What is Agentic AI and How Does it Work? (Ultimate Guide)

If you ask ten different people in the tech world what an "AI agent" is, you may get ten different answers. Truthfully, we're all figuring this out as we go, and the definitions, scopes, and use cases of AI tools seem to change by the week.

Here's a definition that most folks would probably agree with, though:

Agentic AI is an artificial intelligence system that can accomplish specific goals with limited supervision, acting autonomously to make decisions and perform tasks without constant human guidance.

Unlike traditional AI models that operate within predefined constraints, agentic AI exhibits autonomy, goal-driven behavior, and adaptability to changing environments.

Artificial intelligence is changing how we approach the workplace, from the ideation process to how we interact with customers. While many AI tools have shortcomings concerning informational accuracy and social nuance, agentic AI systems seek to change that.

The market value of agentic AI is growing at a lightning fast pace, with industry projections expected to surpass $47 billion by 2030. Industries around the world are in active need of adaptable, autonomous tools that can oversee everything from customer service to supply chain management.

Generative AI models are one of the most common AI systems available today, but they've drawn scrutiny for struggling in several key areas. Recent studies have found AI hallucinations -- a term for AI systems making up information -- happens anywhere from 3% to 27% of the time. When AI has the potential to solve complex tasks or oversee businesses, there's a major need for more accurate, reliable AI solutions.

What is agentic AI and how can it potentially solve some of the problems of other AI tools? Even more, how can both of these AI systems work together to reduce human intervention on the way to business growth?

Key Takeaways:

• Agentic AI is a form of artificial intelligence that reduces human oversight, enabling AI to make complex decisions, multitask, and learn from previous datasets. It uses natural language processing, machine learning, and predictive analytics to go where other AI models can't.

• Agentic AI operates under agentic architecture, a complex digital framework that oversees autonomous AI agents and aligns their actions with business objectives. They help businesses automate workflows, reduce operating costs, or fill in gaps for labor and skill shortages.

• Implementing AI solutions is easier when you have a dedicated marketing partner who can work within your business's existing framework. Rightpoint is a go-to resource for brands and organizations who want to tap into today's best digital tools and remain competitive.

Understanding Agentic AI: Key Characteristics & Functionality

Agentic AI represents the next evolution in artificial intelligence, characterized by its ability to perform complex tasks with minimal human oversight. These systems fundamentally change how businesses approach automation by combining autonomy with adaptability.

Core Elements of Agentic AI

Autonomous Decision-Making: Unlike traditional AI that requires constant guidance, agentic AI operates independently through sophisticated programming including machine learning and natural language processing. This independence allows it to make decisions and take actions with minimal human intervention.

Complex Problem-Solving: Agentic AI systems handle multifaceted challenges by directing other AI components, multitasking, and improving their own performance through self-learning protocols. This enables them to address issues that would normally require significant human expertise and attention.

Access to Data and Tools: Agentic AI systems rely on comprehensive access to diverse data sources and digital tools to function effectively. Unlike simpler AI that works with limited, pre-defined datasets, agentic AI can query multiple sources simultaneously, including APIs, databases, web resources, and real-time inputs.

This broad access to information enables more contextually aware decisions and allows the AI to leverage specialized tools for specific tasks, similar to how human experts use different resources depending on the challenge at hand.

Adaptive Intelligence: Rather than requiring frequent updates, agentic AI adapts in real-time to changing conditions through self-monitoring and improvement. This adaptability includes self-improvement in dynamic environments, error detection, and providing recommended fixes based on new data.

How Agentic AI Functions

Data Collection & Integration: AI agents build robust datasets from multiple sources including human input, external datasets, web searches, and APIs. This continuous gathering of information allows them to make increasingly informed decisions over time.

Decision Execution: Using sophisticated algorithms for risk assessment, pattern recognition, and predictive analytics, these systems make informed decisions based on real-time data. These decision-making capabilities form the "brain" of AI agents, enabling them to mimic valuable human skills like risk mitigation and situational awareness.

Continuous Improvement: Through iterative feedback loops and real-world interactions, agentic AI continuously refines its performance. While some human intervention remains necessary for complex issues, the self-improvement protocols significantly reduce the debugging burden on developers.

