Agentic AI: A guide to the next wave of CX innovation

Agentic AI explained: Its role in the future of CX

For decades, artificial intelligence (AI) has been impressive, but predictable. Even the most groundbreaking systems, like Google’s AlphaGo, followed a familiar pattern: learn, optimize, execute. Once the training was done, the AI stuck to what it knew, i.e., applied pre-learned strategies from reinforcement learning and self-play.

But what happens if AI could rewrite the rules autonomously in real time? Most AI today, like OpenAI’s original ChatGPT or DALL·E, respond when prompted—it generates text, images, or code based on input.

But agentic AI is something else entirely. It doesn’t wait for commands—it pursues goals, makes decisions, and takes action with minimal human oversight. In other words, it thinks and acts on its own.

So, how does AI move from passively generating content to autonomously solving complex tasks?

What is agentic AI?

Agentic AI are systems that operate independently to achieve specific goals with minimal human oversight. While traditional AI processes data and provides insights, agentic AI takes action, i.e., it makes decisions and adapts in real time based on its objectives.

Specifically, these systems don’t simply execute pre-programmed tasks. They have contextual awareness, which lets them evaluate options, and adjust their behavior based on new information. Over time, agentic AI learns from user behavior and past interactions.

The academic roots of agentic AI

Here’s something you may not know: Agentic AI’s foundation is deeply tied to long-standing theories in psychology, philosophy, and decision science.

Below, we’ll cover two (but not all) theoretical concepts that help frame the depth behind these systems: Theory of Mind and Value Alignment.

Theory of Mind: Can AI predict intentions?

Theory of Mind (ToM) is a cognitive science concept that describes our ability to attribute beliefs, intentions, and emotions to others. It’s what allows us to anticipate how people will react in different situations.

For AI to function as a true agent, it needs some version of this ability—not in a conscious sense, but as a predictive model for understanding and responding to human behavior.

In more advanced situations, AI coordinating with other AI systems (or humans) would need to predict intentions in multi-agent environments—whether that’s in financial markets, logistics, or autonomous robotics.

That said, while today’s AI systems don’t have true ToM (yet), research in multi-agent reinforcement learning and cognitive modeling suggests that future agentic AI, especially in customer service, will need some level of intent prediction to navigate complex interactions.

Does value alignment keep AI in check?

If agentic AI operates with autonomy, the next logical concern is: How do we make sure it pursues goals that align with human values? This is the Value Alignment Problem, one of the biggest ethical challenges in AI.

Unlike traditional AI, which is typically narrow in scope, agentic AI has more freedom to make decisions—and that’s where things can get messy. That’s because misaligned incentives can have unintended (or even dangerous) behaviors.

For example, a trading algorithm that’s designed to run purely for profit might take extreme risks and ignore long-term stability. Or, a hiring AI that’s focused on efficiency could develop biases that exclude qualified candidates.

Value alignment makes sure that AI’s objectives stay in line with human ethics and interests, not just what’s mathematically optimal. For instance, generative AI already has some level of guardrails.

That said, we have some techniques (like reinforcement learning with human feedback (RLHF) and inverse reinforcement learning) that try to address this by shaping AI’s reward structures to reflect ethical constraints.

What are the differences between agentic AI and generative AI?

As we already covered, agentic and generative (gen) AI, though related, are distinct systems. Let’s look at the differences:

How agentic AI works

As we covered earlier, agentic AI reflects a shift from passively processing datasets to proactively working toward some sort of goal without significant guidance. These systems operate through a structured machine learning pipeline that allows them to perceive, decide, act, and learn.

Step 1: Building perception (understand the world)

Agentic AI starts by making sense of its environment. This means that just like humans gather information through sight, sound, and past experiences, AI systems ingest data from multiple sources, like APIs, databases, sensors, or live user input.

For example, a customer service agentic AI wouldn’t just process a single request; it would pull up order history, identify past issues, and detect sentiment in real time.

Similarly, a financial AI wouldn’t just look at today’s market prices; it would analyze global economic indicators, company earnings, and geopolitical events before making a trade.

Put simply, the more data an agentic AI can access, the better it understands the context it’s operating in—and that’s the foundation for everything that follows.

