Agentic AI Explained: Definition, Benefits, and Use Cases

Imagine an AI system that automates financial audits, streamlines supply chains, or handles customer service beyond basic interactions. Instead of waiting for instructions, the system resolves issues, personalizes solutions, and gets the best outcomes with minimal human oversight.  

This is the new frontier emerging: agentic AI—systems that don’t just respond to queries but autonomously plan, execute, and adapt to complex, multi-step tasks.  

Gartner has named agentic AI one of the top technology trends for 2025, highlighting its potential to transform industries. Unlike traditional artificial intelligence (AI) models that passively generate responses, agentic AI can execute tasks, collaborate with other systems, and adapt in real time. 

In this article, we will talk about agentic AI, its benefits, and real-world applications. Let’s explore why this technology will redefine automation. 

What is agentic AI? 

Agentic AI is an advanced AI system that can act independently, make decisions, and adapt to changing situations. These AI systems can handle complex tasks such as strategic planning, multi-step automation, and dynamic problem-solving with minimal human oversight. This makes them more capable than traditional rule-based AI. 

Unlike conventional AI, which is programmed for specific tasks, agentic AI can interpret complex goals, understand context, and make informed decisions. This allows for more dynamic and context-aware interactions, enabling AI agents to take meaningful actions rather than just responding to commands. 

Agentic AI is designed to work like a human employee performing tasks that comprehend natural language input, set objectives, reason through a task, and modify actions based on updated input. It employs advanced machine learning, generative AI, and adaptive decision-making to learn from the data, refine its approach, and improve performance over time. 

Understanding agentic AI’s key characteristics is the first step toward learning how to use it:

• Goal-oriented: Focuses on achieving specific objectives and adjusts strategies to optimize results. For instance, an AI agent tasked with improving customer satisfaction can analyze feedback and identify common complaints to provide better solutions. 

• Adaptability: Learns from interactions and feedback to improve performance over time. For example, imagine a virtual assistant refining its recommendations based on user preferences and learning which responses lead to better engagement. 

• Autonomy: Operates independently, making decisions and taking actions without human intervention. For instance, an agentic AI system managing system maintenance can detect performance issues and schedule fixes without waiting for human intervention. 

• Environment interaction: Observes changes in its surroundings and modifies actions accordingly. For example, a scheduling AI detects last-minute changes in availability and automatically reschedules meetings to minimize disruptions. 

• Multi-agent collaboration: Works alongside other AI agents and systems to handle complex workflows. For example, a document-processing AI can collaborate with a compliance-checking AI to review contracts, flag issues, and ensure regulatory adherence.  

• Integration with tools: Connects with external platforms (e.g., emails, databases, or automation tools) to execute tasks efficiently. For instance, an AI assistant can integrate with communication tools like Slack to summarize discussions and suggest action items for follow-ups. 

The benefits of agentic AI 

Agentic AI provides numerous benefits and is gaining traction across industries such as healthcare, IT support, transportation, finance, and cybersecurity. Its ability to operate autonomously and adapt to new challenges brings several advantages that can transform business operations. 

Here’s how agentic AI is making a difference for enterprises: 

• Increases efficiency and productivity: Agentic AI can quickly and accurately handle repetitive tasks like data entry or managing IT system updates, freeing employees to focus on more important work. This leads to faster processes and smoother workflows across teams. 

• Improves customer satisfaction: By handling customer queries faster and more accurately, agentic AI can provide a more personalized experience. Businesses can improve response times and resolution rates, increasing customer satisfaction. 

• Supports scalability: As businesses grow, the volume of tasks and data increases. Agentic AI can quickly scale to handle more work without significant resource increases, helping companies maintain efficiency as they expand. 

• Enhances decision-making: Agentic AI helps businesses make smarter decisions by analyzing large amounts of data in real time. It highlights key insights, patterns, and risks, allowing companies to act faster and more effectively. 

• Reduces operational costs: Automating everyday tasks means fewer manual hours are needed, cutting labor costs. Businesses can run more efficiently without extra expenses, with fewer errors and more consistency. 

• Ensures accuracy and consistency: Agentic AI executes tasks precisely, minimizing human errors and maintaining uniform standards. Whether processing financial transactions or managing compliance checks, AI agents ensure reliability and adherence to policies at scale. 