The Technology Stack Behind Agentic AI

• Large Language Models (LLMs): Enable natural language understanding and generation, helping AI interpret human communication with deeper understanding of semantics and syntax

• Machine Learning: Powers pattern recognition and adaptation capabilities

• Predictive Analytics: Supports forecasting and informed decision-making

• APIs and Integration Tools: Connect AI systems to external platforms and data sources

What is Agentic AI vs. Generative AI?

While agentic AI executes specific, complex tasks, generative AI models create content based on text-based prompts.

These models often make up the critical building blocks in an agentic AI workflow, but are supplemented by the other tools, data, and instructions that make the workflow more autonomous.

How Agentic AI Systems Work: The Power of AI Agents and Agentic Architecture

AI agents are the foundation that separate agentic AI from other automated tools. Each AI model uses a mixture of predictive analytics, natural language processing, and machine learning to drastically reduce human oversight.

We're going to break down how each tool contributes to AI agents, the platforms they run on, and their most common use cases in business today.

What Makes an AI Agent so Useful?

What makes an AI agent so useful are how they can understand natural language, problem solve and even improve their own programming. Since each AI agent fundamentally draws upon a business's specific data, they can even provide useful business insights or spot fresh opportunities that human employees may overlook.

While a chatbot can answer basic questions or book tickets to a show, it can't understand the deeper nuances or context of human speech. Its limitations become clear when cultural differences or tone enter the picture, which is nothing to say of the unique business needs of different industries.

Without the ability to understand conversations on a deeper level or evolve, chatbots remain a cost-effective -- yet limited -- resource.

What are the Different Types of AI Agents?

Unsurprisingly, agentic AI's adaptable roots make it easy to customize each agent to suit the needs of different business models. In fact, multiple types of AI agents tend to work better together by complementing different skillsets.

Below are a few of the most common types of AI agents and the specific tasks they're best suited for.

Model-Based Reflex Agents

This type of AI agent is both reactive and proactive, providing businesses with an AI assistant that's able to gradually adapt to the world around it. While it's still not the most complex AI agent available, it can achieve more than a purely reactive AI that only responds to direct prompts.

Model-based reflex agents are able to store new information, update their memory, and make minor adjustments on-the-fly.

An everyday example of a model-based reflex agent are self-driving cars. While these self-driving cars still require input from the driver, they're able to operate independently to a limited degree based on external feedback. They can accelerate based on certain inputs, then brake based on others.

Agentic AI: Workflows vs. agents

Utility-Based Agents

When one of the most common reasons why businesses turn to AI agents is to make everyday operations run more smoothly. Whether it's completing tasks faster or reducing mistakes, utility-based agents are rooted in an efficiency approach.

Utility-based agents come with programming that prioritizes reaching an end result in the most efficient way possible. These agents follow a set of fixed criteria to give them direction and help them gradually improve. Businesses can easily modify utility-based agents to fit monthly or annual goals, then change as their needs evolve.

One common example of a utility-based agent are recommendation systems for online stores. To help businesses provide a more personalized customer service experience, their efficiency-based programming prioritizes details that increase a customer's chance of staying on the page. They'll recommend products based off of past purchases or heatmaps that show customers lingering on certain parts of a page.

Learning Agents

These agentic AI systems are in high demand due to their ability to learn and change their behavior based on ongoing feedback. Unlike model-based reflex agents, they're able to integrate a more complex feedback loop that analyzes their behavior and makes finer adjustments.

An example of learning agents are email spam filters that gradually improve based on feedback. If it notices its email user constantly putting certain emails back into their inbox from the spam filter, it will analyze these emails to find common patterns and reduce the chances of the wrong emails being sent to spam.

This adaptable programming saves users time having to manually go into their spam filter settings and adjust them over time.

Hierarchical Agents

Under a hierarchical model, multiple AI agents will operate under an AI agent with more autonomy and decision-making capabilities, not unlike a manager who will oversee a team of workers.

You're likely already familiar with hierarchical agents from complex assembly lines in manufacturing plants. A major reason why these assembly lines are so efficient are thanks to hierarchical AI agents overseeing the production line, planning and executing tasks while lower AI agents handle basic assembly.

Goal-Based Agents

Goal-based agents may seem similar to model-based reflex agents at a glance, but they're a little more complex. While model-based reflex agents are still partially reactive and have limited independence, goal-based agents are able to thoroughly plan their actions before acting on a goal or set of goals.

