Lesson
The Emergence of Agentic AI
The emergence of agentic AI
Are LLMs actually capable of “thinking”?
When LLMs were originally released, they were effective for tasks like brainstorming or converting content formats, but struggled with strategic thinking, generating nuanced copy without human intervention, or solving complex math and logic problems.
This was because their "reasoning" was often a simulation derived from pre-existing content where humans have already performed the actual reasoning, rather than genuinely reasoning themselves.
However, new capabilities have emerged that are designed to have AI models incorporate more structured thinking or a logic chain before generating an answer. This type of AI is referred to as reasoning models, which aim to emulate human reasoning to generate answers and are expected to check their work. Products and companies powered by reasoning models are categorized as agentic AI.
What is agentic AI?
There is no widely agreed-upon definition of agentic AI, but you can think of Agentic AI systems as those that use “agents” designed to perform tasks by taking actions, planning, and often leveraging memory. Review the key features of an agentic AI model by selecting the numbered icons below.
How does agentic AI work?
Agentic AI systems often require coordinating across multiple capabilities, including planning, using tools, and maintaining context or memory throughout the activities involved. Select the tabs below to learn about each activity.
When an agent receives a request, it breaks a complex goal into smaller, manageable steps. For instance, if a Braze marketer asks an agent to launch a campaign, the agent might decompose the task of "sending an email campaign" into specific actions like writing the copy, defining the target segment, and setting up conversion events.