Agentic AI

Question 1

What is agentic AI?

Accepted Answer

Agentic AI refers to artificial intelligence systems that don’t just react or follow preset rules—they act with autonomy, initiative, and adaptability to pursue goals. This form of AI is capable of independently making decisions and taking actions to fulfill objectives in dynamic environments. Agentic AI is an AI system that combines multiple types of artificial intelligence that, together, make it capable of planning, acting, learning, and improving. Agentic AI systems can: Make decisions based on context and changing conditions Break down goals into sub-tasks and pursue them independently Collaborate with tools and other AI systems to get results Reflect and adapt over time to get better results These new AI capabilities open up vast new applications for AI across every facet of enterprise operations, and have brought AI agents into being. Agentic AI is brainpower that allows AI agents to act independently within unstructured environments—enabling enterprises to expand automation beyond specific, defined tasks and tackle complex, end-to-end processes.

Question 2

What are the benefits of agentic AI and agentic automation?

Accepted Answer

By powering next-generation AI agents to perform a wider array of complex tasks than ever before possible, agentic AI vastly expands what can be automated. For enterprises, this delivers a number of important benefits:Increased efficiency and productivity Empowered autonomous agents can now take on complex, decision-intensive tasks that were previously beyond the reach of machines. This allows people to focus their energy and expertise on strategic initiatives, creative problem-solving, and more meaningful customer relationships—the activities that can fuel business growth.Enhanced customer experiences Agentic AI revolutionizes customer interactions by providing personalized and responsive experiences at scale and speed. Leveraging sophisticated models, AI agents can infer customer intent, predict needs, and offer tailored solutions, all while operating 24/7 to ensure consistent support. Human augmentation Rather than replacing people, agentic AI systems can enhance their performance, productivity, and engagement. For example, from call centers to marketing departments and beyond, AI agents have brought consistency and higher quality to employee performance—regardless of tenure. Moreover, intelligent agents operating within autonomous systems can take on many time-consuming and complex tasks, allowing human effort to shift toward creativity, problem-solving, and nuanced decision making. In sum, strategic collaboration between AI agents and people expands enterprises’ capacity to tackle complex challenges, serve customers better, and drive efficiency across their organizations.

Question 3

What are the highest impact agentic AI use cases today?

Accepted Answer

Streamlining the insurance claims process The insurance industry is no stranger to paperwork and manual processes, but agentic AI is rewriting the rules. Insurance companies can leverage this technology to automate much more of the claims process than ever before possible. While people serve as the final approvers, AI agents can work with RPA robots to take on more of the work. For example, an AI agent can instantly assess the validity of a claim, direct robots to gather necessary information from internal and external sources, and even create and send communications and queries to customers. Along with accelerating the claims process, this reduces the administrative burden on human adjusters, allowing them to step in as final approvers while also having the time to focus on more complex cases and deliver a higher level of personalized service. Optimizing logistics and supply chain management Every minute counts in the world of logistics and supply chain management. Delays, disruptions, and inefficiencies can ripple through the entire system, costing businesses time and money. Agentic AI is emerging as a powerful tool to tackle these challenges head-on. Agentic-AI-powered software agents can analyze vast amounts of data in real-time, optimizing routes, predicting potential bottlenecks, and even adjusting inventory levels based on demand fluctuations. This dynamic optimization can help ensure that goods and services are delivered efficiently, reducing costs and improving customer satisfaction. Empowering financial decision making Agentic AI is also making waves in the financial sector, enabling AI agents to analyze market trends, assess investment opportunities, and even create personalized financial plans for individual clients. Freed from the burden of detailed, data-heavy analysis and report generation, financial advisors can now focus on building relationships and offering strategic guidance. Beyond investment advice, agentic AI is also transforming how financial institutions manage risk. AI agents can analyze vast amounts of data to surface potential risks and vulnerabilities, helping financial institutions proactively manage their exposure and ensure compliance with regulations. This proactive approach helps minimize losses while strengthening the overall resilience of the financial system.Accelerating drug discovery and development The healthcare industry is undergoing a digital transformation, and agentic AI is playing a pivotal role. For example, some healthcare providers are turning to AI agents to recommend tailored treatment plans based on individual patient data. This personalized approach to healthcare holds the promise of improved patient outcomes and a more efficient use of medical resources. Agentic AI is also accelerating drug discovery and development by equipping AI agents to rapidly analyze massive datasets, zero in on potential drug targets, and predict their efficacy. This highly expedited process is driving lower development costs while dramatically compressing development cycles. Transforming customer service and customer support Delivering exceptional customer experiences is a top priority for businesses across all industries. Agentic AI is stepping in to enhance customer support with AI agents that handle complex queries, anticipate customer needs, and resolve issues with context-awareness—creating high-quality, always-on support. Imagine a virtual assistant that not only answers your questions but also proactively offers relevant information and recommendations based on your past interactions. This hyper-personalized service builds brand loyalty by providing customers with a top-notch experience—when and where they need it. Accelerating and optimizing testing Agentic testing is revolutionizing the software testing field—augmenting human software testers with AI agents across all phases of testing. Testing agents go beyond executing scripts; because they can understand goals and plan actions, they can assist testers in quality-checking requirements, generating test cases, automating manual test cases, and providing real-time, actionable insights into test results. Autonomous AI agents can respond many unpredictable challenges that pervade modern quality assurance (QA) environments.

