Understanding Agentic AI: The Future of Autonomous Customer Service

What is Agentic AI?

Gartner¹ predicts Agentic AI will autonomously resolve 80% of common customer service issues without human Intervention by 2029. Despite AI’s rise, 89% of customers emphasize the need to combine human connection with AI efficiency to optimize experiences.

Agentic AI, also known as autonomous AI, is a form of artificial intelligence that operates independently to design, execute, and optimize workflows. This empowers enterprises to make decisions and complete tasks more effectively. These AI agents can plan, adapt, and achieve predefined goals without human intervention.

Agentic AI leverages advanced reasoning and iterative planning to solve complex, multi-step problems autonomously. In contrast, AI chatbots typically use Generative AI to respond to single queries, processing one interaction at a time using Natural Language Processing (NLP).

The next frontier in AI is Agentic AI, poised to enhance productivity and operational efficiency across industries. These systems can process vast amounts of data from various sources and third-party applications, independently analyzing challenges, developing strategies, and executing tasks. Businesses are adopting Agentic AI to personalize customer service, streamline software development, and facilitate patient interactions.

For example, an AI agent in customer service could go beyond answering questions. It could check a user’s outstanding balance, recommend ways to pay it off, and complete the transaction after user approval.

How does Agentic AI work?

Define your objectives, and Agentic AI takes action. If a suitable workflow exists, the agent executes it; otherwise, agents may design their own workflows on the fly, always consulting with humans when needed. If circumstances change, AI agents adapt their strategies to achieve optimal results. Agentic AI proactively anticipates needs, predicts outcomes, and responds to opportunities.

Agentic AI follows a four-step process for problem-solving:

• Perceive: Agents gather and process data from various sources—sensors, databases, and digital interfaces—extracting features, recognizing objects, or identifying relevant entities in the environment
• Reason: A large language model (LLM) orchestrates tasks, generates solutions, and coordinates specialized models for functions like content creation, visual processing, or recommendations. Techniques such as retrieval-augmented generation (RAG) enable access to proprietary data for accurate, relevant outputs
• Act: Agentic AI swiftly executes its plans by connecting with external tools and software via APIs. Built-in guardrails ensure tasks are performed correctly; for instance, a customer service AI may handle claims up to a set amount, with larger claims requiring human approval
• Learn: Agentic AI continuously improves through a feedback loop (data flywheel), feeding interaction-generated data into the system to enhance models. This adaptive capability helps businesses drive better decision-making and operational efficiency

Capabilities of Agentic AI

• Workflow Design: Use AI agents to implement best practices and rapidly create new workflows
• AI-Powered Decisioning: Automate decision-making with context-rich data to determine the next best action or enable straight-through processing
• Adaptability: Enhance business agility as Agentic AI adapts to change and predicts future scenarios, ensuring optimal outcomes
• Workflow Execution: AI agents identify and execute the appropriate workflows to resolve user requests automatically
• Workflow Optimization: Leverage AI agents to optimize and execute complex, multi-step workflows

Spot the Difference: Generative AI Vs. Agentic AI

Artificial intelligence (AI) has become a buzzword in today’s world. The term often brings to mind robots and self-learning machines, but in reality, AI encompasses a wide range of subfields. Two of the most discussed advancements are Generative AI and Agentic AI. It is crucial to understand that they operate in fundamentally different ways. Grasping these distinctions is essential for understanding how AI is shaping and will continue to shape our world.

Generative AI

Generative AI is focused on creation—think of it as the inventive branch of artificial intelligence. These systems are designed to produce a wide range of content, including text, images, music, code, and even videos. By learning from existing data, generative AI generates original outputs that emulate human creativity. The advent of tools like ChatGPT, DALL·E, and MidJourney has brought Generative AI into the mainstream. These platforms rely on advanced machine learning models, especially neural networks, to analyse patterns from their training data and replicate them.

However, Generative AI is not without flaws. Its outputs are constrained by the quality of its training data: if the data is biased or incomplete, the results will reflect those limitations. Moreover, Generative AI does not truly “understand” the content it creates; it predicts subsequent elements based on recognized patterns. Despite these drawbacks, Generative AI is rapidly transforming industries ranging from marketing to entertainment.

