What is Agentic AI and How Can it Help Enterprises

Customer service is really a balancing act: speed versus personalization, efficiency versus empathy. But what if you could have it all – speed, personalization, empathy - everything? This is the new standard set by agentic AI. We know customer demands are growing more intricate and to cater to them, you need solutions that go beyond automation — technology that understands, adapts and acts with purpose.

This isn’t automation as we know it. Agentic AI brings a level of sophistication to AI agents that take charge, solve intricate problems, learn from every interaction and adapt in real time. Let’s dive a little deeper and see why it can help you raise the bar on your customer experiences.

What is Agentic AI?

Agentic AI refers to an advanced artificial intelligence system designed to operate autonomously, making independent decisions and taking actions without requiring constant human oversight. What sets it apart from traditional AI is this possession of agency to solve high-cognition problems and interact with external environments (tools, data, humans) beyond the limitations of its initial training data.

Agentic AI uses advanced technologies like machine learning, large language models and reinforcement learning to help companies develop, manage and orchestrate multiple AI agents within a unified platform. It allows you to leverage this multi-agent generative system to seamlessly coordinate their collective capabilities to perform specific tasks — like booking flights or handling end-to-end complex customer service scenarios.

For a contact center, the true power of agentic AI lies in its ability to scale customer service automation while preserving — and often elevating — customer experiences, breaking free from the long-standing trade-off between efficiency and quality.

Of course, Agentic AI is far reaching and goes much beyond customer service. In this piece, we will limit ourselves to discussing AI Agents as they pertain to the customer service space. Here are the key characteristics of agentic AI in a bit more detail.

• Autonomous decision-making

AI agents can operate independently, performing tasks without human oversight. They evaluate scenarios in real time, consider various options and select the best course of action based on predefined objectives and contextual data. This eliminates the need for constant supervision. For instance, an Agent can identify a customer’s problem — such as a delayed order — analyze the context and initiate corrective actions like re-routing the shipment or issuing refunds.

• Natural language understanding

Agentic AI goes beyond basic conversational capabilities by comprehending complex, layered instructions and executing corresponding tasks autonomously. For example, a customer might say, “I need to reschedule my delivery to next week and update my address for future shipments.” An agentic AI framework is not only able to understand the multi-part request but also takes action: accessing the delivery schedule, confirming the change, updating the customer profile and notifying relevant systems.

Unlike traditional AI, which might require step-by-step guidance, agentic AI technologies can enable autonomous end-to-end orchestration of such tasks, ensuring that the interaction is fluid, intuitive and entirely hands-off for the customer.

• Advanced reasoning and adaptive planning

Agentic AI can analyze complex situations, reason through multiple variables and adjust its strategies as circumstances change. For example, when a customer contacts support with an issue involving multiple products or services, Agentic AI can process the details, identify dependencies and offer a cohesive solution. It can adjust the approach dynamically, such as shifting priorities if a new, urgent query arises mid-process.

• Self-learning and continuous adaptation

Agentic AI frameworks need historical data to implement reinforcement learning. Analyzing past interactions and customer feedback, allows the AI Agents to learn and adapt in order to deliver more effective responses and actions. For example, after encountering repeated issues with a product, an Agent can proactively suggest better troubleshooting steps or identify patterns for escalation. Over time, Agents can evolve to handle emerging scenarios, making it more effective at resolving high-cognition customer concerns.

• Multitasking and workflow coordination

Agentic AI frameworks can support complex multi-step customer service tasks simultaneously by orchestrating complex workflows that require interdependent actions.

Imagine a customer contacts support to replace a defective smart device. The AI simultaneously verifies the purchase details, checks warranty eligibility, retrieves replacement inventory from the system and calculates shipping timelines. While processing these, it also updates the customer’s account with tracking details and sends confirmation via email and SMS. If inventory is low, it integrates with supply chain systems to prioritize the request. By autonomously handling multiple facets of customer case management in parallel, agentic AI can deliver experiences so seamless, that it feels as though the resolution anticipated the request itself.

• Actionable intelligence and seamless Integration

Agentic AI can also be used to autonomously transform insights into real-world actions with minimal latency. In the context of CX, this means identifying a potential issue, such as a recurring billing error and resolving it before the customer even reports it.

For instance, AI can detect anomalies in payment processing, cancel incorrect charges and initiate refunds automatically while updating the customer about the correction. Its ability to integrate deeply with contact center CRM, ERP and customer engagement systems ensures that these actions are synchronized across platforms, creating a unified process without human intervention.

How does Agentic AI work in customer service?

To understand the spectrum within which agentic AI functions, we need to first understand the role of, as Gartner puts it, master agents and subagents within the broader context of large language models and multi-agent orchestrator systems.

👉 Subagents: These agents are highly focused performers, designed to excel at specific tasks like inventory management, billing or sentiment analysis. Specialized or subagents can function within defined parameters to bring precision and speed to their roles.

👉 Master agents: Acting as conductors, master or manager agent can harmonize the subagents. They ensure all tasks — no matter how complex or interdependent — are executed cohesively, seamlessly integrating inputs and outputs to achieve the desired outcome.