How agentic AI works 

Before getting into the problem-solving process of agentic AI, it’s essential to understand its architecture. Agentic architecture is the structure that guides how AI agents behave. It allows them to perform tasks and adapt to changing situations independently. This framework helps them connect with different systems, process data, and make decisions without constant human input. 

Like humans think, plan, and reflect to reach goals, agentic AI follows similar processes. The architecture supports key elements such as  

• Intentionality: The ability to set goals and plan actions. 

• Self-reactiveness: Continuously monitoring performance and making real-time adjustments. 

• Self-reflection: Learning from feedback and refining behavior over time. 

Through these elements, agentic architecture allows systems to handle complex tasks, continuously improve, and operate in dynamic, real-world settings. It integrates AI agents with various data sources and external systems through APIs, ensuring they have the right information to make informed decisions. 

With this architecture in place, the agentic AI systems can then follow a four-step process to solve problems efficiently and autonomously: 

1. Perception: The AI agents gather data from their environment, whether from sensors, databases, or APIs. This data helps them understand key elements of its environment, build context, and identify relevant patterns or opportunities. 

2. Reasoning: After perceiving the data, the agent passes it to a large language model (LLM). The LLM processes all this information by identifying patterns, drawing connections, and applying logic to generate informed conclusions. This enables the AI to develop context-aware reasoning and improve its problem-solving ability.  

3. Action: With a plan in hand, the agent takes action by interacting with various systems or tools via APIs. Whether it’s updating databases or sending notifications, it carries out its tasks autonomously, without the need for human intervention. 

4. Learning: As the agent performs tasks, it continuously learns and adapts from its actions. The feedback loop helps the AI system refine its decision-making process, improving its efficiency and accuracy over time. 

Agentic AI vs. generative AI 

Agentic AI and generative AI are powerful technologies, but they operate in fundamentally distinct ways. While both use advanced machine learning (ML) techniques, they differ significantly in their design and the problems they solve.  

Let’s look at the key differences and similarities between these two AI systems. 

Key differences and similarities 

Real-world use cases of agentic AI  

Like generative AI, agentic AI is entering various industries and transforming business operations. Industries, including finance and healthcare, use agentic AI agents to optimize workflows, improve customer experiences, and drive innovation.  

Let’s consider five real-world applications of agentic AI across different sectors: 

1. AI agents in cybersecurity 

AI agents autonomously monitor and respond to security threats and identify vulnerabilities. This way, they can detect anomalies and mitigate cyberattacks before they escalate. 

Example: Darktrace employs AI agents to detect unusual network activity and neutralize cyber threats in real time, preventing potential data breaches without manual oversight. 

2. AI agents in HR and recruitment 

AI agents streamline hiring by screening resumes, scheduling interviews, and predicting candidate success based on skills and experience. 

Example: HireVue uses AI to analyze candidate responses and facial expressions during interviews, helping recruiters make data-driven hiring decisions. 

3. AI agents in finance 

AI agents in finance automate decision-making by analyzing market trends, detecting fraud, and optimizing transactions in real time. They adapt to new data patterns and improve financial strategies without human intervention. 

Example: JPMorgan Chase uses AI agents to detect suspicious transactions by analyzing spending behaviors. These agents flag potential fraud in milliseconds, reducing false positives and improving security. 

4. AI agents in supply chain management 

AI agents enhance logistics by predicting disruptions, optimizing delivery routes, and managing inventory efficiently to reduce costs and improve fulfillment times. 

Example: Amazon’s AI-driven supply chain system reroutes shipments based on weather conditions and traffic patterns, ensuring faster deliveries with minimal delays. 

5. AI agents in marketing 

AI agents enhance marketing strategies by analyzing consumer behavior, personalizing campaigns, and optimizing real-time ad spend. Based on performance data, they can autonomously prioritize best-performing content, thereby improving engagement.  

Example: Coca-Cola uses AI to analyze social media trends and customer sentiment. This helps the brand create hyper-personalized marketing campaigns that resonate with audiences in different regions. 

Challenges and risks of agentic AI 

Agentic AI has immense potential yet comes with significant challenges. For businesses intending to embrace it, it is as essential to know the risks as it is to understand the benefits. Addressing these issues early may make AI systems safe and aligned with human values. 

