The Guide to Generative AI Agents

“AI” has been around for a long time (e.g. Siri, chatbots), and yet, there is so much excitement for this new era of AI agents. What are Generative AI agents, what makes them special compared to older AI technologies, and why is the world excited about them?

This post is a broad overview of the answers to these questions and why so many companies are choosing to partner with new AI agent solutions rather than older incumbents.

Decagon enables large companies (like Rippling, Eventbrite, Bilt, etc) to deploy AI agents to handle customer support and experience. You can learn more about us here.AI agents represent a new class of software systems that do more than just assist—they autonomously complete tasks that once required human effort.

Traditional software applications were designed to make your work easier—to help you look up information, analyze data, or draft a report. But in each case, the work itself still depended on you.

AI agents change that. These are autonomous systems capable of reasoning, making decisions, and achieving goals with creativity and flexibility, all while adhering to the boundaries set for them. While traditional applications assist you in doing the work, AI agents do the work for you.To understand the leap that AI agents represent, it helps to look at how technology has evolved in a specific industry. Let’s take customer support as an AI agent example. Before the rise of Generative AI, chatbots were the state-of-the-art in customer service automation. These bots, typically driven by decision trees, were designed to handle straightforward inquiries with pre-written responses. While they lightened the load for human agents, they often led to frustrating customer experiences—chances are you’ve encountered a chatbot that couldn’t understand your issue, leaving you repeatedly asking to speak to a human.

On the backend, tools were available to assist human agents by making their work more efficient—offering templates, categorizing tickets, or retrieving information from knowledge bases. But ultimately, the human agent was always in control.

Generative AI agents take this a step further. They can autonomously handle entire processes: crafting personalized responses, retrieving data, executing actions, and even analyzing conversations post-interaction. They don’t just assist—they manage the entire workflow from start to finish.

This capability is the real breakthrough of this new generation of AI agents. By relying on software to complete entire jobs, we fundamentally transform how work gets done.So, why is the concept of AI agents taking off now? The answer lies in recent advancements in Generative AI, particularly in the development of large language models (LLMs). These models, which power systems like OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Gemini, are remarkable for their ability to understand and generate human-like language, as well as reason through complex problems.

You’ve likely seen these models in action—solving difficult questions, creating detailed responses, or even generating creative content. When used effectively, these LLMs enable generative AI agents to do more than just chat; they allow the agents to get the job done.In many ways, Generative AI models are the brain of an AI agent. They provide the cognitive abilities that allow the agent to understand tasks, make decisions, and interact with users. But a brain alone isn’t enough. To function effectively in the complex environments of the real world, an AI agent needs more—it requires a complete system where each part plays a crucial role

• Orchestration: This layer acts as the agent’s nervous system, guiding its actions and ensuring it follows the business logic specific to each customer. Orchestration integrates the AI models with the company’s goals, making sure the agent’s actions are not just correct, but also aligned with what the business needs.
• Data Sources and Tools: Think of these as the agent’s muscles. They provide the strength necessary to perform tasks—whether that’s retrieving data from a CRM, processing a transaction, or interacting with specialized tools. This layer allows the agent to be customized and effective in performing the specific tasks required by each business.
• Hallucination Protection: This acts as the agent’s immune system, safeguarding against errors that could arise from the non-deterministic nature of AI models. Enterprises require robust guardrails to maintain the trustworthiness and reliability of their generative AI agents.
• Continuous Learning: This is the agent’s sensory system, constantly taking in feedback and adapting to improve its performance over time. A well-designed continuous learning loop enables the agent to refine its responses, update its knowledge base, and continually evolve to meet new challenges.

Generative AI represents a fundamental shift from the older AI technologies. It’s not just an incremental improvement; it’s a transformation. As the technology evolves rapidly, leading enterprises understand the need to partner with modern Generative AI solutions to stay ahead of the curve.

To help navigate this shift, we’ve put together a simple guide comparing the differences between classical AI and Generative AI. By understanding these differences, companies can make better decisions about how to leverage AI to drive innovation and improve customer experiences.