Question 4

What’s the difference between agentic AI and generative AI?

Accepted Answer

While both agentic AI and generative AI (GenAI) are pivotal technologies, their focuses differ. Each has its unique strengths and applications. GenAI is built to create—copy, images, code, and ideas. Its ability to support natural language processing makes it a powerful tool for content generation. Agentic AI, by contrast, is built to act. It plans, decides, and executes to reach outcomes. Where GenAI stops at creation, agentic AI continues—implementing actions, triggering workflows, and adapting to new circumstances. Together, these technologies are complementary. For example, GenAI might draft marketing content, while agentic AI launches and iterates the campaign automatically based on real-time performance data.

Question 5

What is the history of agentic AI?

Accepted Answer

In 2017, Google researchers introduced the Transformer architecture—a step-change innovation in how machines process language. Unlike earlier models, Transformers used attention mechanisms to understand context more efficiently and at scale. This set the stage for modern AI. Over the next few years, researchers built on this foundation by training Transformers on massive text datasets. The result: large language models (LLMs) that could generate text, answer questions, and even reason—just from natural language prompts. By 2022, conversational AI reached a tipping point. Fine-tuning techniques made LLMs more aligned with human intent, safer to interact with, and easier to use. AI went from being a tool people operated to a partner they could talk with. Now, we're seeing the next leap: agentic AI. By adding planning, memory, and tool use to LLMs, these systems go beyond giving answers and actually plan and take action to meet specific goals. AI agents can follow multi-step instructions, call APIs, and complete goals autonomously. This marks a turning point in automation.

Question 6

What is the science behind agentic AI?

