GenAI vs AI Agent vs Agentic AI

GenAI        ----   AI Agent    ----    Agentic AI 

(Thinks)                (Acts)               (Thinks, plans, acts and adapts)

Engineering analogy:

• GenAI → A smart function (Creates content when you ask)
• AI Agent → A running service (Observes, decides and acts toward a goal - loop)
• Agentic AI → A coordinated system of services with control logic (Autonomously plans, coordinates actions, and adapts until the goal is achieved).

Agentic AI

Agentic AI refers to AI systems that can autonomously plan, decide, and take actions over multiple steps to achieve a goal - not just generate an answer.

Agentic AI = advanced AI Agents with autonomy + reasoning

AI Agents are the building blocks of Agentic AI - An architecture and behavior, not a single model.

e.g., Imaging workflow orchestration (DICOM/PACS), SCADA self-healing agents, Vehicle diagnostic agents (CAN, UDS)

AI Agents

 An AI Agent is a software system that can: Observe → Decide → Act to achieve a goal.

AI Agent  =  Goal-driven software that takes actions based on observations.

Reads data (logs, sensors, messages, files, APIs)  -> Applies rules, models, or LLM reasoning -> Executes commands (API calls, device control, alerts, updates) -> Observes results and adjusts behavior (loop repeats)

Perception  ->  Decision  ->  Action  ->  Feedback Loop (repeats)

e.g., Clinical workflow agents (Medical), SCADA monitoring agents (Industrial Automation), Diagnostic agents (CAN, UDS) (Automotive)

GenAI

AI systems that generate new content— text, images, code, audio, video —based on patterns learned from data.

Generative AI is content generation, works reactively (responds to input, doesn't act on its own) - usually not autonomous.

e.g., ChatGPT, DALL·E, Claude, Gemini, Stable Diffusion.

AI vs Machine Learning vs Deep Learning

Artificial Intelligence - Human Intelligence exhibited by Machines.

Machine Learning - An Approach to Achieve Artificial Intelligence.

Deep Learning - A Technique for Implementing Machine Learning.

Artificial Intelligence is a generalized form of Machine and Deep Learning and check this incredible post for detailed information.

Succinctly, Deep Learning is-a Machine Learning is-a(n) Artificial Intelligence.

Deep Learning

Deep Learning extends (IS-A a type of) Machine Learning by adding cascade of layers to efficiently exercise data patterns before predicting the outcome to the closest possible, which lead to some scenarios better than humans.

Today's driver-less cars do use this mechanism underneath.

Machine Learning

Machine Learning extends (IS-A type of) Artificial Intelligence (AI) by incorporating algorithms - are often categorized as supervised and unsupervised - into computers with an ability to learn without being explicitly programmed when exposed to the new data. 

AI

OpenCV

Open Source Computer Vision is a library of functions, designed for computational efficiency with a strong focus on real-time computer vision applications. Having written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, so it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

It has C, C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.

Deployed uses span the range from stitching street view images together, detecting intrusions in surveillance video, monitoring mine equipment, helping robots navigate and pick up objects, detection of swimming pool drowning accidents, checking runways for debris and inspecting labels on products in factories on to rapid face detection.

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Intel IPP

Intel® Integrated Performance Primitives is a multi-core, multi-OS and multi-platform ready, computationally intensive and highly optimized software library that meets all your Imaging, Vision, Signal, Storage and Security needs.

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OpenCL

Open Computing Language is the open standard for cross-platform, parallel programming diverse processors found in personal computers, servers, mobile devices and embedded platforms. It greatly improves the speed and responsiveness of a wide spectrum of applications including gaming, scientific, medical software, professional creative tools and vision processing etc...

To put it in a nutshell, the open standard for parallel programming of heterogeneous systems.

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CUDA vs OpenGL

CUDA is a parallel computing platform API, which extended the usage of GPU (which was otherwise used only for graphics processing) for general purpose processing. But the scope is limited to CUDA-enabled GPUs manufactured by NVIDIA only and does not work on different GPUs by other manufacturers. CUDA platform is designed to work with C, C++ and Fortran programming languages hence easier to code straight to GPU. Compatible with most standard operating systems.

e.g. Identify hidden plaque in arteries, Analyze air traffic flow and Visualize molecules etc...

OpenGL is a graphics library, which provides set of API that makes use of GPU (hardware-accelerated) to render graphics faster - focus is specific to rendering on GPUs. Supported on essentially every GPU. Required advanced skills in graphics programming. It is language and platform independent.

e.g. CAD, Virtual reality, Flight simulation and Video games etc...

CUDA

Compute Unified Device Architecture is a parallel computing platform and API to use a CUDA-enabled GPU for general purpose processing (GPGPU), which leads to dramatic increase in computing performance by harnessing the power of the GPU. 

The CUDA platform gives direct access to the GPU's virtual instruction set and parallel computational elements to execute compute kernels. Supports OpenCL programming framework.

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OpenGL

Open Graphics Library (OpenGL) is a low level abstract API to interact with graphics processing unit (GPU) to draw 2D and 3D graphics and is a cross-language, cross-platform API typically used to achieve hardware-accelerated rendering, which is faster over software rendering.

OpenGL deals with only rendering and provides, no APIs related to input, audio or windowing. 

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