What makes agentic AI unique is its ability to facilitate end-to-end automation and decision-making across workflows. It creates an interconnected system that is the driving force behind the following use cases, where agentic AI shows its capacity to handle complexity.

Take a look at how this system works in tandem to execute multiple tasks within a cohesive system:

Source

🛍️ Use case 1: Proactive customer issue resolution across retail channels

Agentic AI can enable retail brands to move from reactive customer service to proactive problem-solving. Imagine a scenario where a customer’s online order is delayed due to supply chain disruptions. Instead of waiting for the customer to report the issue, a master agent can detect the delay by integrating real-time data from inventory systems, shipping partners and demand forecasts.

The AI can autonomously initiate a workflow: notifying the customer about the delay, offering alternatives such as expedited shipping for another product or a refund and coordinating inventory adjustments to prevent future delays. Specialized agents can collaborate invisibly in the background — one managing inventory data, another handling logistics timelines and a third reviewing the customer’s account for loyalty benefits to tailor compensation offers.

Traditional AI systems lack this orchestration capability, as they often require manual input for such multi-faceted interventions. With agentic AI, the resolution process can be seamless, fast and deeply personalized, turning what could have been a customer service failure into an opportunity to build trust and delight.

🏦 Use case 2: Dynamic contact center orchestration in BFSI

In the financial sector, where accuracy, privacy and responsiveness are critical, agentic AI can transform contact center operations by enabling dynamic orchestration. Consider a customer disputing a credit card transaction flagged as fraudulent. Instead of routing the issue through multiple layers of human agents, agentic AI can orchestrate specialized agents to manage the process.

One AI agent can verify transaction histories and identifies fraudulent patterns, while another can initiate the dispute process with the bank’s back-office systems. Simultaneously, a compliance agent can ensure regulatory requirements are met, while a customer communication agent can draft personalized updates about the case status. Agentic AI can tie these tasks together, providing the customer with clear, real-time updates via their preferred customer service channel.

🛎️ Use case 3: Hyper-personalized support in hospitality

The hospitality industry thrives on personalized guest experiences. Agentic AI can strip away the generic, replacing it with interactions that feel crafted with intent. Imagine a frequent traveler booking a hotel stay. Before their arrival, agentic AI agents can already be at work: one analyzes their previous stays to anticipate preferences, such as room temperature, pillow type or dining choices, while another integrates with local services to recommend tailored activities based on the traveler’s profile.

If the guest requests a last-minute change — like a room upgrade or late checkout — a master agent can coordinate specialized agents to check availability, update booking systems and adjust billing seamlessly, all without staff intervention. The customer receives real-time confirmations, ensuring their needs are met without delays.

🚗 Use case 4: Predictive maintenance and preemptive support in automotive

Agentic AI frameworks can take customer experience in the automotive industry from reactive to predictive maintenance. Imagine a customer’s vehicle sending diagnostic data to the manufacturer’s systems. Agentic AI can detect signs of a potential issue, such as declining battery health or engine anomalies, before the problem causes a breakdown.

A specialized agent can be triggered to contact the customer, recommend nearby service centers, schedule appointments and ensure parts are pre-ordered for repair. If the customer opts for a remote service, the AI can coordinate with mobile repair units to execute the fix at the customer’s convenience.

This proactive approach minimizes downtime and deepens customer trust by preventing disruptions. Traditional AI could flag potential issues, but it cannot autonomously coordinate the end-to-end resolution in a way that is scalable across use cases. Traditional AI can also not reason and think through the steps it needs to take to achieve an outcome or goal. Agentic AI can ensure customers feel supported and confident in their vehicle’s reliability.

🛒 Use case 5: Cross-border customer collaboration in e-commerce

In global e-commerce, handling cross-border transactions and support involves unique complexities like language barriers, shipping logistics and varying regulations. Agentic AI has the potential to overcome these challenges by enabling seamless cross-border customer interactions.

Take the example of a customer in Japan seeking to return a product ordered from a U.S. retailer. The Agentic framework can mobilize language-specific agents to craft messages in the customer’s native language, coordinates with logistics agents to arrange return shipping and consult compliance agents to ensure tax and duty refunds align with local regulations.

5 benefits of Agentic AI for enterprises

The benefits agentic AI yields are owed to its functional autonomy. Let’s quick run through five of such top reasons why you should use agentic AI for your enterprise.

• End-to-end automation of complex processes

Agentic AI can manage entire workflows independently. From identifying an issue to resolving it across multiple systems, it ensures a smooth, cohesive process without requiring manual intervention.

• Collaboration across multiple agents

Agentic AI can enable specialized agents to work in concert, coordinated by a manager agent. This multi-agent system efficiently handles complex, interconnected tasks, reducing friction for both customers and businesses.

• Dynamic adaptation in real-time

Unlike traditional AI, agentic AI can adjust to evolving scenarios autonomously. Whether it’s a shift in priorities or unexpected variables, the system has the ability to reason through and drive the most optimal outcome. For example, agentic AI can adapt on the fly to, for example, reroute logistics during delays or adjust workflows for service requests, ensuring seamless continuity through the entire lifecycle of interactions.