Accepted Answer

Agentic AI combines multiple types of artificial intelligence to enable thinking, acting, and adapting: large language models (LLMs) for reasoning and communication; planning AI for task sequencing; reinforcement learning for optimizing actions; and memory systems for context retention. It also leverages tool-use models that allow it to interaction with external systems and tools. Additional AI models permit these systems to self-reflect, perceive, and benefit from continuous learning.Each of these forms of artificial intelligence plays a key role in empowering agentic systems’ capabilities. Large language models (LLMs) The AI landscape has shifted dramatically with the introduction of powerful LLMs. These models, trained on massive datasets, have given AI a newfound ability to comprehend and generate human-like text. Conversations between people and machines have become more natural and meaningful, opening up many new possibilities for AI applications. LLMs provide the foundation for natural language understanding, enabling AI agents to interpret complex instructions, engage in meaningful conversations, and even generate creative content. This enhanced language capability allows agentic AI to interact with users in a more natural and intuitive way, opening up new possibilities for collaboration and problem-solving. Moreover, LLMs have enabled agentic AI to reason and make decisions based on the information they process. Analyzing vast amounts of data and identifying patterns allows these AI agents to generate insights, make predictions, and take actions that align with their pre-defined objectives. This ability to think and act autonomously is a key characteristic of agentic AI, differentiating it from traditional automation technologies. Machine learning Advancements in machine learning algorithms, coupled with ever-increasing computational power, further accelerated this evolution. These systems could now learn from vast amounts of data, refining their abilities and becoming increasingly adaptable to new situations. This shift paved the way for more dynamic automation and AI solutions. For example, reinforcement learning, a type of machine learning where autonomous agents learn by interacting with their environment and receiving feedback, has been instrumental in enabling agentic AI's high-level decision making capabilities. Learning from trial and error enables agentic AI systems to optimize their actions to achieve specific goals, even in complex and dynamic environments.

Question 7

What technological innovations and advances are spurring the application of agentic AI throughout the enterprise?

Accepted Answer

Integrating AI tools with existing enterprise systems has been a game-changer. It’s allowed AI agents to tap into a wealth of data sources across the organization—from customer relationship management (CRM) and enterprise resource planning (ERP) systems to supply chain management and HR tools. Breaking down data silos and connecting disparate systems gives agentic AI a 360-degree view of your business, enabling it to make smarter, data-driven decisions and automate complex workflows. Advancements in cloud computing have also played a crucial role in fueling the growth of agentic AI. Cloud platforms like Microsoft Azure and Amazon Web Services (AWS) provide the scalable computing power and storage capacity needed to train and deploy complex AI models. Additionally, sophisticated data processing tools enable businesses to extract valuable insights from massive datasets in real-time, fueling the decision making capabilities of agentic AI. Furthermore, the development of agentic automation platforms like the UiPath Platform™ has accelerated enterprise adoption. For example, the UiPath Platform provides comprehensive, fully integrated capabilities for deploying AI agents, orchestrating end-to-end workflows, and enabling agentic automation at scale. In combination, these capabilities allow businesses to deploy and govern intelligent, goal-oriented agents that collaborate with robots and people to execute complex, dynamic processes with precision and trust.

Question 8

How have these advances come together in enabling the emergence of AI agents?

Accepted Answer

As discussed above, the convergence of powerful LLMs, sophisticated machine learning, and seamless enterprise integration has enabled the rise of agentic AI—which is the “brainpower” behind AI agents. Fueled by greatly enhanced analytical, reasoning, learning, and inferential skills, this new breed of AI agent can now operate with minimal human intervention to complete complex, dynamic processes that were once considered the exclusive domain of human intelligence. They can set goals and design and optimize processes for completing work. They can autonomously decide to take actions, or direct other agents to start a process. They can learn from their experiences and improve their performance over time.

Question 9

What risks and challenges come with agentic AI adoption?

Accepted Answer

The potential of agentic AI is significant, but it's important to be mindful of the potential risks that come with this transformative technology. As AI systems become more autonomous and impactful, ensuring their responsible and ethical use becomes paramount. Researchers and developers are actively working on developing frameworks and guidelines to ensure that agentic AI operates in a transparent, fair, and accountable manner. Some areas of concern:Autonomy and oversight While the autonomy of agentic AI offers numerous benefits,it's important to carefully consider the implications of granting decision making power to machines. Striking the right balance between autonomy and human oversight is crucial to prevent unintended consequences and ensure that AI-driven actions align with ethical and legal standards. Transparency and reliability Agentic AI systems raise concerns about reliability, transparency, and trust because AI agents act autonomously, without constant monitoring by people. The known phenomenon of LLM hallucinations (confident but false outputs) adds to the concern: if an AI agent generates a plan, file, or message based on fabricated data, people may not realize it’s wrong. Agentic systems’ autonomy can make those errors cascade across actions, not just outputs. This is why it is critical to add in audits, human-in-the-loop controls, and source verification capabilities into every agentic AI system. Security and privacy Agents often require access to sensitive data, raising concerns about security and privacy.  That means enforcing rigorous controls, including access management, encryption, and monitoring. There are other ways to reduce risk and enhance security. For example, an AI agent can call upon robots—which execute exactly as designed, ensuring consistent, rule-based performance with no deviations or unexpected behavior—to access sensitive or classified data.

