The sky’s the limit for AI agents. Designed to operate autonomously, they can do everything from managing your calendar to handling customer queries. Gartner says that, by 2026, at least 15% of your day-to-day work decisions will be made autonomously through AI agents—up from 0% in 2023.

Businesses like Microsoft, Salesforce, and ServiceNow are quickly building their own AI agents. In fact, Salesforce CEO Marc Benioff believes that AI agents represent the next major leap in AI. 

All told, the AI agents market is expected to expand from $5.1 billion in 2024 to $47.1 billion by 2030, reflecting a compound annual growth rate (CAGR) of 44.8%. 

And yet, AI agents have inspired many questions. How do they differ from large language models (LLMs)? What role do people play when an AI agent operates autonomously? It’s essential for your business to understand AI agents and to start using them ASAP. 

Your AI agent is a friend to your LLM

AI agents are similar to LLMs in that both perform tasks for you. But, unlike LLMs, AI agents don’t require constant human prompting. When assigned an objective, they can independently generate tasks, complete them, create additional tasks as needed, adjust priorities, and continue cycling through tasks until the objective is achieved.

By contrast, LLMs generate text and images based on patterns learned from vast amounts of training data but don’t make decisions autonomously. Similar to AI agents, LLMs can accelerate your business growth. 

AI agents often integrate multiple AI technologies (including LLMs) to execute tasks such as customer service, data analysis, or even complex operations like autonomous driving.

However, despite their ability to integrate seamlessly with LLMs, AI agents are not replacements for LLMs. Rather, they complement them. Think of AI agents as enhancements for the capabilities of LLMs. Both serve distinct roles in the AI landscape and can work together to accomplish more complex tasks.

Here’s why you need AI agents: LLMs excel at understanding and generating human-like text, but AI agents enable LLMs to autonomously perform complex tasks in real-world environments with minimal human micromanagement. 

Businesses are adopting AI agents to drive cars, serve customers, and more

AI agents have achieved widespread adoption across several industries, including automotive and retail. In fact, an AI agent has probably helped you without you even knowing it. Here are a few examples:

• Tesla’s autopilot and full self-driving systems are examples of autonomous AI agents that assist with driving tasks. These systems handle tasks like lane changes, parking, and highway driving based on real-time environmental data.

• Siemens uses AI agents for predictive maintenance in its manufacturing plants. These agents monitor equipment in real time to predict when machines will fail, allowing for proactive maintenance that reduces downtime by 40% and increases productivity by 10%.

• DHL uses an autonomous AI agent called Cubicycle to optimize delivery routes based on traffic conditions and weather forecasts. This agent improves delivery efficiency by dynamically adjusting routes in real time.

• AI chatbots in customer support are enabling many companies to automate the handling of customer inquiries without human involvement. These bots resolve issues like password resets or refunds autonomously by interacting with backend systems and databases.

These examples illustrate how autonomous AI agents are being deployed across various sectors to improve efficiency, reduce costs, and enhance decision-making processes while operating independently from human oversight.

Build your own or use a commercially available AI agent

When using AI agents, your business will face the same decision as with GenAI applications built on LLMs: build your own or use a commercially available option. You can certainly build them yourself, or work with products like Agentforce from Salesforce for customer relationship management or ServiceNow to automate workflows.

Each option has advantages and challenges, depending on your organization’s goals, resources, and long-term strategy.

One consideration is total cost of ownership. Developing an AI agent in-house can be expensive and require significant investment in talent, infrastructure, and ongoing maintenance. Costs include initial development and continuous updates, retraining models, and monitoring performance over time. Specialized hardware, like advanced GPUs, and cloud services add to the expense.

That said, building your own AI agent allows you to tailor it specifically to your business needs. You have full control over its functionality, behavior, data integration, and user interactions. This can be especially beneficial for businesses in niche markets or heavily regulated industries where off-the-shelf solutions may not fully meet requirements.

And you can design custom-built AI agents with scalability in mind from the start. As your business grows or evolves, you can modify agents to handle increased workloads or new functionalities.

On the other hand, commercially available AI agents typically have lower upfront costs since they’re pre-built and ready to deploy. Vendors often offer subscription-based pricing models, which can be more predictable and manageable for businesses with limited budgets. However, licensing fees and potential vendor lock-in can add to long-term expenses.

Whichever path you take, a frontier AI data foundry platform will play a crucial role in bringing your AI agent online. 

Proceed thoughtfully with AI agents

As AI agents become embedded in various business functions, companies are exploring how best to deploy them without compromising quality or control. Applying AI agents thoughtfully helps ensure these tools not only support but also elevate business objectives. 

Keep humans in the loop

Although AI agents operate autonomously, it’s important to have human oversight to monitor performance and adjust when needed.

Human involvement helps catch unexpected issues, interpret complex nuances, and adjust AI behavior according to shifting business goals. Having humans in the loop also helps ensure that you apply AI responsibly.

This layer of monitoring enables you to maintain quality control, especially in high-stakes environments like healthcare, customer service, or financial services.

Start small and scale gradually

Rather than deploying AI agents across multiple workstreams all at once, start with a small, controlled deployment focused on a single use case. By gradually expanding, you can observe outcomes, refine models, and reduce risks associated with rapid deployment. A phased approach also makes it easier to train employees to work alongside AI agents.

Invest in continuous model training and improvement

AI agents operate in dynamic environments—and the data they use to make decisions will evolve over time. Regularly retrain agents with updated, relevant data to keep them aligned with current market conditions, regulations, and consumer behaviors.

Continuous improvement helps maintain accuracy and ensure optimal performance as business needs change.

Establish clear metrics for success

Define and track metrics specific to AI agent performance and impact on business objectives. This could include task completion rates, decision-making accuracy, customer satisfaction levels, or reductions in operational costs.

Clear metrics provide visibility into the agent’s contribution to business success and highlight areas for further enhancement. 

Accelerate AI agent deployment with a frontier AI data foundry platform

However you proceed, a frontier AI data foundry platform can help you succeed with AI agents. Centific’s frontier AI data foundry platform is purpose-built to curate high-quality, labeled data for training AI models. Whether building an AI agent from scratch or using a pre-built solution, the quality of your training data significantly affects performance.

Learn more about how your business can succeed with an AI data foundry.