Imagine a world where AI agents not only assist you in daily tasks but also transform how businesses operate. These intelligent software systems are already bridging the gap between the reasoning power of large language models (LLMs) and the practical needs of business, such as accessing private data and executing actions.

In this article, we explore how AI agents are becoming integral to business applications, reshaping software development, and transforming operations across industries.

What Are AI Agents?

AI agents are autonomous, assistive systems designed to act based on user input or environmental conditions. They are like advanced digital assistants, equipped with vast knowledge and the ability to access relevant data. These agents can reason, plan, and take action to accomplish tasks, much like an expert human counterpart.

In business, AI agents are revolutionizing operations. A service agent, for example, can function as a company's most knowledgeable technical support representative, available 24/7. Similarly, a marketing agent can use real-time data to adapt strategies and execute tasks autonomously, such as adjusting pricing or launching campaigns.

The Role of LLMs in AI Agents

The rise of AI agents is powered by large language models (LLMs), which provide two critical capabilities:

1. Deep language understanding: LLMs can comprehend complex and nuanced language, making them ideal for chatbot-type agents. They can understand user queries in depth and respond using natural language.

2. Reasoning and decision-making: LLMs can also reason and make decisions, allowing agents to plan and execute tasks.

However, LLMs alone aren't enough. They face limitations, such as lack of access to private data and an inability to take direct actions (e.g., updating records or placing orders). This is where AI agents come into play.

A New Era in Software Development

AI agents represent a shift in how software is developed. Rather than building monolithic applications, software is now composed of modular building blocks, or "actions," that agents can orchestrate using the reasoning capabilities of LLMs.

For example, at Salesforce, these actions might include functions like "Locate Order" or "Change Order Address," grouped into functional areas like "Order Management." AI agents can utilize these building blocks to perform tasks across domains, providing users with seamless experiences.

How AI Agents Operate

Here’s how an AI agent typically works:

1. Task Understanding: The agent leverages the LLM’s language abilities to grasp the task at hand.
2. Planning and Execution: Based on its understanding, the agent plans a series of steps, executing actions iteratively. It may ask clarifying questions and continue adjusting its actions until the task is complete.
3. Response: The agent generates a response or completes the task based on its execution.

This approach enables agents to handle new, unanticipated requests without needing predefined requirements. The flexibility to compose and orchestrate different actions on the fly is transformative for businesses.

Empowering Business with AI Agents

AI agents may empower businesses by enabling AI-driven capabilities across key areas like sales, service, marketing, and commerce. Developers can also customize these agents, adding new actions or integrating external systems via code, APIs, and flows.

Key features of AI agents include:

• Access to private data: Agents can access and act on customer and enterprise data while adhering to permissions and sharing models. This ensures that data remains secure and private.
• Action execution: Agents can execute complex logic and interact with external systems, making them ideal for automating business processes across industries.

Different Levels of Autonomy

AI agents operate at varying levels of autonomy:

• Assistive agents (copilots): These agents collaborate with humans, enhancing their capabilities by providing suggestions and actions.
• Autonomous agents: These agents operate independently, but with the ability to hand off tasks to humans when necessary.

Regardless of their autonomy, agents must be governed by guardrails to ensure reliability, data security, and adherence to business rules.

Guardrails for AI Agents

Trust layers protect sensitive data by using secure gateways, data masking, toxicity detection, and audit trails. Permissions and sharing models further ensure data security across all applications.

The Future of AI Agents

As AI agents become more prevalent, their impact on business and software development will continue to grow. Low-code tools make it easier for businesses to develop, customize, and deploy AI-powered agents tailored to their specific needs.

The future of business applications is here, and AI agents are leading the charge, offering unprecedented flexibility, efficiency, and innovation across industries.