Reinforcement Learning: Smarter AI, Faster Growth

March 27, 2025

🕒 7 minutes

In the last few months, we’ve had a lot of new AI developments. Salesforce Agentforce platform, SAP Joule Agents, Amazon Bedrock, and ServiceNow AI agents on Now Assist Skill Kit are just some of the most relevant advancements. However, we can see one constant in all of them: the increasing use of AI agents.

Companies across various sectors are integrating them to enhance efficiency and drive innovation. In fact, according to a recent Gartner survey, 55% of organizations that have previously deployed AI now adopt an “AI-first” approach, considering this technology for every new use case they evaluate.

But this momentum doesn’t seem to stop there. Gartner predicts that by 2028, 33% of enterprise software applications will incorporate agentic AI, a significant leap from less than 1% in 2024. This surge is expected to result in at least 15% of day-to-day work decisions being made autonomously by AI agents.

We are seeing a pivotal shift of AI agents or, more exactly, a transition from experimental tools to a central component of a business strategy. That’s why understanding the mechanisms that allows them to adapt to different business goals has become imperative to ensure agents remain competitive in an increasingly AI-driven world.

So, today we’ll dive into Reinforcement Learning, the main method that enables AI agents to adapt to complex, dynamic environments.

What Is Reinforcement Learning?

Reinforcement Learning (RL) is a type of ML where an agent learns to make decisions by performing actions and receiving feedback from its environment. This way, the agent aims to maximize cumulative rewards through a process of trial and error.

This approach is particularly effective in dynamic environments where the optimal action may not be immediately apparent. Let’s put this in perspective. Consider a sales optimization AI that helps a company refine its pricing strategy.

Initially, the system suggests different price points for a product based on historical data. Over time, it’ll observe customer reactions—increased sales (reward) or decreased demand (penalty)—and adjusts its strategy accordingly.

Reinforcement Learning is a particularly effective approach in dynamic environments

Just like a seasoned sales manager who refines pricing through experience, RL-powered AI agents learn the best course of action by continuously adapting to feedback. This ability makes RL invaluable for dynamic business environments such as demand forecasting, customer engagement, and personalized marketing.

This way, by leveraging RL, businesses can automate complex decision-making processes, improving efficiency and outcomes without relying on predefined rules or static models.

What are the key components of RL?

RL has many components that work together to enable AI agents to learn optimal behaviors through continuous interaction with the environment. To save you some time, we summarized them as it follows:

Agent: The decision-maker or learner.

Environment: The external system with which the agent interacts.

State: A representation of the current situation of the agent.

Action: Choices that the agent can make.

Reward: Feedback from the environment based on the agent’s action.

Policy: The strategy that the agent employs to determine actions based on the current state.

Value Function: A prediction of future rewards for a given state, guiding the agent to prioritize long-term benefits over immediate gains.

Reinforcement Learning vs other ML types

As we were saying, Reinforcement Learning is one of many other ML paradigms. And knowing the differences between them is crucial to selecting the right approach for business applications. So, to help you in this task, we summarized them in the following table:

Aspect	Supervised Learning	Unsupervised Learning	Reinforcement Learning
Definition	Learns from labeled data to map inputs to outputs.	Finds hidden patterns in unlabeled data.	Learns through trial and error, optimizing rewards over time.
Data Type	Requires large amounts of labeled data.	Works with raw, unstructured data.	No predefined dataset; learns dynamically from interactions.
Objective	Make accurate predictions based on past data.	Discover groupings and relationships in data.	Maximize cumulative rewards through adaptive learning.
Examples	Email spam detection, image recognition.	Customer segmentation, fraud detection.	AI-driven chatbots, robotic automation, game-playing AI.

How RL Shapes AI Agent Behavior?

Let’s see then how we can set AI agents behavior through RL. And to clarify the idea, let’s use the same reinforcement learning example of an AI-driven customer support agent.

Step 1: Define the Agent and Its Environment

To implement reinforcement learning in AI agents, you must first define the agent and its environment. In this case, the AI customer support agent that will interact with customers. The environment is the customer service platform, including customers, their queries, and the interaction context (chat, email, etc.).

Step 2: Set Up States, Actions, and Rewards

Next, you must identify the states, actions, and rewards in the RL process. These elements will help the agent understand how to take the best possible action in each situation. In this case, we can identify them as it follows:
– States: Represent the current context of the interaction, such as the customer’s inquiry type (e.g., product question, shipping issue) and the agent’s current knowledge of the problem.
– Actions: These are the responses the agent can take, such as offering an FAQ answer, offering a discount, escalating the issue, or providing a resolution.
– Rewards: Positive feedback (such as customer satisfaction) leads to a reward, while negative feedback (such as a long resolution time) results in a penalty. The agent learns over time which actions lead to the most rewards.

Step 3: Allow the Agent to Learn from Trial and Error

So, once the agent begins interacting with real customers, it will go through the trial-and-error process. On them, the AI tries different actions (responses) based on the state of the customer’s response—whether positive or negative—determining the agent’s reward or penalty.