Step 2: Decision logic (choose what to do next)

Once an AI understands its environment, it has to decide what to do. And this isn’t a matter of following rules; it’s about weighing multiple possibilities and selecting the best course of action.

Some AI systems rely on reinforcement learning, so they’ll refine their systems based on past successes and failures. Others use neural networks that recognize patterns or symbolic AI that follow structured logic. While the method depends on the task, the goal is the same: evaluate options, predict outcomes, and make a decision.

Step 3: Action (execute in the real or virtual world)

Unlike generative AI, which waits for human input, agentic AI operates with intent. This means it doesn’t just react to human input or suggest actions; it takes them.

For example, a logistics agentic AI places orders before supplies run out. Similarly, a customer support agentic AI updates an account instead of passing the task to a human. Or, a robotic agentic AI adjusts factory operations in real time without waiting for an engineer’s approval.

Step 4: The feedback loop (learns and improves)

In simple terms, agentic AI isn’t static. So, it doesn’t just execute tasks and move on.

Let’s look at customer service. For instance, a standard AI system might analyze customer interactions and detect patterns, but agentic AI would close the loop by adjusting its own behavior and autonomously resolving issues.

Conversational analytics plays a key role in this evolution of feedback loops.

Contact centers generate thousands of hours of conversation data every day, but most of that information goes unused. Conversational analytics extracts meaning from call transcripts, chat logs, and sentiment analysis to highlight trends and recurring, complex problems.

For example, PolyAI’s conversational analytics allows businesses to:

• Automatically categorize and analyze customer issues (e.g., detecting an increase in billing-related complaints).
• Spot the root cause rather than surface-level frustration (e.g., customers might not be upset about pricing itself, but about how hard it is to access itemized charges online).
• Improve workflows based on structured conversation data, reducing friction in customer interactions.

As you can see, this data-driven feedback loop is a step toward agentic AI, but it’s not fully there yet. That’s mostly because conversational analytics is still primarily descriptive and helps companies understand what’s happening—but true agentic AI moves into prescriptive and autonomous territory.

The 4 types of agentic AI

Not all agentic AI operates the same way. At a high level, agentic AI falls into four major categories: reactive, deliberative, learning, and multi-agent systems.

Each represents a different stage in autonomy, adaptability, and decision-making complexity. Let’s break them down.

1. Reactive agent

Reactive agents respond instantly to changing conditions without planning ahead or storing past experiences or internal models. They operate in the present, i.e., adjust their actions based on immediate inputs. While this makes them fast and reliable, it also limits their ability to improve over time.

Think of a Roomba navigating a room. It doesn’t have a pre-programmed map of your home. However, it reacts to obstacles in real time and shifts direction when it bumps into furniture. Or a smart thermostat like Google Nest activates heating the moment the temperature drops but doesn’t analyze long-term weather patterns.

In short, a reactive AI is useful for simple tasks, but it doesn’t learn or adapt.

2. Deliberative agent

On the other hand, deliberative agents analyze, plan, and weigh different possibilities before making a decision. So, instead of reacting impulsively, they predict future outcomes and select the best course of action.

A self-driving car is a classic example because it maps the road ahead, evaluates different route options, and anticipates traffic conditions before deciding where to go. A delivery drone operates the same way, as it selects the safest or fastest route before taking off.

3. Learning agent

Learning agents (based on machine learning) evolve based on new data and past experiences. Unlike reactive agents that repeat the same behavior, learning agents continuously refine their decision-making, adjusting their responses to become more effective.

A streaming service like Netflix is a perfect example. When you start watching a show, it tracks your viewing habits, analyzes your preferences, and refines its recommendations over time.

In customer service, learning AI is critical for long-term efficiency. A voice assistant that handles thousands of calls per day should get better at recognizing customer intent, improving responses, and reducing friction in interactions.

Instead of using static decision trees, our conversational AI architecture uses Natural language processing (NLP), Natural language generation (NLG), and Natural language understanding (NLU) to analyze customer conversations, detect any patterns in queries, and refine the responses.

In due course, our agents can also recognize subtle variations in speech and adapt to new ways customers phrase requests to improve resolution accuracy.

