AI systems are gaining agency to create plans and act autonomously, driving automation and workplace productivity.
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Intelligent agents in AI will make your AI more useful
Today’s AI models perform tasks such as generating text, but these are “prompted” — the AI isn’t acting by itself. That is about to change with agentic AI, or AI with agency. By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.
Intelligent agents in AI are goal-driven software entities that use AI techniques to complete tasks and achieve goals. They don’t require explicit inputs and don’t produce predetermined outputs. Instead, they can receive instructions, create a plan and use tooling to complete tasks, and produce dynamic outputs. Examples include AI agents, machine customers and multiagent systems.
Intelligent agents in AI are nascent but quickly maturing
While agentic AI is still in early stages, it's not too soon to gain an understanding of the technology, determine how to manage risk and prepare your tech stack.
The future of AI is about agency — and productivity
By giving artificial intelligence agency, organizations can increase the number of automatable tasks and workflows. Software developers are likely to be some of the first affected, as existing AI coding assistants gain maturity.
Agentic AI has the potential to significantly empower workers. It’ll enable them to develop and manage complicated, technical projects — whether microautomations or larger projects — through natural language.
Intelligent agents in AI will change decision making and improve situational awareness in organizations through quicker data analysis and prediction intelligence. While you’re sleeping, agentic AI could look at five of your company’s systems, analyze far more data than you ever could and decide the necessary actions.
Pinpoint high-impact AI opportunities with Gartner’s AI Use Case Insights for IT Leaders. Discover, evaluate, and prioritize AI opportunities to accelerate IT transformation and demonstrate value to the business.
Current AI agency is low, but expect it to grow
AI agency is a spectrum. At one end are traditional systems with limited ability to perform specific tasks under defined conditions. At the other end are future agentic AI systems with full ability to learn from their environment, make decisions and perform tasks independently. A big gap exists between current LLM-based assistants and full-fledged AI agents, but this gap will close as we learn how to build, govern and trust agentic AI solutions.
Manage the risks of agentic AI to reap its benefits
As intelligent agents in AI bring myriad automation opportunities, they also create challenges. These include:
Agentic AI proliferating without governance or tracking
Agentic AI making decisions that are not trustworthy
Agentic AI relying on low-quality data
Employee resistance
Agentic-AI-driven cyberattacks enabling “smart malware”
Effectively managing the risks of software entities acting autonomously requires advanced tools and strict guardrails.
Look for agentic AI in your technology stack
Agentic AI will be incorporated into AI assistants and built into software, SaaS platforms, Internet-of-Things devices and robotics. Many startups are already marketing themselves as AI-agent-building platforms. Hyperscalers are adding agentic AI to their AI assistants.
Prepare for and stand up agentic AI
To get started with agentic AI:
Select use cases based on efficiency, suitability and desired business outcomes.
Leverage APIs and events to enable agentic AI. This will allow AI agents to interact seamlessly with various tools and environments, ensuring they can execute tasks and receive information effectively.
Read the Planning Guide for Application Architecture, Integration and Platforms to access action plans for agentic AI and other key technology trends.
Learn more about how Gartner works with technical teams to navigate agentic AI, execute efficiently and drive business results.
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Intelligent agents in AI FAQs
What are some examples of intelligent agents in AI in real life?
AI-enabled machine customers — or nonhuman economic actors that obtain goods and services in exchange for payment — are examples of increasingly common intelligent agents. In the near future, they will make optimized decisions on behalf of human customers based on preset rules, and will quickly evolve toward greater autonomy and inferring of needs.
Are LLM-based agents like ChatGPT considered intelligent agents in AI?
In 2024, chat agents like ChatGPT, DALL-E, Google Bard, etc., do not have the agency to make plans and take action. In that sense they are not examples of agentic AI. Instead, they run on the current generation of LLMs and respond to user “prompts” by predicting the most common combination of words that would follow. AI agents, in contrast, will have agency to define and accomplish tasks.
What are common characteristics of agentic AI?
A fully mature intelligent agent will have agency to learn from its environment, create complex plans and perform tasks autonomously.
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