In today’s rapidly evolving AI landscape, 40% of executives identify AI and virtual assistants as the most critical innovations for their companies. With an average ROI of around 300% from implementing virtual assistants, the value they bring is undeniable. 

Until recently, pre-designed, personal, AI-driven software like Siri, Alexa, and Google Home were often the most sophisticated expressions of conversational AI in our lives. That’s changing rapidly, as companies aspire to develop their own customized “superagents” capable of integrating with diverse business communication channels—such as phone, SMS, and Slack—enhancing operational efficiency and delivering tailored customer experiences.

Keep reading the article to learn more about AI agents, an agentic approach to workflow automation, and effective strategies for integrating AI solutions into your business.

LLMs vs Agentic Approach 

Behind words are ideas. Words have no purpose without ideas.

— Robb Wilson, CEO and Co-Founder of OneReach.ai

Large language models (LLMs) are fascinating, but they’re all about predicting the next word based on the preceding words in the thread. They don’t have an understanding of the world and can’t reason about ideas. They can create the illusion of reasoning, but LLMs are just stringing words together based on patterns they’ve learned. 

AI agents take it a step further. Instead of just predicting the next word, they predict the next action you need to take, like booking a flight or choosing a hotel near your destination. What’s even more exciting about AI agents is their ability to integrate context and data. 

Using “communication data fabric” AI agents can swarm around tasks armed with context, making informed decisions and taking actions on your behalf without constantly interrupting you for input. The agentic approach can create an entire team of these virtual assistants, each specialized in different tasks, but sharing information and working collaboratively. 

Avoiding AI Overload: Starting Small is Key

Many people are intimidated by the technologies surrounding AI, often diving into overly complex problems that existing software can’t solve. This leads to confusion, failure, and abandoned projects. It’s smarter to start small, like creating your own company’s simplified version of Siri or Alexa that can answer questions about your business, project, or team. 

A common mistake is attempting to load an entire knowledge base or all company documents into a large language model (LLM) to create an all-knowing AI. This approach is overwhelming and ultimately ineffective. The idea that dumping thousands of documents into a training vector database will result in flawless answers is unrealistic. However, by starting small and focusing on manageable projects, businesses can make AI useful, functional, and beneficial.

Beyond Generative AI: The Future of Intelligent Decision-Making

The notion that generative AI will be the ultimate tool for creating some sort of reasoning artificial general intelligence (AGI) is unlikely. The truth is that LLMs are much stronger as a user interface than any sort of logic engine or brain. Time will tell how critical LLMs become in communicating with machines that can then pass information to other systems better suited for reasoning.

True innovation occurs when generative AI, symbolic AI, and traditional logic are integrated into a cohesive cognitive architecture. At OneReach.ai, we prioritize classification when creating conversational apps and agentic workflows for businesses, both large and small. It helps determine criticality and routing. Once routing is established, everything else falls into place. This is the cornerstone of effective AI solutions. While AI agents can operate within this framework, they typically struggle with classification independently. That’s why for years we’ve been advocating for keeping humans in the loop for control and navigation.

Strategic AI Integration: Customizing “Superagents” for Business Success

As AI continues to reshape industries, the focus is shifting from generic, pre-designed solutions to bespoke AI agents tailored for specific business needs. Customized “superagents” are emerging as the new norm, designed to seamlessly integrate across diverse communication channels and enhance operational efficiency while delivering personalized customer experiences.

It’s also important to note that the goal of using AI agents isn’t to replace human intelligence, but to augment it. It’s about creating tools that work with us, not instead of us. 

Discover more about taking an agentic approach to automating your workflows and processes in our whitepaper, “What Everyone Is Getting Wrong About AI Agents.”

Explore forging humanistic AI in the “Invisible Machines” episode with Tom Gruber, Co-Founder of Siri.

Learn about building an autonomous company with AI agents in the bonus episode of the Invisible Machines podcast.