This way, the agent adjusts its actions based on feedback. For example, if offering a discount leads to a happy customer, it receives a reward, reinforcing this behavior for future similar situations. In short, this trial-and-error learning is what enables the agent to continually optimize its responses over time.

Step 4: Fine-Tune the Agent’s Policy

The agent’s policy is the strategy it uses to decide what action to take in each state. Basically, as the agent gains more experience, it fine-tunes its policy based on the rewards it receives.

For example, if the agent learns that customers tend to appreciate faster responses, it may prioritize quick and helpful answers, adjusting its approach accordingly. Over time, the RL-powered customer support agent will develop a more efficient and personalized strategy, better suited to the needs of each customer.

Step 5: Evaluate and Monitor Performance

After the agent has been running for some time, it’s important to evaluate and monitor its performance. The following are some of the most common indicators:
– KPIs: Measure customer satisfaction, resolution time, and escalation rates to track the success of the agent’s decisions.
– Adjustment: If the agent’s actions are not producing the desired outcomes, it can be adjusted by tweaking the reward system or updating the training dataset.
– Ongoing Learning: Even after deployment, the agent should continue to learn from new interactions, making its decision-making process more effective over time.

RL vs RL with Human Feedback: What are their differences?

Now, there’s an important question when it comes to choosing this ML type. Should I go with regular RL or opt for Reinforcement Learning with human feedback (RLHF)? For developing AI agents, the decision significantly impacts various aspects of the project, including the time to market, cost of implementation, complexity of the system, and the quality of the output.

So, let’s look at their differences more deeply. As we saw, in standard RL, an AI agent learns by interacting with its environment, exploring various actions and adjusting its behavior based on rewards or penalties.

RLHF, as a more refined form of Reinforcement Learning, introduces human feedback into the training loop. Rather than relying solely on environmental rewards, human evaluators provide subjective assessments or ratings of the agent’s actions. In short, this helps refine agents’ responses beyond technical accuracy.

For example, in customer support, this could help agents to balance efficiency with empathy, improving user experience and strengthening brand trust over time. This way, the additional “human” layer of guidance allows them to learn from expert input, ensuring that their actions align more closely with specific business goals, ethical considerations, and customer needs.

Pros and Cons of RL and RLHF

Factor	Reinforcement Learning (RL)	Reinforcement Learning with Human Feedback (RLHF)
Efficiency	Highly efficient in environments with clear rewards and rules.	Can be slower to deploy due to the need for human intervention.
Scalability	Scalable in environments where rewards are quantifiable and predictable.	Less scalable due to dependence on human input.
Adaptability	Learns autonomously based on interaction with the environment.	Adapts more quickly in complex or ethical environments due to human guidance.
Human Involvement	Requires minimal human oversight once the system is set up.	Requires ongoing human input, especially in early stages of learning.
Applications	Suitable for gaming, inventory management, and simpler tasks.	Ideal for customer support, healthcare, or high-stakes applications.

When Should you Use Human Feedback?

So, when to use human feedback in Reinforcement Learning then? Well, while this varies depending on your industry and your business goals, there are four cases when it’s highly recommended to use RLHF for training your AI agents. These are:

Complex Environments: RLHF aids agents in complex scenarios like customer support, where feedback guides learning beyond rewards.

Ethical Considerations: Human oversight with RLHF ensures AI actions align with safety and regulations, crucial in sectors like healthcare.

Human Preferences: RLHF helps agents adapt to nuanced human preferences, such as optimizing product recommendations based on customer satisfaction.

Rapid Refinement: RLHF accelerates agent learning and behavior refinement, ideal for fast-changing business environments.

The AI Blackbox: RLHF as a solution of the AI reliability problem

AI agents are becoming a core asset for many industries. However, for businesses integrating them into their operations, reliability can be a double-edged sword. While these agents offer significant benefits, their behavior isn’t always stable, which can lead to unpredictable outcomes and pose serious risks in critical business functions.

Reinforcement Learning allows AI agents to learn from their environment and improve over time, but this adaptability also presents challenges. Agents can develop unexpected behaviors that are difficult to predict or control.

Industries like finance and healthcare can’t afford AI-driven mistakes. An unstable learning process could result in financial losses, regulatory violations, or operational disruptions, making reliability a top priority.

AI Agents behavior isn’t always stable, which can lead to unpredictable outcomes

On the other hand, RLHF adds a layer of oversight, helping to align AI behavior with human expectations. While this improves stability, it doesn’t eliminate risk entirely. Human feedback is naturally subjective and sometimes inconsistent, which can cause AI models to struggle with generalization across different scenarios. It also slows down the learning process, delaying deployment in fast-moving business environments.

For IT leaders, balancing AI agents autonomy with reliability is crucial. They must be robust enough to function in dynamic environments while maintaining predictable, stable outputs. To achieve this, companies need strong testing, monitoring, and governance frameworks to ensure their AI systems remain trustworthy.

Without these safeguards, businesses risk operational failures that can erode customer trust, reduce efficiency, and increase regulatory scrutiny. But don’t worry—at Inclusion Cloud, we’re here to help. Schedule a meeting to discuss how we can build a strong foundation for your AI systems.

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