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Agentic AI, explained

By Beth Stackpole·

Citation: This article is summarized from the original guide by MIT Sloan.

What is Agentic AI?

Today, attention has shifted to the next evolution of generative AI: AI agents or agentic AI, a new breed of AI systems that are semi- or fully autonomous and thus able to perceive, reason, and act on their own. Different from familiar chatbots that field questions and solve problems, this emerging class of AI integrates with other software systems to complete tasks independently or with minimal human supervision.

MIT Sloan associate professor John Horton describes a particular class of AI agents as "autonomous software systems that perceive, reason, and act in digital environments to achieve goals on behalf of human principals, with capabilities for tool use, economic transactions, and strategic interaction". They can execute multi-step plans, use external tools, and interact with digital environments to function as powerful components within larger workflows.

How are Businesses Using Agentic AI?

Companies across sectors are starting to use AI agents. In financial services, companies are exploring agents to detect fraud, provide customized advice, and automate loan approvals. Retail giants are building LLM-powered AI agents to automate personal shopping experiences and facilitate customer service and business activities.

One particularly important application for agents may be performing tasks that a human typically would—such as writing contracts, negotiating terms, or determining prices—at a much lower marginal cost. In markets with high-stakes transactions, such as real estate or investing, AI agents can analyze vast amounts of data and documentation without fatigue and at near-zero marginal cost.

What Should Organizations Consider When Implementing Agentic AI?

  • Implementation is often the heaviest lift: Converting data into standard, structured formats for AI agents is especially important, because it helps them identify different data sources and requirements while maintaining consistency. Research found that 80% of the work was consumed by tasks associated with data engineering, stakeholder alignment, governance, and workflow integration.
  • Consider the "personality" of AI agents: Designing AI agents to have personalities that complement the personalities of other agents and human colleagues leads to better performance, productivity, and teamwork outcomes.
  • Embrace a human-centered approach: You have to make sure the agentic decision-making is aligned with a human-centered decision process.

    • What are the Risks of Agentic AI?

  • Irregular reliability: A rogue AI agent deciding to reject a mortgage loan based on faulty information can do just as much damage as simple hallucinations. Organizations need to be able to explain business decisions and consistently apply the same standards.
  • Cybersecurity: As AI agents gain permissions to access datasets and enterprise systems, it is vital to build robust permission-based systems.
  • Accountability: Organizations need to clearly delineate who bears responsibility when agentic AI makes an error or causes harm. A governance board should be established at the organizational level to oversee accountability.
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