Agentic AI Examples in the Automotive Industry: 4 Cases for Your Inspiration

Agentic AI Explained

If you are not a newbie in the artificial intelligence club, you have probably heard of such terms as agentic AI or AI agents floating around. Maybe something about the smarter version of traditional AI models or an impartial decision-making employer working day and night?

While some still hesitate about whether artificial intelligence is worth the investment for their businesses, others actively involve agentic AI in their everyday workflow, making the overall process easier, faster, more efficient, and precise. But what if the agentic AI definition is an uncharted landscape for you? No worries!

Below, we have given the clearest definition of what is agentic AI in simple words, provided real-life agentic AI examples, and ensured our readers stay updated with the latest business AI trends. Top insights already await you!

What Is Agentic AI, and How Does It Differ from Traditional AI?

In simple terms, agentic AI is an artificial intelligence tool that stands as an autonomous digital agent, capable of setting, achieving, or adapting your specific goals without permanent human intervention. Imagine it as your personal assistant, powered by artificial intelligence and dedicated to continuous self-improvement and achievement of your objectives.

The core idea of agentic AI lies in its ability to take initiative, plan, make decisions, and act in pursuit of goals, often without a direct human prompting. But what are the key characteristics of agentic AI? Let's have a closer look at the agentic AI's main traits:

Autonomy

Agentic AI, unlike a regular one, can decide what to do next without needing constant human instruction. For example, instead of simply cancelling your client's failed appointment, it would offer a few tailored options, schedule a new meeting, send a reminder, and contribute to your customer service at once.

Goal-Directed Behavior

Uses of agentic AI work toward defined objectives, not just one-off tasks. If you ask your AI agent to "find new customers on LinkedIn," it can research, reach out, track progress, and refine its strategy based on the incoming info.

Reasoning and Planning

As one of the most explicit agentic AI examples, it uses logic, memory, and multi-step planning to achieve pre-set goals. This approach makes it closer to how humans break complex tasks into smaller actions, rather than a machine learning model handling the sequence of actions at once.

Tool Use and Environment Interaction

In contrast with conversational AI or ML models, agentic AI can integrate APIs, use software, browse the web, send emails, or interact with external platforms, expanding its influence beyond mere text conversations. This, in turn, leads to an almost endless list of uses of agentic AI, being a universal soldier for any industry, from business and finance to the automotive industry.

Learning and Adaptation

The true wheelhouse of agentic AI systems lies in its ability to learn from feedback or changing conditions, improving its efficiency, accuracy, and reliability over time. Simply put, agentic AI is closely associated with continuous learning and can also be called Continual Learning AI.

As we are now familiar with agentic AI's "second name", let's still dive deeper into its first one :)

What makes agentic AI "agentic"— is it just automation with feedback loops or something more?

To begin with, and to set the record straight, agentic AI encompasses not only automation but also advanced reasoning, planning, decision-making, and adaptability to go far beyond basic automation.

To put it simply:

• Traditional automation: follows pre-programmed rules step by step. It can execute repetitive tasks efficiently, but it lacks the ability to adjust goals or rethink strategies when conditions change.
• Feedback loops in automation: allow systems to make limited adjustments, like regulating time limits or optimizing a simple workflow. Still, the system is reactive rather than proactive.

By contrast, agentic AI does not just react to the condition but also plans, makes decisions, and, most importantly, takes actions. Uses of agentic AI include automation of daily workflow tasks without human intervention, allowing the AI model not just to complete basic routine but to make reasonable decisions followed by beneficial actions.

But what makes agentic AI different from traditional AI?

Let's take a look at the key features making agentic AI different from regular AI:

Goal-Oriented Autonomy

Agentic AI can set intermediate objectives and plan sequences of actions to achieve a desired outcome without human micromanagement.

Contextual Reasoning

Unlike basic automation, agentic AI can analyze complex environments, weigh options, and choose the best plan forward.

Dynamic Adaptability

If any external conditions change, like a new variable in logistics or an unexpected hazard in autonomous driving, agentic AI can adjust, improve, or completely change its strategy in real time.

Multi-Step Action Execution

Instead of executing a single command, agentic AI can handle a chain of decisions, coordinating with other systems or digital agents to achieve top results.

