In January, MIT SMR columnists Thomas H. Davenport and Randy Bean predicted that agentic AI would be “a sure bet for 2025’s ‘most trending AI trend.’ ” They called that one correctly.

“Agentic AI seems to be on an inevitable rise: Everybody in the tech vendor and analyst worlds is excited about the prospect of having AI programs collaborate to do real work instead of just generating content, even though nobody is entirely sure how it will all work,” they noted.

That’s still true, almost a year later. Agentic AI continues to capture the imaginations of leaders and the hopes of tech vendors. Yet much of the discussion around AI agents is hypothetical, and most corporate work remains in the early-experimentation stage. Even OpenAI cofounder Andrej Karpathy recently declared that it may take 10 years for AI agents to work well.

While market watchers are beginning to raise concerns about the circular nature of the deals fueling the AI economy, many corporate leaders are nonetheless feeling significant pressure to figure out how to innovate using AI — especially agentic AI.

With all the hype about agentic, however, it can be tough to sort through the facts. Do you have a clear picture of what agentic AI does? Of how software agents communicate? Of what the technology’s limitations are? Here, we briefly answer some key questions about agentic AI technology, using excerpts from two recent MIT SMR articles, “Agentic AI at Scale: Redefining Management for a Superhuman Workforce” and “Three Agentic AI Security Essentials.” Let our expert researchers and practitioners get you up to speed. Let’s delve into it.

1. What are AI agents?

“Although there is no agreed-upon definition, agentic AI generally refers to AI systems that are capable of pursuing goals autonomously by making decisions, taking actions, and adapting to dynamic environments without constant human oversight. According to MIT’s AI Agent Index, deployment of these systems is increasing across fields like software engineering and customer service despite limited transparency about their technical components, intended uses, and safety.”

“AI agents — powered by large language models (LLMs) — are no longer futuristic concepts. Agentic AI tools are working alongside humans, automating workflows, making decisions, and helping teams achieve strategic outcomes across businesses.”

2. How do AI agents differ from other AI tools?

“Unlike older AI applications that operate within narrowly defined boundaries, like chatbots, search assistants, or recommendation engines, AI agents are designed for autonomy.”

3. Do companies see tangible ROI from agentic AI investments?

“Among companies achieving enterprise-level value from AI, those posting strong financial performance and operational efficiency are 4.5 times more likely to have invested in agentic architectures, according to Accenture’s quarterly Pulse of Change surveys fielded from October to December 2024. (This research included 3,450 C-suite leaders and 3,000 non-C-suite employees from organizations with revenues greater than $500 million, in 22 industries and 20 countries.) These businesses are no longer experimenting with AI agents; they are scaling the work.”

4. How do AI agents communicate to get work done?

“AI agents operate in dynamic, interconnected technology environments. They engage with application programming interfaces (APIs), access a company’s core data systems, and traverse cloud and legacy infrastructure and third-party platforms. An AI agent’s ability to act independently is an asset only if companies are confident that those actions will be secure, compliant, and aligned with business intent.”