What are AI Agents & Why Should You Care?

Imagine a world where your business doesn't just respond to customer queries—it anticipates them. Where your sales team has a tireless assistant that never sleeps, and your support staff can focus on the most complex issues while routine matters are handled seamlessly. Well, this isn’t some far-fetched dream but a reality that Gen AI-powered agents are making true.

But here's the problem: many businesses still confuse AI agents with traditional robotic IVRs, capable of only limited, rule-based tasks. It's like comparing a chess grandmaster to a calculator—both useful, but in entirely different terms.

In today's evolving market, there's a pressing need for more adaptable and capable AI solutions for enterprises. That's where AI agents come in, ready to transform your technology stack and supercharge your customer experience.

In this blog, we'll explore anything and everything related to AI agents. From what they are, and how they work, to why they should be on your radar. So without further ado, let’s get started.

What are AI Agents: An Overview

AI agents are not your traditional robotic IVRs. They're sophisticated, AI-powered assistants capable of understanding context, making decisions, and taking action to achieve specific goals. Unlike traditional AI models that simply process inputs and produce outputs, AI agents can reason, plan, and adapt to new situations too.

Key characteristics of AI agents include:

• Autonomy: They can operate independently to achieve goals
• Reactivity: They respond to changes in their environment and interruptions
• Proactivity: They can take initiative to reach objectives
• Social ability: They can interact with other agents/humans in an empathetic manner

For enterprise settings, AI agents are the best choice for augmenting your customer experience across the entire lifecycle. They can improve sales by qualifying leads more effectively with empathetic communication, enhance customer service with personalized interactions, and streamline support by handling complex queries without human intervention.

How AI Agents Work

At their core, AI agents are just like your best agent with access to a vast library of knowledge and a toolbox of skills. But here’s what makes them different:

1. Large Language Models (LLMs ) for Reasoning: Think of this as the brain of the operation, capable of understanding and generating human-like conversations.
2. External tool integration: This is the agent's ability to use various tools, like accessing databases or APIs, to gather information or perform actions.
3. Memory & context management: Agents can remember previous interactions and maintain context, much like a human agent would in a conversation.

One popular approach to designing AI agents is the ReACT framework. ReACT stands for Reasoning, Acting, Observing, and Iterating. It's an effective paradigm for building an AI Agent.

Here’s how it functions together:

• Reasoning: The agent analyzes the task and plans its approach.
• Acting: It takes action based on its plan, using available tools.
• Observing: The agent evaluates the results of its actions.
• Iterating: If needed, it adjusts its approach and tries again.

This framework allows AI agents to tackle complex, multi-step problems with a level of sophistication that traditional AI models simply can't match.

Types of AI Agents

Just as there are different types of human agents having varied capabilities, there are various types of AI agents, each with its own strengths and ideal use cases:

Simple Reflex Agents:

• Are rule-based and react based on current input, without considering customer history or future consequences.
• Used for basic customer service inquiries, and simple data retrieval tasks.
• Limitations: Not suitable for complex enterprise environments requiring context or memory.
• Example: A chatbot that provides store hours or basic product information.

Goal-based Agents:

• Work towards achieving specific objectives, considering different approaches.
• Can be used for business process optimization, and project management assistance.
• Example: An AI agent that optimizes inventory management by analyzing sales trends and supply chain data.

Utility-based Agents:

• Makes decisions based on what provides the highest utility or value.
• Used for resource allocation, and risk assessment in financial services.
• Example: An AI agent in a call centre that routes calls to the most appropriate agent based on customer history, agent skills, and current workload.

Learning Agents:

• Improves performance over time through experience and feedback.
• Used for personalized adaptive customer interactions
• Example: An AI sales assistant that learns from successful sales calls to improve its pitching strategies over time.

Agentic vs Non-Agentic AI

Agentic and Non-Agentic AI are different in their own terms. Choosing between them is like deciding between hiring an intern and an executive. Here's how they’re different from each other:

The right choice depends on your specific needs. Non-agentic AI might be enough for simple, repetitive tasks, but for businesses looking to provide superior, adaptive customer experiences, agentic AI is the way to go.

Use Cases & Benefits of AI Agents in Enterprise Settings

AI agents are not just fancy voice bots—they're the most practical solution for enterprise operations and support. Here are just a few ways they can benefit you and your customers:

• Enhanced CX and support: AI Agents don't just answer questions but anticipate your customers’s needs too.
• Streamlined decision-making: It can analyze vast amounts of data to provide insights for informed decision-making.
• Improved data analysis: From market trends to customer behavior, AI agents can uncover patterns your human agents might miss.
• Automated query resolution: Capable of handling level 1, 2, and even some level 3 queries without human intervention, freeing up your staff for more complex tasks.
• Personalized solutions: Tailor responses, offers, and recommendations based on individual customer profiles and histories.

Risks & Limitations of Today's AI Agents

While AI agents offer immense potential, they're not without challenges:

• Accuracy and reliability: AI agents can make mistakes or misinterpret complex queries.
• Need for oversight: Human supervision is still crucial, especially for high-stakes decisions.
• Data privacy concerns: Handling sensitive information requires robust security measures.
• Balancing autonomy and control: Determining the right level of agent autonomy is a delicate balance.
• Ethical considerations: Ensuring fair and unbiased operation of AI agents is an ongoing challenge.

Implementing AI Agents: Best Practices for Enterprise Decision Makers

Deploying AI Agents into your business is a big decision and we totally understand that. But if you are ready to implement AI agents into your business, Here's a practical roadmap you can follow:

1. Assess organizational readiness: Evaluate your current business needs, tech infrastructure, and team capabilities.
2. Identify high-impact use cases: Start with areas where AI agents can make the biggest difference.
3. Ensure proper data infrastructure: AI agents are only as good as the data they can access. So feed them with effective data at all times.
4. Balance automation with human expertise: Design workflows that combine AI efficiency with human insight as total dependency on AI might cause some irreversible damage.
5. Develop an AI governance framework: Establish clear policies for AI use, monitoring, and accountability.

What We Think About AI Agents & Augmenting CX at Ori

At Ori, we're not just excited about AI agents—we're passionate about their potential to transform business operations and customer experiences. From BFSI to hospitality, and automotive to healthcare, we envision a future where AI agents become indispensable partners in delivering exceptional service and driving growth.

Imagine a bank where loan approvals happen in minutes, not days. Picture a hotel where your preferences are anticipated before you even arrive. Think about a healthcare system where follow-ups and medication reminders are handled seamlessly, improving patient outcomes.

This isn't a distant future—it's the reality that Ori's agentic AI is creating right now. By implementing our AI agents, businesses aren't just improving their CX; they're achieving tangible growth through increased conversions and operational efficiency.

To the forward-thinking leaders and decision-makers reading this, the age of AI agents is here. The question isn't whether to adopt this technology, but how quickly you can adapt it to stay ahead of the curve.

So, if you want to augment your customer experience while staying ahead of your competitors, book a free demo with our experts here .