In tacking stock, agentic AI, in contrast with a regular one, not only does what it's told, but also analyzes, makes decisions, and takes next steps on its own.

How Does Agentic AI Work?

With a solid background of understanding vital differences between agentic and regular artificial intelligence, we are ready to take a ride to its tech side asap. Below, we will reveal everything you need to know about how exactly agentic AI algorithms work.

Simply put, agentic AI works by combining traditional artificial intelligence models with autonomy and decision-making frameworks. In other words, instead of simply predicting the next word, classifying the picture, or tailoring the recommendation, agentic AI is designed to pursue goals, adapt to new inputs, and take actions across diverse tools or environments. Here are five key milestones of how agentic AI works:

Goal-Oriented Reasoning

When working with agentic AI, you start with a high-level objective, or a general goal for the model, like "summarize the research", "book an appointment", or "improve a sales strategy for my business". However, agentic AI does not just respond passively; It plans multiple steps, breaks the objective into smaller sub-tasks, and prioritizes what to do first, just like a human would do.

What does it mean for you? — Your AI agent can operate as a full-fledged helper, employee, or even a personal consultant as a part of your system.

Memory and Learning Loops

Unlike static models, agentic AI can utilize short-term and long-term memory algorithms. In a nutshell:

• Short-term memory: keeps track of the current task flow, context, and past actions in a session.
• Long-term memory: stores knowledge or results from previous interactions, so it can refine its approach over time.

What does it mean for you? — Your agentic AI model can be refined, improved, or modernized over time based on its previous requests or any changes your business experiences.

Tool Use and Environment Interactions

Most importantly, agentic AI is not confined to text generation. Moreover, it can call external tools: databases, APIs, search engines, coding environments, and other tools. In addition, agentic AI can observe the results of its actions and adjust the next step accordingly.

What does it mean for you? — The agentic AI model can work not only within your internal system but also analyze external information sources, cooperate with third-party APIs you would like to include, like scheduling tools, chats, databases, visual info, or any other potentially useful sources. This makes agentic AI a perfect tool for your massive datasets or complicated multi-step daily tasks.

Self-Reflection and Error Correction

As one of the most beneficial traits, many agentic systems have a built-in "critic" or a so-called reflection loop. For instance, after generating an answer or taking an action, AI agents conduct a thorough check: "Does this move me closer to the goal?". If not, the model can revise the plan or retry with a new approach until it reaches the closest to the ideal level of quality.

What does it mean for you? — With agentic AI, you can be sure your plan will be completed in the most favourable way for your business, taking all the necessary steps without a thorough human intervention or micromanagement.

Continuous Adaptation

Agentic AI is all about the combination of planning, memory, tool use, and self-reflection, making it flexible, adaptive, and trustworthy. On the one hand, it can handle dynamic tasks where conditions change. And at the same time, it can chain together multiple reasoning cycles, improving performance as it goes.

What does it mean for you? — There is no need to observe your agentic AI model permanently, letting it conduct a self-development process and improve its performance along with your business evolution.

As we have now dived deeper into the peculiarities of the agentic AI, it may seem like all cakes and ale, but before moving straight to the agentic AI examples and its power for your business, we must consider a few potential challenges in order to avoid mistakes and mitigate the risks.

What are the main challenges in building safe and reliable agentic AI? (And how to mitigate them)

When taking advantage of agentic AI implementation, businesses and developers always have to consider a few vital challenges they may face, and be aware of trustworthy mitigation strategies. Let's have a look at them:

Autonomous Decision Safety Concerns

When agentic AI makes decisions on its own, even minor errors can escalate. For example, a model handling documentation trades or medical recommendations could make unsafe moves without human oversight.

How to mitigate the risk?

• human control or manual approval for high-impact decisions;
• create sandbox environments and conduct proper model training before its deployment;
• implement fail-safe defaults, meaning the AI agent stops and "asks for help" in critical situations instead of guessing.

Alignment with Your Intentions

Agentic AI can interpret goals differently than intended, leading to outcomes that technically meet the request but fail the spirit of it. For instance, choosing not the best goal-reaching strategies and risking your reputation.

How to mitigate the risk?

• proper prompting and its refinement, along with objectives and model evolution;
• teaching your agentic AI not just what to do, but rather how, in order to meet your personal vision;
• ensuring the model's permanent learning based on its experiences.

