AI Agents Are the Next Wave of Enterprise AI

The interest surrounding artificial intelligence (AI) has never been higher, but AI agents are the next wave of innovation enterprises need to prepare for.

An AI agent is a fully automated program with complex reasoning capabilities and memory that uses an LLM to solve problems, creates its own plan to do so, and executes that plan. To be an AI agent, the program must include four key components:

• A system of code with longform instruction-following capabilities
• An API understanding to use other tools
• Self-reflection capabilities to check its own work and progress in solving problems
• Some level of memory to store information

What separates AI agents from other AI software is the automation capabilities and responsiveness to any given environment without user intervention. These programs can work proactively on their own and require little assistance from a user. Think of an AI chatbot like ChatGPT, which needs continuous prompts from a user every step of the way. What if ChatGPT could create and act upon its own prompts rather than relying on a user?

AI agent chatbots do exactly this and autonomously review and improve on their results as they work to reach a definitive goal. Any of today’s popular generative AI (GenAI) tools could be upgraded to an AI agent—and I expect we’ll start to see that transition take place soon. 

GenAI is poised to transform the way people work and boost productivity in sales, marketing, customer operations, development, and more. McKinsey & Company estimates that applying GenAI to customer care functions could increase productivity at a value ranging from 30% to 45% of current function costs. Now imagine the impact of GenAI tools if they could work autonomously. That’s the power of AI agents. 

Understanding how your organization could leverage AI agents and if your environment can support them will be paramount to your future success with the technology. Yes, the capabilities surrounding enterprise AI are impressive, but no one should adopt it just to be part of a trend.

What could your organization do with a technology that is fully autonomous? And what boxes would your environment need to have checked off to handle an AI agent? These are the types of questions you need to answer.

What AI Agents Bring to the Table

What makes AI so intriguing is its potential to exponentially boost productivity, but this also spurs many questions. What could this look like? What kind of tasks could this technology be applied to? And will it be an industry disruptor? But to get a sense of where the technology is headed, you must understand its current state.

While it may seem like we’re only in the early days, AI agents are already quite prevalent and vary greatly in terms of tasks they can perform. For example, AI agent chatbots such as Auto-GPT work on tasks users give them by writing multiple sub-prompts to themselves along the way and even review their own work to improve results. These tools can autonomously monitor channels and continuously collect data so employees can spend time on more important work. 

AI agents are also used in self-driving cars. To drive autonomously, these vehicles use AI agent programs to monitor their surrounding environments, consider safety precautions and driving laws, and make decisions on the road to reach destinations.

One example of where enterprises can utilize AI agents is fraud monitoring. The ability to quickly analyze an immense amount of data, identify patterns, and find anomalies, enables AI agents to detect potentially fraudulent activities faster than a human ever could.

The Challenges and Requirements of AI Agents

For enterprises to properly leverage AI agents, they will first have to check off many major boxes. By its very nature, an AI agent demands a great amount from any environment it’s in. It will autonomously go through massive amounts of data to make decisions.

That alone leads to many prerequisites for an organization’s data architecture and infrastructure, including:

• Data management and infrastructure to acquire, store, and move large volumes of data
• Compliance with industry regulations
• Security and privacy
• The expertise to train and manage AI agents with enterprise data

All these requirements need to be addressed, especially for enterprises in regulated industries. For example, if an AI agent is used in a hospital, it must operate in a way that is HIPAA compliant. But even if an AI agent is not used in a regulated industry, it will still demand heightened privacy and security standards. Imagine a scenario where the AI agent is used for sensitive information, such as employee data or client documents. Organizations will need to be sure their environment meets proper security standards to avoid the risk of data leaks.

For an organization to leverage the productivity advantages of AI agents, it must first make sure its environment is prepared. While there are infrastructure barriers to adopting AI, the interest is there, and many organizations will make the investment.