4. Multi-agent systems

Multi-agent systems unlock new possibilities. Instead of a single AI working alone, multiple autonomous agents collaborate to solve complex tasks.

For example, think about a fleet of warehouse robots. Some robots focus on sorting products, others handle deliveries, and others manage inventory. Each robot has its own objective, but they coordinate in real time, ensuring the entire system runs efficiently.

In customer service, this concept applies when AI assistants, chatbots, and backend automation tools work together. A voice assistant might handle the initial customer inquiry, while a separate AI system retrieves account information, and another processes refunds or updates orders.

So, instead of relying on a single AI model, multi-agent systems distribute tasks dynamically and, in turn, improve response times and efficiency.

Agentic AI and AI agents: What’s the difference?

The terms AI agent and agentic AI are often used interchangeably, but they don’t mean the same thing. While all agentic AI systems can be considered AI agents, not all AI agents are truly agentic.

Think of an AI agent as a broad category, so any AI system that interacts with its environment and makes decisions qualifies here. That could be anything from a basic rule-based chatbot to an autonomous trading algorithm. But agentic AI refers specifically to systems that exhibit high degrees of autonomy, adaptability, and goal complexity.

Real-world use cases of agentic AI

Harvard Business Review has already explored some of the potential applications of agentic AI. Here’s where (and how) agentic AI can make an impact in different industries:

• Customer service: For instance, AI agents detect issues before customers complain, e.g., predict a late delivery, notify the customer, and offer a discount automatically.
• Manufacturing: Let’s say AI-powered systems monitor factory machinery, predict failures, and adjust production schedules to prevent downtime (like Juna.ai).
• Sales support: For example, the AI automates lead qualification and follow-ups, books meetings, and answers common questions so sales reps can focus on closing deals.
• Healthcare: AI assistants could act as virtual caregivers to remind patients to take their medication, handle pre-op questions, and triage hospital requests (like Hippocratic AI).
• Supply chain management: Let’s say the AI can reroute shipments, adjust inventory, and prevent delays by responding to real-time disruptions.

And these examples only scratch the surface of what agentic AI can do.

What are the benefits of agentic AI?

AI has already changed the way we work. And as agentic technology develops, it will not only increase the efficiency of individual tasks but also redefine broader technology in ways we can’t yet fully imagine.

Here are some potential benefits:

• More reliable outputs: Instead of pulling from static training data, agentic AI searches, refines, and verifies information in real time, leading to more accurate and up-to-date results.
• Less manual effort: Instead of waiting for step-by-step instructions, AI can take a broad objective, execute multiple steps autonomously, and refine its method along the way.
• Scalability: A single AI system can handle tasks that would normally require multiple human workers, whether that’s automating customer support, optimizing supply chains, or managing complex workflows.
• Faster decision-making: As we previously covered, since agentic AI doesn’t just analyze data (it acts on it), it could adjust production schedules, reroute, or respond to customer needs before issues arise.

There’s also a multiplying effect. The more autonomous AI becomes, the more industries it can transform and unlock new levels of productivity, efficiency, and innovation across the board.

What are the challenges or risks of agentic AI?

The more autonomy AI gains, the harder it becomes to predict, control, and correct its decisions. For instance, if an AI system is focused on efficiency, what happens if it prioritizes speed over accuracy or fairness?

Moreover, if there’s any bias in training data, these can have unintended consequences that spiral. So, without clear protections, businesses run the risk of deploying systems that make high-stakes errors with no easy way to intervene.

Then, there’s also the question of cybersecurity, data privacy, and trust. If AI is making decisions, businesses need to know how (and why), especially in regulated industries like healthcare and finance. Because as the system grows more complex, its reasoning can become harder to track, audit, or explain.

So, the real challenge isn’t just building more capable AI; it’s keeping it accountable, adaptable, and aligned with human judgment when it matters most.

Agentic AI is just the beginning

As we covered, agentic AI pushes traditional AI beyond passive assistance. But, the real question is how far we’ll let it go. Whether it becomes a trusted collaborator or a system we struggle to control depends on how we design, regulate, and integrate it into the world we already know.