• Proactive issue resolution

Agentic AI has the potential to detect potential problems before they affect customers. It can adjust delivery timelines, resolve payment discrepancies or offer alternatives without waiting for a complaint, reducing customer frustration.

• Highly personalized experiences

Agentic AI can draw on historical data, real-time inputs and advanced orchestration to deliver deeply tailored experiences. More than just recommend, it can adjust dynamically, making every interaction uniquely relevant to the customer’s context.

How to implement Agentic AI in businesses (+Challenges)

The promise of agentic AI is immense, but implementing it requires a thoughtful approach. Organizations need to balance ambition with practicality while addressing the challenges that come with integrating advanced AI systems into their operations.

1. Codify business processes and knowledge

Agentic AI thrives on well-defined workflows and subject matter expertise. Before you think of implementing one, document your processes, from customer service and call center scripts to escalation protocols, in a structured, codified format. This not only enables effective contact center training but also ensures consistency in AI decision-making.

2. Organize IT and data infrastructure

Smooth integration requires robust IT systems. As an enterprise, you need to ensure data accessibility, streamline APIs and build flexible architectures that can allow agentic systems to interface with your present tools without causing disruptions. Centralizing feedback loops will also be critical for steady learning and adaptation.

3. Establish human-in-the-loop mechanisms

Autonomy must be balanced with oversight. Human experts should validate agent outputs for fairness, accuracy and contact center compliance, especially in customer-facing tasks. These control mechanisms are essential to mitigate risks like bias or incorrect decisions, ensuring AI agents operate within ethical and regulatory boundaries.

4. Re-evaluate agent roles and metrics

When agentic AI can handle routine tasks, human agents focus on high-stakes cases that need deeper expertise and judgment. This shift means common customer experience metrics like the number of cases resolved or average handling time no longer reflect their true impact. Businesses need to redefine success by evaluating how well agents manage intricate issues. Metrics should emphasize quality over quantity, spotlighting their ability to solve problems and deliver value in ways AI cannot.

5. Adopt change management practices

Adopting agentic AI can understandably create anxiety among employees, who may worry about their roles becoming obsolete. To ease these concerns, you may want to position AI as a partner, not a replacement. Emphasize how it takes over repetitive tasks, allowing your talent to focus on more purposeful work that AI cannot replicate.

Open communication is key: explain how AI fits into the bigger picture and creates opportunities for skill development and growth. Provide training programs to help employees adapt to the change, stimulating confidence and ensuring they see AI as an enabler, not a threat.

Challenges and risks

• Bias in decision-making

Agentic AI has the power to completely reshape CX, but it also carries the risk of amplifying hidden biases embedded in training data or workflows. These biases can lead to unfair outcomes — such as unequal resolutions for similar customer issues — undermining trust in a domain where fairness and consistency are non-negotiable.

To mitigate this, you should approach data with a critical lens, ensuring it reflects diverse, balanced perspectives. Regular audits of AI decisions are essential, not just as a checkpoint but as an exercise to improve it over time. More than a technical responsibility, tackling bias is a commitment to equity, accountability and the integrity of customer relationships.

• Regulatory and compliance complexity

In regulated industries, agentic AI must navigate stringent legal and compliance requirements to avoid costly fines or reputational harm.

Missteps — such as mishandling sensitive customer data or violating industry-specific protocols — can undermine trust and lead to significant setbacks. To ensure compliance, you will need to implement clear guardrails, such as audit trails and predefined decision boundaries. You might also want to look into consulting with legal and regulatory experts to align AI processes with applicable laws and standards...or choose an agentic AI partner that boasts of a powerful Trust Center.

• Productivity leakage

Saving time with agentic AI may not always lead to tangible results if the extra capacity isn’t used effectively. When agentic AI takes over routine tasks, metrics like average resolution time improve, but the freed-up capacity can lead to idle moments if not purposefully redirected. Agents, now handling fewer but more advanced cases, might face gaps in focus or efficiency, leading to productivity leakage.

To counter this, you could redefine how time is utilized. Establish clear goals, such as dedicating extra time to thorough case reviews, collaborative troubleshooting or creating in-depth knowledge bases to aid future resolutions.

Why choose Sprinklr as your Agentic AI partner for CX?

Sprinklr offers a hybrid approach to agentic AI, combining hard-coded workflows with generative capabilities for flexibility where it matters most. Whether it’s autonomous agents tackling complex problems or copilots assisting human agents, Sprinklr brings balance to your service operations.

• Unified AI, built from the ground up: With a decade of industry exposure, Sprinklr’s Unified AI operates as a central intelligence to train AI models that make implementation of your specific use-cases easy.
• Hybrid workflow flexibility: Some workflows need precision, and others need adaptability. Sprinklr balances hard-coded, rule-based tasks (like password resets) with generative AI for dynamic, nuanced cases.
• Conversational AI done right: We support both menu-driven and generative flows, so your processes remain compliant where necessary but flexible where it counts.