Here are some challenges to consider: 

Ethical concerns 

Since AI agents make judgments, they may inadvertently perpetuate biases or act unexpectedly. If not handled appropriately, these actions may result in unfair consequences such as discriminatory hiring decisions or unjust legal outcomes.  

Solution: Regularly audit AI models for bias through adversarial testing and validation. Establish ethical development guidelines, including diverse training datasets and fairness constraints. Ensure human oversight in critical decision-making and deploy continuous monitoring to detect and correct biases over time. 

Security and privacy risks 

Agentic AI connects with multiple systems and handles sensitive data, making it a potential target for cyber threats. Bad actors could manipulate AI outputs or gain unauthorized access if security measures aren’t strong enough. 

Solution: Use strong encryption and strict access controls and implement LLM guardrails to ensure the agent’s robustness against malicious inputs. 

Balancing AI and human collaboration 

AI is designed to augment human decision-making processes, not to replace them. Teams may either overly rely on AI or struggle to integrate agents successfully if their roles aren’t clearly defined. 

Solution: Establish clear AI usage policies, train employees on AI collaboration, and ensure that final decisions remain in human hands where necessary. 

Risk of misuse 

Like any technology, agentic AI can be exploited for harmful purposes. This may include generating misinformation, automating cyberattacks, or bypassing security systems. 

Solution: Implement strict guidelines for AI usage and actively monitor for misuse. Also, establish accountability measures for AI-driven actions such as maintaining audit logs and ensuring human oversight in high-risk applications. Develop incident response plans to address AI-related misuse. 

Transparency and trust 

People need to understand how AI makes decisions. If AI reasoning is a black box, businesses may face trust issues, compliance risks, or difficulty explaining outcomes. 

Solution: Prioritize explainability by implementing model interpretability techniques such as SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations). Maintain detailed audit logs of AI decision-making processes and enforce role-based access controls to protect sensitive data. Use AI security frameworks to ensure transparency and compliance. 

Emergent behavior 

As agentic AI systems interact with their environments and other AI agents, they may develop unexpected behaviors that were not explicitly programmed. These emergent behaviors can lead to inefficiencies, unintended decision-making patterns, or even harmful actions if left unchecked. 

Solution: Monitor AI interactions, establish fail-safes, and conduct rigorous testing in varied scenarios to detect and mitigate unintended behaviors. 

Best practices for implementing and adopting agentic AI 

How can you ensure effective agentic AI implementation in your organization? These intelligent systems can transform your operations, but a thoughtful and strategic approach is key to successful adoption.  

Here are some best practices to help you get started: 

1. Define clear objectives 

Before integrating agentic AI, identify the specific problems you want to solve and define measurable criteria. Whether automating repetitive tasks or improving decision-making, having a clear goal ensures a focused and effective implementation. 

Example: A bank wants to speed up fraud detection. They aim to cut manual review time while keeping accuracy high using agentic AI. Such clear goals help them track progress and adjust as needed. 

2. Choose the right tools and technologies 

Not all AI platforms are the same. Selecting the right framework, AI models, and integration tools is crucial for success. Consider factors like scalability, compatibility with existing systems, and security features. Open-source AI frameworks might offer flexibility, while enterprise-grade solutions often provide better support and compliance features.  

Example: A retail company using AI for demand forecasting may opt for an enterprise-grade AI solution with built-in compliance and customer support to ensure accurate inventory management. On the other hand, a pharmaceutical company working on drug discovery might prioritize tools with strong data processing and regulatory compliance.  

3. Start small and scale gradually 

Jumping into full-scale AI deployment can be risky. Instead, begin with a small, controlled pilot project that allows you to test AI agents in the real world. Use this phase to identify potential challenges, refine workflows, and assess AI performance before rolling it out on a larger scale. A phased approach reduces risk and makes it easier to adapt as you learn. 

Example: A logistics company implementing agentic AI for route optimization might first test it on a single delivery hub before expanding it across multiple regions. 

4. Ensure transparency and explainability 

Agentic AI should not be a black box—businesses need to understand how AI makes decisions. Choose models that provide clear reasoning for their outputs and maintain audit trails to build trust and accountability. 

