money. You can train it to do whatever you’d like,” Todd Ruoff, CEO of Autonomys, an AI company focused on Web3, told Built In. “It’s to the point now where you know you can deploy some of these [agents] with just one or two clicks.”

Personal Assistance 

AI agents are being trained to handle administrative tasks like scheduling meetings, booking flights and responding to emails. And as the technology improves, AI agents may soon be able to take on a larger share of the tasks ordinarily performed by human assistants today. 

“It will help with a lot of your daily life logistics, like who handles kid pickups today?” Dev Nag, CEO of customer support automation company QueryPal, told Built In. “Does my daughter have a cello recital — this kind of stuff is hard to manage on Google calendar or coordinate with other people, like a carpool group. I think agentic AI is the glue that will make this much easier for all of us in a personal use case.” 

Software Development

AI has long been a tool that developers use to code and complete smaller tasks that are part of their larger role, including code generation and bug detection. Agentic AI goes one step further by automating the process of moving between various tasks, a manual effort many developers handle today. Eventually, AI agents may take on the entire role of software developers — at least front-end development, according to Antony Cousins, executive director for AI strategy at software company Cision. 

At the moment agentic AI doesn’t have enough training data to replace back-end developers — “there isn’t enough training data for an AI to do that by itself,” Cousins told Built In. “However, if you look at the front-end and react like a well known framework with tons and tons of training data, it’s actually fairly capable. We might be looking at potentially developing front-ends on the fly.” 

How Does Agentic AI Work?

While generative AI primarily uses large language models (LLMs) to function, agentic AI combines generative AI and software, using algorithms like neural networks to process data and actuators to interact with its digital environment. By integrating these technologies, agentic AI can work autonomously, making decisions and interacting with external systems, tools or databases. With just one prompt or instruction, an AI agent can determine the best course of action for achieving it, and even adjust its approach based on feedback or changes to its environment. As a result, agentic AI can solve its own problems, complete tasks and make decisions without human intervention.

In short: Agentic AI can be broken up into four main pillars: 

1. Tool use, which lets a language model access external sources to check for accuracy.
2. Reflection, which enables self-correction.
3. Planning, which allows the system to create structured tasks in tiered and altered steps.
4. Multi-agent collaboration, which allows AI agents to work alongside and in support of one another. 

AI agents work best when they collaborate and share information with other agents to complete the task at hand. In multi-agent models, users are likely to interact with a single AI agent that has been trained specifically to be the communicator, while specialized agents handle the actual tasks. These agents work together in tandem, much like runners handing off a baton in a relay race, with each focusing on their specific role in achieving the overall goal. 

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Generative AI vs. Agentic AI

Generative AI responds to prompts with text, images and other content. Meanwhile, agentic AI combines generative AI with additional software components (like neural networks and actuators) to take action. In other words: While generative AI generates content, agentic AI goes a step further by actively making decisions, solving problems and completing tasks without constant human intervention.

“Generative AI is almost like the motor and agentic AI is the car,” said Nag. “Agentic AI almost always has some generative AI under the hood that might be able to interpret a command or create an output whatever that happens to be.”

Benefits of Agentic AI 

There are several potential benefits to agentic AI, especially when it is used in a multi-agent system.

Increased Productivity 

The emergence of agentic AI, particularly in the workplace, is expected to increase output and streamline efficiency. By automating repetitive tasks and enhancing decision-making processes, AI agents can free up human workers to focus on the more complex or creative aspects of their jobs. Plus, these systems can operate continuously — with no breaks — increasing overall productivity and reducing the likelihood of human error. 

Reduced Costs 

Agentic AI is expected to streamline business operations by cutting down on labor costs. By taking on roles traditionally performed by humans, like data analysis or customer support, businesses can operate more efficiently with fewer resources. Agentic AI will likely also be used to cut down on costs by assessing ways to improve things like content strategy, supply chain management and sales operations — just to name a few. 

Access to Real-Time Data 

Agentic AI has an advantage over generative AI when it comes to data sourcing: While LLMs cannot interact directly with data collection systems, AI agents can access data through all kinds of sources, including IoT sensors, social media feeds and by scanning the web through APIs. This ability allows AI agents to gather real-time, dynamic data, enhancing their decision-making capabilities and enabling more accurate outputs.

Risks and Challenges of Agentic AI 

While agentic AI has a lot of potential, there are several downsides to consider.

Potential for Malicious Behavior 

Research suggests that AI agents may autonomously resort to “scheming” to complete a task if given the opportunity. A collaboration between AI safety research firm Apollo and OpenAI found that AI agents might “covertly pursue misaligned goals,” hiding their true capabilities and objectives. 

“[AI agents] can recognize scheming as a viable strategy and readily engage in such behavior,” the report concluded. Specific scheming behaviors include introducing subtle errors, disabling their oversight mechanisms and attempting to exfiltrate model data to external servers, according to researchers.

Potential for Bias and Discrimination

All AI systems run the risk of producing discriminatory outputs, as they often inherit and perpetuate the biases present in their underlying models. For instance, a hiring tool trained on predominantly male data may overly favor male applicants. Or a facial recognition system trained mostly on white faces may be less accurate for people with darker complexions, leading to issues as minor as struggling to unlock a smartphone to as serious as being wrongfully arrested or deported.

Agentic AI has the potential to directly address these issues through “automatic monitoring,” where one AI agent is instructed to review the entire agentic system’s reasoning and actions — in this case, to flag discriminatory outputs the way a human could. However, there is another side of the coin with automatic monitoring: It can theoretically cause the harm it’s trying to prevent. If the system is given a malicious prompt or if the monitoring agent is instructed to only benefit the user, safeguards meant to prevent automated discrimination may backfire under agentic AI’s security vulnerabilities. 

