Imagine a teammate that works tirelessly, learns continuously, and adapts to your needs. That's the promise of AI agents.
AI agents are artificial intelligence systems that use tools to accomplish goals autonomously. Unlike traditional software that follows rigid, pre-programmed rules, AI agents can observe their environment, make decisions, and take actions with minimal human oversight. They have the ability to remember across tasks and changing states, use one or more AI models to complete complex workflows, and decide when to access internal or external systems on your behalf.
A consumer goods company optimized its global marketing campaigns using an AI agent. A project that once required six analysts per week now requires a single employee working with an agent, delivering results in under an hour.
AI agents operate independently, making decisions without constant human oversight
They perceive their environment and respond to changes in real-time
AI agents don't just react—they take initiative to achieve goals
They continuously improve performance based on experience and feedback
AI agents operate through a continuous three-stage cycle that enables them to observe their environment, leverage large language models for planning, and access connected systems to take action and accomplish goals.
AI agents constantly collect and process information from their environment including user interactions, key performance metrics, or sensor data. They retain memory across conversations for ongoing context.
Using language models, AI agents autonomously evaluate and prioritize actions based on their understanding of the problem, goals, context, and memory.
AI agents leverage interfaces with enterprise systems, tools, and data sources to perform tasks, delegate actions, or ask users for clarification.
This observe-plan-act cycle is self-reinforcing because AI agents continuously analyze how the world has changed and learn to be more efficient over time.
AI agents vary in implementation but tend to have five core components that work together seamlessly:
The protocols and APIs used to connect agents to users, databases, sensors, and other systems, allowing intelligent software agents to observe their environment.
Includes both short-term memory for recent events and immediate context as well as long-term memory for factual knowledge, concepts, and past task performance.
Defines the agent's attributes, such as its role, goals, and behavioral patterns—essentially the agent's personality and purpose.
Typically uses an LLM or SLM to take observations from the environment, including memory and the agent's profile, to assemble appropriate action plans.
Comprises the APIs and system integrations that define the universe of actions available to the AI agent for executing its plans.
AI agents are fast becoming common across industries. Early adopters have already unlocked value from these intelligent software agents in multiple functions.
A leading consumer packaged goods company used intelligent agents to create blog posts, reducing costs by 95% and improving speed by 50x.
A leading global bank used AI virtual agents to interface with customers, reducing operational costs by 10x.
A biopharma company used AI agents for lead generation, reducing cycle time by 25% and gaining 35% efficiency.
An IT department used AI agents to modernize its legacy technologies, increasing productivity by up to 40%.
AI agents are gaining traction quickly across an array of business applications—and the market for AI agents is expected to grow at a 45% CAGR over the next five years.
As AI agents become commonplace—and they will—humans will work closely with them as teammates. AI agents will be onboarded, just like human workers, to learn roles and responsibilities, access relevant company data and business context, integrate into workflows, and support humans' responsibilities.
Complex disciplines that previously required large teams of people will now become much smaller teams of humans working alongside many types of AI agents.
Organizations will scale faster since AI agents can replicate quickly, and companies will not be as dependent on hiring to grow. By building AI agents, companies will unlock new business models and accelerate productivity, freeing up workers to be more creative and productive.
Supervising virtual AI agents will become a core teaming skill, to ensure agents achieve their objectives and uphold standards of privacy, fairness, and ethical use. The more AI agents proliferate, the greater the need for employees to manage them—putting a premium on training in responsible AI at every level of the organization.