Transperancy & Explainability

Agentic AI decisions are often a "black box." For agentic systems, the lack of visibility into planning steps and reasoning makes it hard to trust or audit them. This approach may not seem transparent enough and cause potential misunderstanding or even data risks.

How to mitigate the risk?

• integrate Explainable AI algorithms and deep learning to create complete transparency for your agentic AI actions;
• possibly, create user dashboards, providing summaries of model decisions, confidence levels, and considered alternatives.

Security & Adversarial Risks

As agentic AI interacts with external systems, it is exposed to adversarial inputs, like malicious prompts, poisoned data, or exploit attempts. Potential attackers could manipulate its memory, tools, or outputs, resulting in your sensitive data leaks or even system damage.

How to mitigate the risk?

• sanitize all the data before agentic AI reaches it;
• implement access control for the sources and tools your AI agent can use;
• continuously simulate adversarial attacks to expose potential pain points.

Regulation & Ethical Standards

Agentic AI can cross legal or ethical lines if left unchecked, from data privacy violations to biased decision-making. And it is vital to ensure your AI agent does not violate anybody's rights or break crucial rules, causing potential regulatory issues.

How to mitigate the risk?

• start from the very beginning and implement fairness, privacy, and inclusivity checks during the agentic AI development stage;
• conduct regular audits against GDPR, AI Act, or any other local laws;

Forewarned is forearmed! That is why devabit is always transparent about the risks or challenges you may face. Any issue can be solved or prevented unless you are aware of it.

4 Exclusive Agentic AI Examples in the Automotive Industry

Finally! The automotive world is moving faster than ever, and 2025 is the year agentic AI starts turning futuristic ideas into everyday reality. In order to fully reveal the potential of AI agents as a perfect co-driver, let's take a look at the top four standout agentic AI examples in the automotive industry.

01 / Mercedes-Benz MBUX + Google Automotive AI Agent

Have you ever wanted Siri to escort you on a long drive? Or ChatGPT to recommend the best route to take today? That is what Mercedes-Benz thought of and made literally a boom in the innovative automotive!

It all started with Google rolling out the next-gen AI agent for the top automotive solutions and Mercedes introducing the MBUX Virtual Voice Assistant, triggered by "Hey Mercedes!" that could handle up to 20 commands. And then, all of a sudden, the collab of the year just happened!

The new agentic AI assistant now features four "personality traits", including natural, predictive, personal, and empathetic. But what does the assistant do?

The MBUX, in collaboration with Gemini, Google Maps, and agentic AI algorithms, supports continuous conversation, handles multi-turn dialog, gives navigation help, recommends points of interest, and integrates predictive suggestions. The main concept behind this agentic AI example is to imitate human-like interactions and create unmatched driving experiences for Mercedes users. From speech tone preferences to complete customization and tailored approaches, MBUX is about to become your best co-pilot. Forget about the request-response format, cause Mercedes' agentic AI assistant is about making real-time decisions, reasonable predictions, and taking your interest-driven actions.

02 / Effortless Vehicle Maintenance & Dynamic Management Platform

How about taking a look at the other side of the barricade? Suppose you are not the driver seeking tailored experiences, but a dealership owner trying to deliver the best customer service ever. Meet the dynamic pricing & capacity management solution for the automotive industry by devabit!

What if a vehicle maintenance process transforms into a personalized customer experience via the power of agentic AI? That is what the developers thought, and immediately transformed the concept into a profit-making solution.

What does it have to do with agentic AI?

In the case of our client, the dealership severely struggled with fulfilling specific time slots for car repair or maintenance services. While some slots are overbooked, and most of the drivers never get to reach the services they seek, others remain empty from day to day, leaving the dealership with hours of idle time and lost income. That is when we remembered of agentic AI.

Within our client-centric platform, the agentic AI model serves to analyze the most neglected by clients time slots and make them one of the most demanded. But how?! Watch us.

Agentic AI algorithms have processed the input data, customer preferences, previous bookings, service statistics, and our client's goal to build a perfect strategy for fulfilling the necessary time slots and boosting sales. When the potential visitor enters the platform to book a vehicle maintenance appointment, the AI agent already knows what time to offer and how to help the client make the right choice. Less popular slots suddenly become a way cheaper option, while the system is also offering a free headlights check, cause the system sees your last headlight repair in the system. Who would ever reject such an offer?