Agentic AI

Agentic AI systems do more than generate outputs—they make decisions, take action, and adapt to changing environments. Operating as intelligent agents, they make real-time decisions, dynamically adapt, and integrate into various systems or workflows. Agentic AI powers technologies such as autonomous vehicles, robotic process automation, and smart assistants capable of scheduling meetings or ordering groceries. These systems use sensors, algorithms, and actuators to perceive their environment, process information, and execute actions.

What sets Agentic AI apart is its purposeful decision-making. Instead of merely responding to inputs, it acts based on objectives, choosing strategies to achieve its goals. For instance, an autonomous drone making deliveries must avoid obstacles, optimize its route, and adapt to unexpected events—all without human intervention.

However, granting these systems autonomy raises important ethical and accountability concerns. Who is responsible if an autonomous system makes an error? How do we ensure such systems align with human values? Addressing these challenges is crucial as Agentic AI becomes more widespread.

Summarizing the Core Differences

The simplest way to distinguish Generative AI from Agentic AI is to consider their main functions. Generative AI focuses on creating new content, whereas Agentic AI is designed to accomplish specific goals. In essence, one generates, while the other acts.

Generative AI is generally static; it produces outputs based on previously learned data but does not interact with or adapt to its environment in real time. Its activities are confined within preset boundaries. Agentic AI, by contrast, is dynamic: it continually processes new information, learns from its surroundings, and adjusts its actions to achieve objectives.

Another important difference is in the complexity of their tasks. Generative AI is best suited for narrow, well-defined problems—such as writing a paragraph or creating digital art. Agentic AI typically handles more complex, multi-step tasks that require ongoing decision-making and adaptation.

Benefits of Agentic AI

• Increased efficiency: By automating workflows and enabling real-time decision-making, AI agents support straight-through processing, transforming your enterprise into an operations powerhouse
• Enhanced self-service: When interacting with a chatbot, Agentic AI understands the customer’s intent and guides them to resolve issues or workflows on their own, without needing escalation to a live agent
• Maximized employee performance: AI agents can assess complex situations and provide employees with step-by-step guidance based on your organization’s policies and best practices
• Better and faster workflow design: Agentic AI, such as Pega Blueprint™, recommends workflow designs that follow industry best practices—accelerating speed to market and serving as a creative partner for developers
• Hyper-personalized engagement: By analyzing vast data, AI agents understand customer needs and determine the best course of action in real time, delivering highly personalized experiences

Challenges and Considerations of Agentic AI

• Cost of Implementation: Integrating Agentic AI can be expensive, especially for mid-sized businesses. Costs may include upgrading infrastructure, licensing software, and providing training for staff. Careful planning and phased implementation can help manage these expenses. And over time, the long-term ROI from time and efficiency savings may balance out the upfront costs. 
• Implementation Barriers: Adopting Agentic AI requires investment in technology and integration. You may need to upgrade legacy systems and provide training to ensure agents can use the tools effectively
• Training and Adoption: Agents might initially struggle with adapting to this new way of working. Proper onboarding and ongoing support are vital to building trust in the technology and maximizing its benefits
• Ethical and Transparency Concerns: These are a major issue in AI deployment. People are especially worried about bias in AI models, and their personal privacy. So, ensure you are transparent with your customers when AI is used
• Balancing AI and Human Touch: While Agentic AI does improve efficiency, we need to keep the human touch. This builds trust and rapport. Striking the right balance ensures customers feel valued and heard, not just processed

What are some Use Cases for Agentic AI?

Customer Service

By 2028, 68% of all customer service and support interactions with technology vendors are expected to be handled by Agentic AI

In self-service situations, AI agents can understand customer intent and determine the right workflow to guide customers in resolving their inquiries. Agentic AI can also assist customer service reps with their work by suggesting actions and automating tasks and processes. AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction. There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation

Agentic AI enables rapid creation of high-quality, personalized marketing content. Generative AI agents save marketers an average of three hours per content piece, freeing them to focus on strategy and innovation. Streamlining content creation helps businesses remain competitive and boost customer engagement.