Question 10

What are some best practices for implementing agentic AI?

Accepted Answer

Orchestration By coordinating how and when AI agents act—across systems, tasks, tools, and human handoffs—orchestration ensures accountability, reduces risk, and aligns outcomes with business goals. It also creates a foundation for governance, making it easier to monitor performance, audit decisions, and intervene when needed. In short, orchestration brings structure, control, and visibility to autonomous workflows and turns agentic AI from a promising capability into a reliable, enterprise-ready solution. Governance Establish clear governance frameworks and compliance measures that define the roles and responsibilities of all stakeholders involved in the development and deployment of agentic AI systems. This includes establishing ethical guidelines for AI use, ensuring compliance with relevant regulations, and creating mechanisms for regular monitoring and auditing. Human-in-the-loop Human-in-the-loop—where people are brought in to processes to validate and provide ultimate confirmation of key decisions—blends automation with oversight, ensuring critical decisions stay aligned with human judgment and business context. By involving people in key approvals, escalations, or quality checks, organizations can catch errors, manage edge cases, and build trust in the system. Human input also creates a feedback loop that helps AI improve over time. In sum, human-in-the-loop makes agentic AI smarter, safer, and more adaptable to real-world complexity. Security and compliance Implement robust security measures, such as encryption, access controls, and regular vulnerability assessments to protect sensitive information and maintain the integrity of agentic AI systems. Additionally, ensure compliance with data protection regulations and establish clear guidelines for data usage to mitigate privacy risks and maintain ethical standards. Testing and validation Rigorous testing and validation are essential to ensure the reliability and safety of agentic AI systems. Conduct comprehensive testing under various scenarios, including both expected and unexpected situations, to identify and address potential flaws or unintended consequences before deploying the system in a real-world environment. Continuous monitoring and improvement Agentic AI systems should be continuously updated to ensure their effectiveness and security. Regular feedback loops, performance metrics, and user feedback can help identify areas for improvement and enable the system to adapt to changing conditions. Continuous learning and improvement are key to maximizing the value and longevity of an agentic AI investment.

Question 11

What does the future look like for agentic AI, agentic automation, and AI agents?

Accepted Answer

The massive leaps forward in AI have created a seismic technology shift. Today, agentic AI enables AI agents that can learn, predict, and take action. Agents can take on undefined tasks, manage complex processes, and make nuanced decisions that until recently could only be completed by people. Agentic AI, in short, is enabling us to explore entirely new possibilities in designing work processes, expanding the role of automation as we redefine the roles of people, robots, and machines in a myriad of processes across the organization. While advances in agentic AI have already changed some of the ways that people and machines interact and collaborate, we’re just at the beginning of the revolution. There’s a massive—and inevitable—wave of work transformation on the horizon that has only just started to coalesce and gather momentum. The way that the most modern and successful companies will operate in five years—what their people do, what machines do, and the ways people, robots, and agents work together—will bear little resemblance to how they operate today. As agentic AI achieves scalability and AI use cases expand throughout every business process, enterprises everywhere will have the means to operate better, faster, and more efficiently. They’ll be differentiated by their outstanding customer engagement, nimbleness in responding to current and future changes, and their ability to attain new levels of employee productivity and engagement. The possibilities are virtually endless, and the future of agentic AI is filled with promise. As this technology evolves, it is reshaping the world of work and the roles of humans and machines in the world.

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