Key takeaways:

• AI agents pair large language models (LLMs) with the ability to make logical decisions and execute real-world actions.
• AI agents simplify the design and deployment of complex automations and make language-based interactions with machines more natural.  
• OneReach.ai’s Generative Studio X enables the creation of AI agents that can automate tasks, and query databases—interacting with employees, customers, and other AI agents on a variety of channels, from text to voice.

Ever chatted with a bot and felt like you were stuck in a bizarre game of 20 Questions? You’re not alone. According to Callvu’s survey, 81% of respondents said they would rather wait to talk with a human agent instead of using a chatbot to resolve their problems.

But what if there was a way to make chatbots more flexible, more human-like, and more capable of handling the unexpected twists and turns of real conversations? Enter the world of AI agents—a revolutionary approach that’s set to transform the field of conversation design. In this post, we’ll explore the difference between standard chatbots and AI agents, the benefits of designing and deploying AI agents, and why they might become truly intelligent digital assistants.

From Scripted Chats to Smart Conversations: How AI Agents Are Redefining Interaction

The standard approach to building conversational AI, such as chatbots and IVR systems, involves creating a flow diagram that depicts the course of human-bot interaction. Natural language understanding (NLU) is used to determine the intent (meaning) of users’ messages, and logical statements route the interaction toward the users’ desired outcome.

For example, when you type, “I’d like to check my balance” in the chat window of your bank’s website, a language model trained on the hundreds of possible questions users might ask classifies the intent of your message as a “balance check.” Based on this classification, a logical statement routes you to your account balance instead of other possible outcomes, such as deposits and withdrawals.

For more than a decade, conversation designers have used this approach to build complex, language-based interactions with computers. However, this approach has downsides:

• Building models of intent recognition requires envisioning and testing all the possible ways a human might interact with a bot (e.g., “balance check,” “how much money do I have?”, “what’s in my account?”, “why can’t I take out money?”)—a time-consuming and costly process.
• Bots typically follow a script that was likely written by a human. Using conversational AI solely for intent recognition misses out on the bigger opportunities present in on-the-fly language generation.

These characteristics make interactions with many bots feel “rigid”—they can’t handle questions that fall outside their programmed scenarios, and the language they use is always the same.

By contrast, AI agents are revolutionizing conversation design by ditching the script and embracing on-the-fly language production. These digital dynamos offer a more fluid, intuitive, and human-like interaction that’s set to transform how we communicate with machines.

AI Agents Explained: Combining LLMs, Logical Reasoning, and Real-World Actions

AI agents are transforming the landscape of conversational AI by combining the power of large language models (LLMs) like GPT-4o and Claude with the ability to make logical decisions and perform real-world actions.

LLMs are notoriously bad at logical reasoning. But by instructing an LLM to solve a problem step-by-step—a technique known as chain-of-thought prompting—the model’s ability to make logical decisions is dramatically improved. In a conversation with a human, chain-of-thought prompting gives AI agents the ability to recognize the intent of the user’s message and then decide what action the user wants the agent to take.

Using Retrieval-Augmented Generation (RAG), AI agents connect LLMs to databases of information specific to an organization and its users’ needs. The best RAG systems use semantic search, finding information related to the meaning, or intent, of the search term. Good RAG systems also limit the large language model’s answer to information returned by the search. If the search fails to find a result, rather than hallucinating an answer, the AI agent tells the user that the searched database doesn’t contain a great answer. 

Unlike ChatGPT, AI agents have access to tools that allow them to perform actions like sending emails and scheduling meetings. Tools can be invoked at any stage of an interaction. For instance, you could ask an agent to edit an email, and after a lengthy back-and-forth about the correct phrasing of a key sentence, tell the agent to send the email and cc a colleague.    

From the standpoint of conversation design, agentic solutions eliminate the need to build models for intent recognition. They also reduce the number of logical statements required in the programming of the interaction. Both intent recognition and logical decision making are handled by the AI agent in a single step. And because these agents rely on large language models, they can use their general knowledge of language to produce text on the fly and handle questions that fall outside their predefined objectives. This leads to more natural, unscripted interactions with users. 

Creating and Orchestrating AI Agents in Generative Studio X (GSX)

Generative Studio X allows users to create complex AI agents quickly, without having to write code. Users simply create and name their AI agents and state agents’ objectives and expected behaviors using natural language. All AI agents in GSX start with the ability to respond to users’ questions and comments. Then you can define actions to allow the agent to perform tasks—like sending emails, scheduling meetings, finding information online (or in a database using RAG), talking to APIs, and more. 

AI agents can also be enhanced by deploying sub-agents for specialized tasks. Moreover, like all language-based interactions built in GSX, agentic solutions can be seamlessly deployed across various communication channels—whether text, voice, or Rich Web Chat.

Transforming Workflows with AI Agents

AI agents enable conversation designers to build automations that would have been impossible only a few years ago. For example, according to the Harvard Business Review, the average professional spends over 25% of their day reading and responding to emails. Automating the task of deciding which emails require immediate attention, and which can be ignored, can save countless hours and boost productivity. Traditional conversation design struggles with such complex tasks due to the extensive time and resources required to develop intricate intent recognition models and logical statements. 

However, AI agents simplify this process by combining language understanding with decision-making capabilities. By leveraging chain-of-thought reasoning and external data sources, AI agents can quickly and accurately determine the urgency of emails, tailoring their responses based on the user’s role. Agentic solutions like this can be built and tested in days, offering a powerful tool for enhancing efficiency and productivity in the modern workplace.

Conclusion 

AI agents are shaking up the world of conversational AI by combining large language models with smart decision-making and real-world actions. This game-changing approach makes designing and deploying complex automations a breeze while keeping interactions feeling natural.

Learn more about AI agents with our whitepaper, “What Everyone Is Getting Wrong About AI Agents,” and try them out in our Generative Studio X.