On the performance side, AI agents can scan hundreds of millions of pages, analyze metrics, flag optimization issues and fine-tune content. Previously, this could take months or even years. Teams no longer need to wait for underperformance to show up in a dashboard—now they can identify issues before taking a hit on traffic or conversions.

Healthcare

Key applications of Agentic AI in healthcare include:

• Workflow automation: Agentic AI automates tasks like staff scheduling, patient intake, documentation, and many other routine manual tasks to free up clinical teams to focus on patient care.
• Real-time decision-making support: AI Agents can support clinicians by providing data-informed choices in the moment and taking actions without human guidance. AI agents can also provide 24/7 support, offering information about prescribed medication usage, appointment scheduling and reminders, and additional resources to help patients adhere to treatment plans.
• Adaptive learning: Agentic systems can adapt to changing circumstances and continuously improve their performance by interacting with APIs, digital tools, and evolving data sets in real-world healthcare environments. Agentic AI brings significant value to healthcare by analyzing patient health data for proactive care, scheduling appointments, and guiding both patients and providers through the complexities of healthcare journeys.

Software Engineering

AI agents are enhancing developer productivity by automating repetitive coding tasks. Projections suggest that by 2030, AI could automate up to 30% of work hours, enabling developers to focus on more complex challenges and drive innovation. With growing IT complexity, 92% of organizations say the support and services they receive are more important than ever.

Video Analytics

Enterprises and public sector organizations around the world are developing Video Analytics AI Agent to boost the capabilities of workforce that rely on visual information from a growing number of devices — including cameras, IoT sensors and vehicles. Video analytics AI agents can analyze large amounts of live or archived videos, request tasks via natural language and perform complex operations like video search, summarization and visual question-answering. These agents can also be used to deliver anomaly alerts, draft incident reports, improve quality control through visual inspection and enhance predictive maintenance. 

Unlike conventional rule-based systems, Agentic AI video analytics adapts dynamically to context, learns from ongoing interactions, and collaborates across enterprise systems. The ability to process video streams in real time with semantic accuracy not only enhances situational awareness but also improves predictive capabilities—driving faster, more informed business outcomes.

Financial Services

Imagine a trading AI agent that analyzes market data and autonomously monitors market trends, deciphers trading signals, adjusts strategies and mitigates risks in real time. Agentic AI will make this increasingly possible, integrating tools via APIs, sensors and advanced reasoning, to respond to new information and data, and automate and enhance financial tasks beyond Gen AI capabilities. Agentic AI’s increased autonomy, relative to Gen AI, enables it to handle repetitive, data-intensive processes. This gives financial institutions and market participants an increased capability to optimize workflows, enhance compliance and improve decision-making, transforming how financial services conduct business and interact with customers in the age of Agentic AI.

Conclusion: The Future of Agentic AI

Agentic AI holds tremendous potential to transform enterprises. Because it operates autonomously, companies will need frameworks that support autonomous workflows, provide seamless data integration, and continually optimize processes. This new technology will drive enterprises to undergo broader transformations to fully harness Agentic AI’s capabilities.

Agentic AI is still in its early stages. However, it has the potential to really transform customer service and other aligned avenues. Here’s a glimpse into what the future might hold:

• Hyper-Personalization: As Agentic AI matures, its ability to analyze and predict customer behaviour might improve, though accuracy may vary depending on the data and context. This may lead to hyper-personalized customer experiences, where interactions are tailored not just to past behaviour but to real-time needs and preferences
• Enhanced Multi-Channel Support: Agentic AI appears ready to make omnichannel customer service truly consistent. Whether a customer reaches out via messaging, chat, email, voice, or video, the AI could provide consistent, informed support and ensure smooth transitions between channels

The distinction between Generative and Agentic AI will blur as both evolve. Advancements may yield systems that merge creation and action, offering the best of both worlds. Imagine an AI that not only generates innovative ideas but also implements them autonomously—a true game-changer across industries like healthcare and manufacturing.

However, this potential comes with responsibility. These technologies must be developed and deployed ethically, with transparency and accountability. Understanding the differences between Generative and Agentic AI will help us appreciate their strengths and guide us toward a future where AI meaningfully benefits humanity.

References

1. https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290 ↩︎