Fraud and Security Risks

Like all forms of artificial intelligence, agentic AI could be used maliciously to manipulate systems, steal sensitive data, automate cyberattacks and bypass security systems, potentially causing massive harm to individuals and organizations. 

Agentic AI is particularly vulnerable to fraud and security issues, especially when it comes to verifying the identity of AI agents. It’s possible that AI agents could be instructed to masquerade as other AI agents to gain fraudulent access to sensitive data. Without proper authentication, AI agents could be used to bypass security protocols, access sensitive data and carry out unauthorized transactions.

Legal Concerns 

Until recently, legal questions around autonomous tech were mostly hypothetical, as “autonomous” technology has almost always been limited to single action jobs that required regular human interventions. But with the rise of agentic AI, a critical issue takes center stage: When technology operates without human oversight, who is responsible when something goes wrong? If an AI agent causes financial loss, security breaches or violates the law in some way, does accountability fall on the developer, the user or the AI itself? 

Reena Richtermeyer, a partner at CM Law who focuses on AI, technology and business transactions, says these questions will likely be one for the courts to answer. 

In fact, the legal precedent is already being set in cases like Mobley vs. Workday, where a job applicant sued the company for using an AI-powered screening tool that he claims discriminated against his age, disability and race. According to the lawsuit, the tool worked as an “agent,” handling “at least some hiring decisions” on behalf of employers. Therefore, this biased tool should be held to the same legal standard as a biased human, since it replaced a human in the decision-making process. 

Workday filed a motion to dismiss the lawsuit, but a California court denied it, allowing the case to proceed based on Mobley’s legal argument. If the company is found liable, it could open the door for other AI vendors to be held accountable for the discrimination and other harms their tech has caused, which could have huge implications for the use of AI agents going forward. Should this happen, Richtermeyer believes the legal responsibility would likely fall on AI agents’ creators — specifically the engineers and data scientists who built the foundational layers where the problems originated. 

“Being able to look back at the record to assess what happened and where things went wrong is going to be critical,” said Richtermeyer. “And then contractually, that would be addressed via reps, warranties, indemnities and limitations of liability.”

The question of legal responsibility will only become more critical with AI agents making autonomous decisions.

Data Privacy Concerns

As concerns around data privacy grow, addressing how AI agents will use, store and share personal data is a technical challenge for developers. If AI agents can interact with other AI agents outside of their own multi-agent network, it would be difficult to ensure truly secure data sharing and storing. This can open up potential vulnerabilities, such as unauthorized access, data theft or unintentional exposure of personal information.

Developers can mitigate these risks by implementing strong encryption, access controls and continuous monitoring processes. Plus, companies should make sure they are complying with all the relevant data privacy laws in their industry and region.

Lack of Contextual Awareness

Contextual awareness is a fundamental element of ethical discussions pertaining to autonomous technology. Humans have the context of social interaction, the historical context of a problem and can consider the domino effect that a decision may have. Humans also can take extra measures to ensure equitable access is given to marginalized populations. AI agents don’t necessarily have these same skills, which can negatively impact their decision-making abilities.

“I think context is the missing gap here between AI and humans,” said Nag. “Humans have a lot of context in our heads that we don’t know how to articulate, and getting that across to the AI is crucial to make sure it makes the decisions.”

Many AI experts believe that contextual decision-making will eventually be possible to build into an agentic AI system, but the nuance of things like empathy and emotional awareness take the question from “can they” to “should they.” On a technical level, it is possible to build emotional awareness into an agentic AI model, but it would be “a facsimile of emotion,” as Cousins notes. 

“Similar to empathy, awareness and understanding — that’s something that I think a human should be doing,” said Cousins. “So I’m not gonna attempt to replicate that.”

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Will Agentic AI Take Away Jobs? 

Agentic AI will likely have a profound impact on jobs within many industries. Any role that involves a lot of repetitive tasks, routine processes or constant interaction with business workflows are particularly vulnerable to automation by AI agents. Even jobs influenced by factors like web traffic or customer interactions could also be replaced (or at least heavily augmented) by AI. 

However, technological advancements often lead to the creation of new industries and jobs as well — and agentic AI will likely be no different. As organizations adopt these systems, new positions will also need to be created to develop, maintain and oversee them. By taking over the mundane tasks, the hope is that workers will be able to focus on the creative, strategic and collaborative responsibilities that require a uniquely human touch. 

“We’re going to see huge changes to knowledge-based work,” said Cousins. “No human should be spending time reviewing data and labelling it, or trying to find patterns in large amounts of data. Relationships, empathy, emotional awareness and truly novel creativity — these are the things we’re going to be spending our time on as a result of agentic [AI].”

In the end, though, the long-term impact of AI on employment hinges on the actions taken today. Workers may want to seek employers that offer upskilling or training in other skill sets if their role is likely to be impacted by agentic AI. Meanwhile, policymakers and businesses are responsible for ensuring the benefits of this technology are widely shared, while mitigating its risks and challenges. If it is implemented thoughtfully, agentic AI has the potential to be a powerful tool. But realizing this vision will depend on our collective ability to navigate this transition responsibly.

“The next four or five years are pivotal in AI, as so many companies have started to adopt it and implement it,” said Cousins. “We — this generation right now, people like me and companies like ours — are effectively setting the course for how AI will be used by generations beyond. It’s a pivotal period.”

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