As the saying goes: limitation breeds creativity. A goal-based agent that comes across limitations when attempting to reach a goal will turn to alternatives instead. Since its primary function is to reach its end goal in the fastest and most accurate way possible, it's able to think critically in ways simple AI agents struggle with.

A goal-based agent could help a business run more effectively by finding alternate routes in supply chain management. They could provide recommendations for shorter routes to lower costs or carbon emission output. This type of agent is incredibly valuable in an overworked team that operates on a tight schedule, spotting what a tired human worker might overlook on their shift.

Further Reading: Anthropic, the creators of Claude, have an excellent, approachable article on the most common types of Agentic AI frameworks.

How Do AI Agents Work?

You can think of AI agents as digital helpers for human employees, operating within the virtual organization of agentic architecture. Just like a business provides the overarching philosophy and values that drive everyday operations, agentic architecture provides the overall structure to the AI platform deploying these AI agents.

AI agents work with both people and other agents to address, dissect, understand, and solve various business problems. Below are a few actions agentic AI take on an average day:

Collecting Data to Build Data Sets

AI agents aren't able to adapt quite as well without regular access to real-time data, so one of their main tasks is building data over time. These data sets come in a variety of options, such as:

• Human input

• Other AI agents

• External data sets

• Web searches

• API (Application Programming Interface)

The last data set is particularly profound since it contributes to the autonomy that AI agents are becoming known for. An API involves two computer programs or applications communicating with each other to share data or recent updates.

Making Informed Decisions and Taking Action

Thanks to a steady diet of real-time data from multiple sources, AI agents aren't operating mindlessly. They're able to use crucial business data -- ranging from statistics to charts to user behavior -- to make informed decisions.

These decision-making algorithms form the 'brain' of AI agents, enabling them to recreate valuable skills such as:

• Risk mitigation

• Course correction

• Pattern recognition

• Predictive analytics

• Situational awareness

With the help of these powerful tools, multiple AI agents can execute tasks and supplement complex workflows with few of the common errors of simpler AI tools.

Adapting and Improving

Businesses need to save time more than ever to meet industry demand, which can be tricky with simplistic AI tools that are prone to common errors like AI hallucinations or glitches. Agentic AI solutions provide self-improvement protocols that free up the busy schedule of programmers and managers.

Agentic AI stands a cut above other AI tools for its ability to gradually improve itself based on past mistakes. Does this feature mean that no human intervention is needed at all? Not quite: developers will still need to intervene from time-to-time on particularly complex issues that AI can't solve. For example, 86% of customers in a 2022 survey stated talking to a real person is still very important when doing business.

However, this ongoing monitoring and debugging makes developers' jobs easier by giving them fewer problems to fix and helping them spot repeated patterns of improvement.

AI agents regularly perform iterative feedback loops rooted in real-world interactions that help spot potential weaknesses. These weaknesses could range from missing data to spotting data siloes that are slowing down communication.

The AI Tools Enabling AI Agents to Go Above and Beyond

What intelligent systems and tools are helping AI agents solve complex problems or automate tasks? Alongside drawing upon training data, AI agents use a mixture of traditional AI tools and real-time datasets to complete complex tasks.

Large Language Models (or LLMS)

With how multifaceted and subtle human languages are, AI capabilities have often shown limitations trying to meet people halfway. Large language models train AI on massive amounts of data to help them recognize, understand, and generate natural text that connects with readers.

With the help of large language models LLMs, vital details such as:

• Semantics

• Syntax

• Ontology

• Machine Learning

Machine learning models are a subcategory of artificial intelligence that enables AI tools -- including agentic AI systems -- to learn from large data sets. These tools are able to spot patterns, draw conclusions, and make more informed decisions.

Agentic Architecture: The Structure That Makes Agentic AI Possible

If agentic AI models are like workers, agentic architecture is like the organization they work within. Agentic architecture is the digital platform that shapes and oversees the virtual spaces in which AI agents operate.

Without agentic architecture overseeing and regulating the behavior of agentic AI systems, these models won't be able to successfully connect with customers or adapt in ways that align with business goals.

What is Single-Agent vs Multiagent Architecture?

Now that you understand what agentic architecture is, let's further break down this structure based on business needs. Single-agent and multiagent architecture's main difference are how many AI agents they use, yet this simple distinction creates a completely different resource.

Single-Agent Architecture is Easy to Design and Deploy, Yet a Little Limited

A single-agent architecture uses one AI agent to independently interact with digital environments and datasets, autonomously making decisions and achieving multiple business goals. They don't work with other AI agents and require fewer resources as a result.

