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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.

What are AI agents?

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 analyse data, recognise patterns, and generate insights – but it’s still just an advisor, waiting for human action. Agentic AI, on the other hand, builds upon these capabilities and applies them to self-direct actions in the real world. Where a traditional AI will work to give you the information you request so that you can make and execute decisions, AI agents can make and execute those decisions themselves.

AI agents explained

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 behaviours over time. Thean even work with other agents to coordinate and perform highly complex workflows.

Agentic AI vs. generative AI

Agentic AI and generative AI both offer huge productivity benefits, but they are not the same. Generative AI can create original content – such as text, images, or video – in response to a user’s prompt. Agentic AI, on the other hand, can autonomously make decisions, act, and pursue complex goals with limited human intervention. Generative AI is reactive to user input, whereas agentic AI is a proactive approach.

How do AI agents work?

An AI agent needs to be able to "see" its working environment in order to analyse it, create plans of action, and carry them out. As a sensory interface, the perception module gathers information from a range of sources, including sensors, linked apps, and direct user interactions.

1. Perception model

An AI agent must possess the ability to "observe" its operational surroundings in order to conduct analysis, formulate courses of action, and execute them accordingly. Functioning as a sensory interface, the perception module adeptly collects information from a diverse array of sources, comprising sensors, interconnected applications, and direct engagements with users.

2. Reasoning engine 

After gathering data, the reasoning engine analyses trends, evaluates risks, and decides on the best course of action. Before acting, AI agents use algorithms to simulate outcomes and weigh several options.

3. Action execution

The execution phase follows immediately after determining the appropriate action. This involves putting decisions into action via workflow management, task automation, or direct physical device interaction.

4. Feedback loop

The feedback loop component keeps track of the outcomes and compares them to performance metrics and desired goals. If disparities are found, the agent can modify its future behaviour to facilitate better results down the road.

Types of AI agents

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.

Rule-based AI agents (task executors)

Rule-based AI agents act in accordance with pre-established rules and adhere to rigorous "if-then" reasoning. They ensure consistency and dependability in organised, repeatable workflows, but they are not adaptive or learning. These agents are helpful in situations where decisions must always follow the same logic without exceptions.

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 (real-time decision makers)

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.

Learning AI agents (adaptive problem solvers)

In contrast to reactive agents, learning AI agents change over time. They use machine learning models to refine their decision-making after analysing 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 maximise acceptance while safeguarding corporate interests, as opposed to strictly adhering to a predetermined contract template.

Conversational AI agents (interactive assistants)

Conversational AI agents are more than just chatbots; they comprehend context, foresee needs, and act in response to enquiries. 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, analyses 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 (independent operators)

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.

Multi-agent systems (cooperative AI networks)

Multi-agent systems (MAS) are comprised of multiple AI agents working together, each specialising in a different aspect of a larger task. They self-organise, cooperate, and communicate to resolve complicated issues that are beyond the scope of any single agent.

Example: A self-healing IT infrastructure system in which various AI agents independently monitor servers, identify malfunctions, redirect traffic, and fix problems. These agents collaborate to preserve uptime, stop data loss, and keep systems running – alleviating the load on human IT teams.

Benefits of AI agents

Using all of the features and capabilities of generative and industrial AI, agentic AI takes action and makes things happen in the real world. This means taking care of risks before they become unmanageable, seizing opportunities while they're fresh, and helping customers get what they need quickly and seamlessly.

  • Operational efficiency and cost savings
    Automate complex workflows, cut down on manual effort, and reduce inefficiencies. By optimising real-time resource allocation and decision-making, you can lower costs, streamline operations, and reduce waste.
  • Enhanced decision-making with data-driven insights
    Analyse complex and disparate data sets as one, getting the insight and reality cheques you need to be more strategic and decisive. Features like predictive modelling can help to anticipate trends, while automated recommendations ensure that you respond proactively rather than reactively.
  • Scalability and adaptive growth
    Expand and pivot when you automate tasks that would otherwise call for additional staff and training. This lets you handle increased workloads, enter new markets, or manage growing data volumes without proportional increases in HR costs.
  • Proactive problem-solving and risk mitigation
    Anticipate challenges and take corrective actions before issues get out of hand – all the while learning from past events and getting more accurate over time. This includes things like predictive maintenance in manufacturing, fraud prevention, and cybersecurity risk mitigation in IT.
  • Personalisation and improved customer experience
    Improve your customer interactions with tailored, real-time responses. From intelligent chatbots to automated order tracking, you’ll be able to offer seamless and personalised experiences that help your customers feel cared for – and more loyal.

AI agent examples in different industries

Agentic AI can be trained to tackle and master challenges and tasks unique to every sector. A few examples include: 

Aerospace and defense

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 reporting the delay to MES or other agents.

Automotive

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 decision on their own. They can autonomously resolve production bottlenecks, modify supply orders based on real-time shortages, and even deploy other AI agents to assist different teams. 

Food and beverage

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.

Fashion

AI agents help brands to react quickly to changes in customer behaviour and demand. In contrast to AI-powered forecasting tools, AI agents act on their own initiative, modifying orders, reallocating production resources, or even initiating localised 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.

Industrial manufacturing

AI agents can provide autonomous, real-time manufacturing process optimisation. 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.

Construction

These projects have unpredictable timelines, shifting regulations, and complex logistics, making real-time coordination and adaptability essential. AI agents don’t just analyse 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.

Risks in implementing AI agents

As AI agents become more autonomous, they bring a host of potential risks that organisations must carefully consider:

  • Privacy concerns: To function at their best, AI agents often require access to sensitive data. This access raises significant privacy issues, especially if this data is transmitted to external servers. Keeping robust data protection measures in place is essential to maintaining trust. 
  • Ethical dilemmas: AI agents act on their own, and sometimes they make high-stakes decisions. It is crucial to maintain IT guardrails to they do not learn to prioritise efficiency over fairness, misinterpret ethical boundaries, or take actions that conflict with business ethics and values.
  • Security vulnerabilities: Bad actors can exploit AI agents, manipulating them into making incorrect or even harmful decisions. Invest in comprehensive training for your teams, and ensure that your systems and solutions are using the best and most reliable cybersecurity measures.
  • Agent and model drift: Over time, AI models may experience "model drift," where their performance degrades due to changes in input data patterns. Similarly, "agent drift" refers to AI agents deviating from their intended behaviour as they evolve and learn from new data. Again, it’s essential to prioritise team training and top quality cybersecurity. 

Best practices and protocols for using AI agents

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. 

Piloted use cases

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.

Robust governance

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.

Assured human oversight

For critical processes, ensure that human-in-the-loop protocols are in place. AI agents should augment human judgement, not replace it. Final decisions, particularly those with significant impact, should have human validation.

Bias mitigation

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 makes sure that AI agents make fair and accurate decisions.

Up-to-date compliance

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.

Conclusion

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 realise 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. Embracing agentic AI is not just about leveraging this amazing technology; it's also about setting an example of doing so thoughtfully and responsibly. With good governance 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. 

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