What are AI agents?
AI agents are the next generation of intelligence. They move beyond analysis to independently act, adapt, and solve real-world problems – without the need for human intervention.
AI agents are the next generation of intelligence. They move beyond analysis to independently act, adapt, and solve real-world problems – without the need for human intervention.
To have agency means to possess the ability to act independently and make choices. That, in a nutshell, is what differentiates an AI agent from the enterprise AI that is embedded in today’s best software solutions. Standard AI can analyze data, recognize patterns, and generate insights – but it’s still just an advisor, waiting for human action.
AI agents, by contrast, build on those capabilities and take things further. Instead of merely offering suggestions, they are designed to take action themselves – executing decisions, coordinating tasks, and interacting with systems on your behalf. These agents are goal-driven, responsive, and increasingly embedded inside the tools and workflows businesses already rely on.
An AI agent is a type of artificial intelligence system that is capable of autonomously performing tasks and pursuing predefined goals. AI agents can problem-solve, make decisions, and execute actions without human intervention. They use large language models (LLMs) and natural language processing (NLP) techniques for a range of applications – from virtual assistants and complex analysis to robotics and self-driving cars. AI agents learn from their experiences and adapt their behaviors over time. They even work with other agents to coordinate and perform highly complex workflows.
The difference between AI agents and agentic AI is subtle but meaningful. It comes down to scope, autonomy, and orchestration. AI agents are individual software entities designed to perceive, plan, and act toward defined goals, and usually do so within a single task or workflow. Agentic AI refers to a broader paradigm, intended to coordinate multiple agents, tools, or processes to achieve complex outcomes. Think of AI agents as individual musicians, each capable of interpreting their part and performing it with precision. Agentic AI is like the conductor – guiding timing, synchronization, and flow across the ensemble to deliver a cohesive symphony.
AI agents operate in a continuous closed-loop cycle that lets them perceive their environment, plan intelligently, act decisively, and learn from the results. Each stage of this loop is supported by a specialized component that helps the agent behave autonomously and adapt over time.
After selecting a course of action, the agent goes into the execution phase. This involves carrying out decisions through task automation, workflow management, or direct interaction with physical or digital systems.
The feedback loop allows the agent to learn from experience. It monitors outcomes against performance goals and success criteria. If results fall short, the agent adjusts its future behavior, improving accuracy and effectiveness over time.
Depending on their degree of autonomy, adaptability, and function within a system, AI agents can take many different forms. While some follow rigid predetermined guidelines, others develop, learn, and even work together with other AI agents to accomplish challenging tasks. As their capabilities increase, so does their ability to operate independently within real-world workflows.
Rule-based AI agents act in accordance with pre-established rules and adhere to rigorous "if-then" reasoning. They ensure consistency and dependability in organized, repeatable workflows, but they are not adaptive or learning. These agents are helpful in situations where decisions must always follow the same logic without exception.
Example: An AI compliance agent that verifies that all supplier contracts follow company guidelines prior to approval. In contrast to a typical rules-based automation tool, this AI agent can independently function as a digital compliance officer by cross-checking several regulations, pointing out discrepancies, and requesting clarifications.
Reactive AI agents respond to their surroundings in real time, but they lack memory, which means they don’t learn from past experiences. Rather, like a thermostat that modifies temperature according to ambient conditions, they make live decisions based on available inputs.
Example: A cybersecurity AI agent that detects anomalies in real time. Without waiting for human intervention, it automatically disables compromised user accounts, isolates impacted systems, or modifies firewall rules – in addition to flagging suspicious activity for human review.
In contrast to reactive agents, learning AI agents change over time. They use machine learning models to refine their decision-making after analyzing feedback from their actions. These agents perform best in dynamic settings where circumstances are ever-changing and call for ongoing development as opposed to preset reactions.
Example: An AI contract negotiation agent that gains knowledge from past business transactions. It adapts negotiating tactics over time, identifying supplier trends and proposing terms that maximize acceptance while safeguarding corporate interests, as opposed to strictly adhering to a predetermined contract template.
Conversational AI agents are more than just chatbots; they comprehend context, foresee needs, and act in response to inquiries. These agents are capable of problem-solving, negotiating, and even making recommendations, in contrast to conventional AI-powered assistants that use a prewritten script.
Example: A customer dispute-resolution agent in a banking app. When a customer disputes a transaction, the AI retrieves account history, analyzes trends, creates a dispute claim, and submits it for processing – all the while conversing with the customer – instead of referring them to a static FAQ or holding for a human representative.