Many smaller businesses and start-ups appreciate single-agent architecture because of how easy it is to monitor and debug a single AI agent. When a brand is already low on staff and working within a tighter budget, single-agent architecture provides a more predictable and cost-effective solution.

Single-agent architecture can provide particularly sophisticated chatbots to help with customer service or advanced recommendation systems for e-commerce applications. A single AI agent will provide a higher level of nuance that translate into saved time for customers and business owners, yet these applications are simple enough for its limited scope.

The challenges emerge when a business needs to scale. While agentic AI is an incredibly useful resource that frees up staff for day-to-day tasks, a single agent can only do so much when facing a higher-than-average volume of complex tasks. Businesses that want to scale may have to turn to multiagent architecture to keep up with growing customer or industry demand.

Multiagent Architecture is More Complex, Yet Requires More Resources

Multiagent architecture is often what people think of first when it comes to agentic AI systems. This incredible resources harnesses the capabilities of several AI agents to provide a result not unlike a particularly well-trained team.

While each AI agent will provide a specialty -- anything from data aggregation to risk mitigation -- each agent will collaborate with the others to provide a productive feedback loop. They'll collaborate to solve problems, spot errors, and gradually evolve their capabilities.

Just like a skilled team, agentic architecture will often have a hierarchy within AI agents to ensure seamless collaboration. A designated 'leader agent' may assign or analyze subtasks, while other agents will report back to the leader agent with updated feedback or new data.

This clearly outlined hierarchy means these agents aren't just running around aimlessly, but maintaining a consistent pattern of behavior that stays aligned with business goals.

Multiagent architecture is well-suited to more complex solutions like multistep workflow automation and dynamic problem solving.

What is Agentic Automation?

A term you'll regularly come into contact with when learning about agentic AI is agentic automation, a catch-all term for the combination of AI agents, traditional AI, and RPA robots. RPA is short for Robotic Process Automation, a more limited programming form defined by fixed outcomes and limited possibilities.

Agentic AI's ability to understand, evolve, and self-improve sets it apart from RPA automation, though it can still harness RPA solutions for simple, streamlined workflow operations.

The Three Functional Components of Agentic Architecture

Just like you wouldn't expect your workers to achieve business goals without proper guidance and management, so too do you need to provide a structure for agentic AI agents. The three main components that align agentic AI systems with business goals replicate how an agency would operate.

1. Intentionality: Agentic AI needs a goal to gradually work towards, whether it's making smart product suggestions to customers for an e-commerce agency or automating repetitive tasks in supply chain management. Whether single-agent or multiagent, agentic architecture works best when it's thinking critically and giving your business another angle with which to view real-time data.

2. Forethought: The ability to anticipate and plan for the future is a skill you can take for granted. This ability is one of the defining features of agentic AI systems to free up human workers, able to analyze data with a forward-thinking mindset to increase chances of success or reduce errors.

3. Self-reflectiveness: Agentic AI systems are so advanced, they're able to analyze their own data and reach conclusions without constant human input. While they're not at the stage of self-aware AI programs in science-fiction films, they've come a long way from human intervention-heavy generative AI. As such, the architectural model should encourage active self-reflectiveness to ensure each AI agent is bringing more value to the table.

The 5 Benefits of Incorporating Agentic AI Into Your Business Model

PWC's 2024 Pulse Survey saw 49% of technology leaders stating AI was now fully integrated into their core business strategy. Another third stated they built AI into their products and services.

From customer relationship management to more robust security measures, agentic AI enables your business to grow in ways you never thought possible.

1. Unparalleled Scalability

When your business is growing faster than you can keep up with, agentic AI systems will keep pace without a hitch in business operations. Their fundamental ability to grow and adapt based on new data means they can quickly contribute to decision-making with limited supervision.

This result means you can expand your operations, collaborate with other businesses, or pivot to a new industry with a tool that grows with you.

2. Immediate Availability

Sometimes you're low on time for training staff or bringing on freelancers. Agenetic AI offers more flexibility by providing immediate availability for complex tasks and simple tasks alike.

3. Practical Insights From Hard Data

Agentic AI provides a long-awaited solution to AI hallucinations and limited programming, providing businesses with useful insights based on their own data. Depending on the AI agent you deploy, you can do everything from optimize workflows to spotting opportunities for new growth.

