
What is Agentic AI?
Agentic AI refers to advanced systems that autonomously analyze data, adapt in real-time, and achieve complex goals with minimal human input.
Agentic AI refers to advanced systems that autonomously analyze data, adapt in real-time, and achieve complex goals with minimal human input.
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Agentic AI refers to advanced AI systems that can autonomously analyze data, pursue complex goals, and adapt in real-time by solving multi-step problems with low to minimal human intervention. Simply stating, for a given environment (data ecosystem) they can understand the task, gather the data from the sources, generate solutions with a reasoning model like RAG/LLMs, and act on it via access to tools and APIs. It ideally consists of guard rails in the form of confirmations or approvals from humans; and a feedback loop.
The capabilities of foundational models (which are typically used in Generative AI), have accelerated the growth and development of Agentic AI models. This makes generative AI and foundational models a great addition to the Agentic AI workflows, as they can assess the situation to brainstorm and create clear steps for innovative solutions. You think of LLMs like the brain of Agentic AI.
Given that AI Agents are an emerging field, there are no hardcore frameworks for evaluating or developing them. In general, Agentic AI follows the structure:
Though it might not be as simple as this, the idea is to have a Planner, Evaluator, and Executor which execute their own part and then flow their output into the next downstream activity like a multi-agent system.
Some of the areas where people have started seeing automation results in Agentic AI models are:
As we’ve noted previously, agents work in their environments and call the tools/functions they want – so there’s a lot of scope for things that can be automated. All we have to do is explore.
P.S. In most cases, you might need automation or workflows but not agentic AI. So, think about the flow and execution before you want to get started with agents.