The Emergence of Agentic AI

The Rise of Reasoning

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 a reasoning model, which aims to emulate human reasoning to generate answers and are expected to check their work.

Advancements in AI have enabled models to move beyond simple content generation to perform decision-making and continuous learning, leading to the rise of a new category of products and companies known as Agentic AI.

What is Agentic AI?

Agentic AI can be defined as when you give an AI system agency to take actions or complete tasks on your behalf.

Agentic AI refers to artificial intelligence systems that use “agents” designed to perform tasks by taking actions, planning, and often leveraging memory. Select each numbered icon to learn more about the key capabilities of an agentic AI model.

• Autonomy: Agentic AI can initiate and complete tasks without constant human oversight.
• Goal-Driven: Agentic AI systems operate with clear objectives and break down complex goals into smaller, executable tasks.
• Reasoning and Planning: Agentic AI uses large language models as a “brain” to plan—analyzing data, understanding context, and deriving a strategy to achieve its goal.
• Execution: Agentic AI can take real-world actions by using tools, interacting with external systems (like APIs and databases), or providing responses to users.
• Adaptability: Agentic AI can learn from its environment and adapt its plans in real time to overcome challenges or incorporate new information.
  

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.

Planning

First, Agentic AI uses two techniques, subgoal and decomposition, to break down a big goal into smaller, simpler steps. This is like a Braze marketer breaking down the task of "send an email campaign" into "write the email copy," "create a segment to send to," and "add a campaign conversion event".

The agent also uses reflection and refinement to learn from its past actions. If a step didn't work, the agent can "think" about what went wrong and adjust its approach for next time. This self-correction allows the agent to get better over time.

Memory

An AI agent needs to remember information to complete a task. It has two types of memory:

• Short-term memory: This is temporary information that the agent uses for the current task, such as everything you've said in the current conversation.
• Long-term memory: This is information it needs to remember for a long time to achieve its goal. It does so by using an external database to store and quickly recall large amounts of information.

Tools

Finally, Agentic AI can utilize different tools to execute its task. These tools include access and use of external programs or databases—like a web browser, a calculator, or a company's sales data—to get real-time or specific information it doesn't already know. This allows the AI to perform a wide range of actions and access information that isn't included in its training.