Simply put, agentic AI serves to analyze a set of diverse data, like slots popularity, booking statistics, your previous appointments, vehicle data, and repair history, in order to create a tailored win-win strategy for both drivers striving for beneficial offers and dealerships seeking profitable solutions for their pain points.

Is Agentic AI Limited to Slot Booking?

No, and one more time, no! In addition to boosting non-buy hours by analyzing user and vehicle data, agentic AI is designed to deliver real-time analytics and smart reports for both dealership employees and visitors. Want to track your car expenses, have a transparent vision of your vehicle's condition, and know when to schedule the next running gear check? The AI agent within our solution ensures you put zero effort and have maximum profit.

What about the result?

• 16% - 24% increase in service gross profit;
• up to $240,000 extra money revenue for dealerships;
• 10% - 25% increase in service revenue;
• $1.5 million in seed funding.
  

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Drawing the bottom line, our above-mentioned solution is one of the most prominent agentic AI examples benefiting both the provider and the user. Maybe it is your turn to take the most out of agentic AI in the automotive?

03 / NIO's NOMI — World's First In-Vehicle Artificial Intelligence

Getting back to the origins, we cannot forget to mention NOMI and its vital contribution to the role of agentic AI in the automotive industry. Starting from NIO’s mission to transform the cabin into a traveling digital living space for NIO’s users and friends, the company has introduced the friendliest co-pilot, NOMI.

Driven by the philosophy of “phys-gital” interaction, meaning physical and digital, NOMI serves a key role in that interaction by becoming the embodiment of tailored services that facilitate a greater emotional connection between users and their vehicle. In a nutshell, the aim was not to just offer tailored driving experiences but to create a sense of emotional connection between the driver and the AI agent. And let us just tell you, they succeeded.

As one of the agentic AI examples, NOMI learns user preferences over time to understand the specific context of the vehicle in relation to its owner. For instance, the system can set the personal seating and steering wheel positions whenever it senses a driver approaching the car. Users can also give NOMI verbal commands to adjust the cabin temperature, open or close windows, or even snap an in-car selfie and have it displayed on the radio screen. Thanks to advanced agentic AI algorithms, NOMI can not just do your commands but make informed decisions in order to polish your ride before you even get to think of it. If you ever wanted your vehicle not just to be intelligent but also affectionate, NIO has already polished this idea with agentic AI.

04 / Volvo's Smart Eye — Avoiding Road Fatalities on a Whole New Level

Not so long ago, Smart Eye has announced that a brand new Volvo EX90 is now equipped with a premium Driver Monitoring System (DMS) software, which will provide a deeper understanding of the state of the driver to help further enhance traffic safety.

Despite electric vehicles establishing their dominance in the innovation market, Volvo is still making some real waves in the industry by introducing and standardizing a DUS in its new fully electric EX90.

This agentic AI technology combines the industry’s first dual camera DUS with a capacitive steering wheel. Powered by Smart Eye’s advanced agentic AI algorithms that analyze driver eye, face, head, and body movements, the system enables a deeper understanding of what is happening with people inside a vehicle, and in particular when a driver, for some reason, is not entirely concentrated on the driving task.

This approach, in combination with agentic AI algorithms, not only enables notifying the driver about the potential hazards but also actually prevents them via autonomous decision-making. This agentic AI example is a perfect-fit solution for drivers still doubting about driverless vehicles, but at the same time, seeking safe and innovative driving experiences.

Agentic AI Examples: What Is Next?

2025, the year of artificial intelligence revolutions, economic movements, and global transitions, and the automotive industry is no exception. While some are desperately trying to impress the drivers of all ages with the safest seatbelts and the fastest acceleration, others stick to the innovation side and choose the most revolutionary approaches. The shift from "What is agentic AI?" stage to it being a part of any next-gen system has happened way too rapidly, but only the best managed to seize an opportunity and get the most out of it.

The real question is: are YOU ready to become the next revolutionizer adopting agentic AI for your business? If your answer is undoubtedly yes, devabit is delighted to help you move from a mere concept to significant results. Leave a quick message for our team in the Contact Us section and receive your free agentic AI consultation asap.