Autonomous AI agents are able to make high-level decisions with minimal human oversight. They don't merely make recommendations; they act, assess the outcomes, and refine their plans. These agents frequently work in high-stakes situations where quick, autonomous decision-making is essential.
Example: An AI-powered procurement agent that manages the complete procurement cycle on its own, by using data to find the best suppliers, negotiating prices, securing contracts, and placing purchase orders. This AI agent acts as a stand-alone digital buyer. It still benefits from human oversight but can drastically improve efficiency.
AI agents leverage all the features and capabilities of generative and industrial AI to take action and make things happen in the real world. This means mitigating risks before they become unmanageable, seizing opportunities while they're fresh, and helping customers to get what they need quickly and seamlessly.
AI agents can be trained to tackle and master challenges and tasks unique to every sector. A few agentic AI examples include:
Precision and compliance are non-negotiable in this sector. AI agents can go beyond standard data analytics to autonomously orchestrate workflows, track and ensure compliance, and tweak supply chain operations in real time. AI agents can quickly update to new regulatory standards, detect and flag out-of-specification parts, and quickly re-order the right one – all while notifying internal systems of potential delays.
This industry demands that supply chains, engineering, and production all work together seamlessly. Instead of relying on scheduled reporting, AI agents can make data-driven decisions on their own. They can autonomously resolve production bottlenecks, modify supply orders based on real-time shortages, and even prioritize task execution across teams – without waiting for escalation.
In F&B, quality control and quick response and reaction times are essential. AI agents actively monitor ingredient quality and mix ratios, identify deviations, and take corrective action in addition to collecting data. To ensure product consistency and safety, an AI agent can, for example, immediately stop production, change to a different supplier, and adjust recipe parameters if an ingredient's quality deviates from tolerance levels.
AI agents help brands to react quickly to changes in customer behavior and demand. In contrast to AI-powered forecasting tools, AI agents act on their own initiative, modifying orders, reallocating production resources, or even initiating localized marketing campaigns in response to spikes in regional demand. Instead of depending solely on the analysis of past data, brands can respond to consumer trends in real time.
AI agents can provide autonomous, real-time manufacturing process optimization. Instead of predicting failures but taking no action, like standard AI-driven maintenance tools, AI agents can shut down compromised machinery, reroute workflows to alternative production lines, and notify external suppliers if parts are needed. This helps maintain productivity without waiting for manual troubleshooting.
These projects have unpredictable timelines, shifting regulations, and complex logistics, making real-time coordination and adaptability essential. AI agents don’t just analyze schedules – they actively monitor site conditions, adjust timelines, and reroute resources as needed. If unexpected delays occur, an AI agent can autonomously reschedule tasks, find alternative suppliers, and notify stakeholders – keeping projects on time and on track.
As AI agents become more autonomous, they bring a host of potential risks that organizations must carefully consider:
As your company’s AI usage becomes more advanced and complex, you’ll want to ensure that you have a strong IT team that is up to date with the latest technological and security measures. In your business culture, you must learn to treat AI agents like human users, with controlled privileges and access to resources, and specific monitoring and warning systems, before anything escalates.
Before fully deploying AI agents, start with professionally-crafted, narrow use cases and pilot them in controlled environments. Define expected outcomes clearly and ensure success metrics align with business objectives.
Put a clear governance framework in place. Define roles and responsibilities for AI usage. Make sure that AI agents operate within set boundaries and that there's a process in place to monitor and review their actions.
For critical processes, ensure that human-in-the-loop protocols are in place. AI agents should augment human judgment, not replace it. Final decisions, particularly those with significant impact, should have human validation.
AI agent performance should be regularly monitored to detect and manage any potential biases or drifts. The use of diverse and representative training data helps to make sure that AI agents make fair and accurate decisions.
Stay informed about evolving AI regulations and ensure that AI agents comply with changing laws and standards. Implement robust security measures to protect AI systems from potential threats and vulnerabilities.
In just a few years, AI has evolved from a passive digital assistant to an active participant in daily operations. With such new technology, we’ve yet to fully realize the benefits and enhancements this could bring to the business world. But you guessed it: This also means we’re probably not fully aware of the potential risks that could come from poor security or rushed implementations. That’s why the path forward with AI agents depends on thoughtful design, responsible deployment, and strong governance. With the right foundation in place, there’s no telling what new horizons AI agents can help you reach.
See how trusted AI agents in Infor GenAI Assistant can support your projects, products, contracts, workforce, and more.