What is Agentic AI?
Agentic AI refers to advanced AI systems that can autonomously analyze data, pursue complex goals, and adapt in real timeby 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.
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Agentic AI Quality Control System Architecture
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</div>How does Agentic AI work?
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:
- It starts with a human command/ discussion
- Clarifying the task and creating the execution flow
- Execute each step of the plan by calling the required tools/environments
- Pause or checkpoint for human feedback
- Task completion
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.
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