Agentic AI Decoded: Your Blueprint for Smarter Systems in 2025
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AI technology is accelerating at a lightning pace. Information technology, education, manufacturing, finance – artificial intelligence has seeped into every stratum of our socio-economic system with demands soaring across sectors.
Agentic AIs are the pinnacle of AI advancements, a result of the escalating demand for autonomous intelligent systems. While LLMs like Chat-GPT, Grok, and DeepSeek work on prompts, agentic AI systems are goal-oriented entities that employ AI techniques like reinforcement learning to complete tasks and attain objectives. Thanks to this ability to work independently without explicit human input, Agentic AIs are set to become the next big thing in tech. Gartner predicts more than 33% of enterprise software applications will be designed by AI agents within 2028 with 15% of routine work decisions made anonymously.
Let us find out what factors make Agentic AI the rising star of modern technology.
What is Agentic AI?
Agentic AI is a spectrum of technologies. At one end, we have traditional agents that perform a particular set of tasks based on some predefined conditions. At the other end, we have state-of-the-art agentic systems that can perform tasks independently, learn from the environment, and make decisions autonomously.
Fully autonomous state-of-the-art AI agents are complex units and have multiple component systems working in cohesion. These components are the key modules that work in cohesion and enable the agentic system to accomplish its goals.
Technology Stacks Powering Agentic AI Systems
The sensing and perception module acts as the interface between the user and the AI agent – how the agent interacts with the outside world. Diverse sensors collect raw data from the environment (be it from a simple keyboard, a touchscreen, or a thermal sensor). The module then cleans and regularizes into a form that’s consumable by the agent.
The sensory and perception module turns raw data into meaningful information for the agent to work on. It is akin to the data ingestion layer whose speed, accuracy, and efficiency ripple across the entire agentic system and determines how well it aligns & augments business needs.
Purpose:
a. Provides a real-time, accurate picture of the environment (physical, digital, human, etc.), enabling autonomous awareness.
b. Preprocesses and interprets raw input into meaningful signals, thereby allowing the machine to perceive inputs and/or the environment and setting the stage for reasoning, planning & action.
Details:
a. Can comprise IoT sensors, cameras, microphones, APIs, database logs, natural language inputs, or a mix of everything.
b. Preprocessing involves noise filtering & normalization for DSP, tokenization for natural language input.
c. Feature extractions might include convolutional neural networks for image recognition, calculating text embeddings for NLP, or applying Fourier transforms on series data.
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The reasoning and decision-making engine is the brain of the entire system. It processes structured information from the sensing module and determines what to do next. This is where the raw ingested data (sensor data, customer complaints, etc.) translates to actionable choices and the component that makes agentic AI truly autonomous.
The decision-making engine is the nerve center of the entire system, whose smarts determine how efficient and effective the AI agent is.
Purpose:
a. Analyses and interprets the current situation/context based on the information ingested.
b. Anticipate & decides upon the best course of action from multiple options
Details:
a. May involve ASICs and/or GPUs that run complex reinforcement learning training.
b. May involve large language models, decision trees, Bayesian networks, Markov models, etc., for executing rule-based systems and/or probabilistic reasoning.
c. Can implement hybrid edge-cloud setups that reduce cost, latency, and decision time.