Example: An AI-powered diagnostic tool in healthcare should not only predict disease risks but also explain them based on patient history, lab results, and clinical guidelines to help doctors make informed decisions. 

5. Prioritize security and compliance 

Since agentic AI interacts with sensitive data, strong security measures are essential. Implement encryption, access controls, and compliance checks to safeguard information and ensure responsible AI use. 

Example: A bank using AI for fraud detection must ensure compliance with financial regulations like GDPR or PCI DSS.  

6. Maintain human oversight 

AI should support human decision-making, not replace it entirely. Keep humans in the loop and establish review processes. You can also set up guardrails to ensure AI aligns with business values and ethical standards. 

Example: AI can screen job applications and rank candidates based on qualifications, but final hiring decisions should still involve human recruiters to account for cultural fit and soft skills. 

7. Continuously monitor and improve 

Agentic AI isn’t a set-it-and-forget-it solution. To keep your agents effective and secure, you will need to update and monitor them regularly. Monitor performance metrics, collect user feedback, and continuously adjust the system for observed results. Agentic AI systems should mature with your requirements, accommodated by fresh data and new challenges over the years. 

Example: Regularly update AI-powered customer support systems based on user interactions, sentiment analysis, and new product launches to keep responses relevant and effective. 

The future of agentic AI 

Agentic AI is quickly evolving, and its impact on businesses and industries is only the beginning. Inherently capable of independent action, it learns by interaction and can cooperate with other systems. Agentic AI has far-reaching consequences in changing how work is done—thus delivering more productive processes, better decision-making, and even new job descriptions. 

The rise of agentic AI 

In 2024, investors valued the market for agentic AI at $5.1 billion, and projections indicate it will exceed $47 billion within the next few years, growing at a staggering 44 percent annual rate (Capgemini). This growth highlights the increasing reliance on AI agents to drive automation and decision-making.  

Gartner predicts that by 2028, 15 percent of daily work decisions will be made autonomously by agentic AI, up from virtually almost none today. 

This shift shows how AI is moving beyond simple, rule-based systems into more advanced, multimodal AI agents working on the principles of agentic AI. These AI systems can process and combine different data types, like text, images, and audio, to enhance their reasoning, decision-making, and autonomous task execution.  

Here are some key agentic AI trends to watch in 2025:  

• Changing the workplace: Agentic AI will handle more repetitive tasks, allowing employees to focus on strategy, problem-solving, and creative work. This shift will require businesses to rethink how humans and AI work together. 

• AI as collaborative team members: AI agents will act more like team members, rather than just tools. They’ll participate in meetings and suggest strategies based on ongoing data analysis. Imagine an AI agent that tracks project progress and identifies roadblocks, proposing solutions before they become issues. 

• Stronger AI regulations: As AI takes greater responsibility, businesses will require improved governance and compliance mechanisms to ensure openness and fairness in AI-driven decisions. Regulatory agencies are already developing AI compliance guidelines to ensure explainability and security in AI-powered operations. 

• Smarter decision-making: AI agents will get even better at reasoning through complex circumstances, assessing many aspects, and even considering ethical issues before making decisions. This will be especially significant in the healthcare, banking, and law sectors. 

Make your data work smarter with Domo.AI 

Agentic AI is changing how businesses interact with data, moving from task-based automation to intelligent, goal-oriented decision-making. It can reason, learn, and adapt to deliver real business impact. 

Domo.AI takes this transformation to the next level. Unlike traditional AI tools, Domo’s AI agents don’t just analyze data—they understand your business. Built on a foundation of deep data integration, real-time collaboration, and enterprise-grade security, these agents work alongside your teams to drive smarter decisions and automate complex processes. 

What makes Domo.AI different? 

With Domo’s tools for building agentic AI, you can:

• Understand your business context and metrics through an advanced knowledge graph 
• Collaborate seamlessly with humans and other AI agents for real-time insights
• Operate within secure enterprise guardrails, ensuring governance and compliance 
• Adapt workflows dynamically, moving beyond rigid automation 

AI in business isn’t about insights—it’s about action. And Domo delivers AI that works for you. 

Ready to see agentic AI in action? Explore the full potential of Domo.AI, and learn how agentic workflows can transform your business.