4. Increased Customer Satisfaction

A 2020 analysis by McKinsey estimated that AI solutions can deliver up to $1 trillion in value every year for banking institutions alone, particularly for customer service divisions. The potential for customer service in your own business is only just beginning.

When customers today expect rapid responses and personalized experiences, agentic AI lets you balance the human touch with digital evolution.

5. Saving Employees Time and Effort

Workers are ready and waiting to use AI solutions for specific tasks or saving time. A 2023 Gallup Workforce study saw 47% of employees wanting to use more AI tools, a dramatic contrast compared to just 16% of human resources officers believing their workplace is ready for AI.

The gap between worker readiness and business confidence needs to be bridged as soon as possible. With AI solutions only growing more useful and accessible, the businesses that take the plunge and start adapting now will be ahead of the curve.

It's one of the many reasons we provide comprehensive training and onboarding courses for businesses who need to implement AI platforms into their existing workflows.

Use Cases of Agentic AI in Different Industries

Understanding agentic AI in action is easier when you see how it operates in different industries. Below are a few common use cases of agentic AI and what you can expect when shifting toward a more technologically advanced business model.

Agentic AI in Ecommerce

We touched on agentic AI in ecommerce when it comes to product recommendations, but that's just one of many functions of this adaptable digital tool. Agentic AI in ecommerce can also take the form of inventory management to save businesses time and money shipping out products.

For example, AI agents can analyze inventory trends to reduce the risk of leftover inventory. It can assess current inventory levels, make accurate predictions based on past purchasing trends, and adjust inventory allocation in real-time.

An ecommerce brand that usually sees overstocking in certain warehouses during peak shopping seasons can turn to agentic AI to maintain stock levels, reducing the risk of overstocking and ballooning holding costs.

Agentic AI in Supply Chain Management

The global supply chain is volatile at the best of times, frequently shifting based on sudden tariffs, bad weather, or labor shortages. Agentic AI is incredibly useful for monitoring external factors and providing a consistent feedback loop to keep business owners ready.

For example, a manufacturer may depend on AI agents to provide swift responses to forecasted delays. Instead of waiting for human workers to eventually learn about the delay and react, it can respond immediately with rerouted shipments and schedule adjustments to save on precious time. It can even provide automated responses to customers to reduce the risk of confusion from manufacturing clients.

Agentic AI in Banking

A 2023 FTC study saw consumers reporting over $10 billion lost in fraud, with bank transfers one of the biggest contributors. More banking institutions are turning to AI agents to catch the suspicious behavior that slips through the cracks to protect customers and employees alike.

AI agents can handle simple tasks for customers, such as automating transfers to avoid overdraft fees or providing investment recommendations based on credit scores. It's also a powerful tool for analyzing data to find missing or inaccurate information, some of the most common details leading to fraud. It can flag anomalies or draw upon previous fraud data to predict the likelihood of fraud or cyberattacks.

Agentic AI in Healthcare

According to a Medi-Tech insights prediction, agentic AI in healthcare will see a staggering growth rate of 35% or more in the next five years. While AI agents won't be able to replace seasoned medical staff, they can support their day-to-day activities by reducing the risk for human error and ensuring sensitive data doesn't get lost.

On the surface, agentic AI can help with surface-level tasks, such as booking appointments or following up with patients. They can also use predictive analytics and learning algorithims to provide tailored treatment plans, predict medicine shortages, or assist with physician diagnoses.

These are just a few of the ways agentic AI create businesses with fewer errors, happier customers, and more opportunities for growth. With Rightpoint by your side, you can seamlessly blend AI-powered platforms and tools into your existing workflow.

How Rightpoint Uses Agentic AI to Help Businesses Harness Real Time Data

Traditional AI isn't going to be enough to help your business stand out as a trustworthy resource to clients and customers. Rightpoint is your marketing partner to help you pivot toward the streamlined, adaptive business model you need to remain competitive.

We know how daunting adapting to new technology can be, especially when it's evolving as rapidly as agentic AI. Our AI services provide you with a well-rounded overview of adoption strategies, learning plans for your employees, and industry-tailored AI tools.

From AI powered platforms to generative AI solutions, we want to ensure you're not only equipped with the right tools: you know exactly how to use them. When you're ready to start streamlining workflows and saving your employees time, contact us for a consultation.