Agentic AI is an important evolutionary step in the design of business software. In this research perspective, we dive into a clear definition of agentic AI, including what to watch out for in terms of "agent-washing" - rebranding and representing already-existing solutions as agentic AI.
Adopting agentic AI requires data
Agents or agent-like systems are beginning to appear in a wide range of business software and will continue to proliferate at an accelerating pace over the next 3 years. Agentic AI can help any enterprise, but the readiness of an organization to adopt the technology will vary significantly - especially if you face insufficient clean data to support training and maintenance, or both.
Not all agents are equal
Our agent taxonomy distinguishes different tools by their sophistication in training and the resulting scope of abilities. In this research perspective, we breakdown 4 common types of agentic AI and then analyze the degree to which the AI is capable of handling static and dynamic complexity. Of course, changes in the complexity result in changes to the data requirements and total costs.
.png?sfvrsn=acada831_0 "four types of agentic ai")
Download our research perspective
To avoid falling for the usual technology hype, you need to have a clear understanding of what agentic AI does and doesn’t do. To avoid being disrupted by your competition, you need to commit now to preparing your data, people and operating model to enable adoption. Get a primer on this new technology with our research perspective.
Page Count: 8
Download Report
