Blog Posts

• 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

- Perception and Sensing Module

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.

- Reasoning & Decision-Making Engine

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.

- Goal & Planning Framework

Here lies the grand strategist of the entire agentic AI system. The goal and planning framework devises the best approach and charts the course towards the system’s objectives. It takes the decisions from the Reasoning & Decision-Making engine and then translates them into a structured sequence of steps and tasks, thereby formulating a long-term strategy.

The goal and planning framework creates an overall strategy or roadmap for attaining an objective. The success of an outcome depends a lot on the overall clarity and foresight of the framework’s devised plan.

Purpose:

a. Sets clear objectives that can be predefined or inferred dynamically.

b. Break down goals into actionable steps that are optimized for time, risks, costs, and resources.

c. Provides direction and structure to the agentic AI system, helps it anticipate & prepare, and aligns everything with the business priorities.

Details:

a. Core technologies included LLMs, search algorithms, and Markov decision processes.

b. Involves hierarchical planning with goal definition, plan generation, optimization, and validation.

c. Low-powered CPUs speed up decisions and processes at the edge. For high computation requirements, cloud services handle scale.

- Action and Execution Layer

Here we have the actuators or the hands of the agentic AI system. The action and execution layer devises the tasks and sub-tasks necessary to execute the plans and decisions of the goal and planning framework. It is here that intent becomes reality with the layer taking actions that translate to tangible outcomes in the real world, such as sending a control signal, executing a trade, etc.

This is the layer that delivers and its speed, efficiency, & reliability impact ROI directly.

Purpose:

a. Conducts tasks according to plans and strategies devised.

b. Interfaces with external devices, systems, and/or humans.

c. Generate feedback for the agentic AI system.

d. Turns strategy into actions and aligns with the strategy’s operational goals.

Details:

a. APIs & integrations, actuators & robotics, signal generators, tools, LLMs – the execution layer can comprise myriad components as per tasks and environments.

b. Can involve ASICs, GPUS, microcontrollers, etc. edge devices, actuators for physical outputs, or serverless functions for actions in the cloud.

c. Software and algorithms in this layer can involve PID controllers for physical output, priority queues, and RTOS for task scheduling, error handling logic, and LLM-powered generation.

- Memory & Knowledge Retention Module

As the name suggests, this module is the storage or the archive where different strategies & workflow, information & interactions, learned experiences & insights are recorded. The memory and knowledge retention module acts as the repository, which allows the agent to leverage historical information for efficient real-time operations. Memory modules can be on-premises, on the cloud, or SSDs on edge devices.

Purpose:

a. This module is responsible for helping the agentic system acquire & accumulate knowledge into which it can then tap.

b. The data retained from previous actions drives long-term value by helping the system recall and act coherently with consistency.

Details:

a. Different storage systems like vector databases, key-value stores, relational databases, etc.

b. Low power solid state drives on edge devices and scalable cloud storages like S3, and BigQuery, etc.

c. Transformers, nearest-neighbor algorithms, quantization, and retention policies for quick retrieval and sub-millisecond searches.

- Learning & Adaptation Mechanism

The ‘evolving intellect’ of the system, this module improves the performance of Agentic AI as it completes one task after another. It allows the system to learn from experience and adapt to changing conditions. The AI system, thus, behaves dynamically and highlights adaptiveness instead of just acting as a reactive tool. Reinforcement learning plays a key role here by learning from past actions and choosing the best possible course of action in the current context.

The adaptation mechanism is the Agentic AI’s growth engine. Its adaptability can help CXOs cut retraining costs drastically and keep the system up to date & relevant.

Purpose:

a. Continuous learning and improvement by extracting patterns & insights from data and refining behavior.

b. Adapt to new scenarios without explicit retraining and/or overhaul.

c. Builds accuracy and long-term resiliency of the system.

Details:

a. Reinforcement, supervised, unsupervised, and online learning paradigms.

b. Performance assessment, model updates, and policy changes as per memory & feedback data and any environment changes.

c. GPUs, TPUs, high-compute cloud clusters like AWS SageMaker for processing.

d. Deep Q-Networks, neural networks, fine-tuned LLMs, clustering, etc., are key algorithms.

- Collaboration & Multi-Agent System

Here is the ‘team coordinator’ of the entire system, enabling every module and component to work in cohesion toward a shared or complementary goal. The module coordinates interactions and joint operations of the different agents in the Agentic AI system. This ensures that the Agentic AI system can tackle complicated and distributed tasks.

The collaboration and multi-agent system is the scalability booster. It ensures all components and agents in a multi-agent system complement each other, can work at scale efficiently, and handle enterprise-grade challenges.

Purpose:

a. Aligns and coordinates agent actions.

b. Manages resource sharing.

c. Breaks big tasks into smaller subtasks and ensures distributed problem-solving.

d. Links memory across agents and enables joint reasoning, and coordinate execution, thereby, lending autonomy to the entire system.

Details:

a. Communication protocols (MQTT, gRPC, etc.), coordination mechanisms (Game Theory, Swarm Intelligence, Consensus Algorithms, etc.), graph algorithms, swarm optimization algorithms, distributed reinforcement learning for synchronization and negotiation among agents.

b. Low latency nodes at the edge and distributed cloud clusters.

- Security & Ethical Guardrails

The ‘gatekeeper and moral compass' of the agentic AI system, the guardrails ensure everything operates safely, securely, and ethically. This layer protects against cybersecurity threats while enforcing fairness, accountability, and compliance.

The security and ethical guardrails are the trust anchor of the agentic AI system as they safeguard against and cut legal risks.

Purpose:

a. Monitors all layers of the entire system and feeds corrections.

b. Ensure no layer goes rogue, balancing freedom with responsibility.

c. Protects the system from attacks, and makes sure everything’s fair, transparent, and ethical.

d. Enforces regulations like GDPR, HIPAA, etc.

Details:

a. Trusted platform modules on-edge device and hardware security modules on the cloud.

b. XAI, anomaly detection, and fairness algorithms such as demographic parity.

The eight layers above work together to make Agentic AIs incredibly versatile, and capable systems that can disrupt and reshape how businesses operate. Agentic AIs are already driving transformations and innovations across industries. But how does this translate to your sector and specifically your business? Let us explore how this tech is rewriting operations across industries as well as the best practices that led to successful implementations & transformations.

• How Agentic AI is Transforming Industries?

The incredible autonomy and adaptability of agentic AI systems are the biggest game changers. Unlike traditional AIs and LLMs, these systems operate with a goal-oriented mindset, plan & reason toward the best course of action given a certain context or circumstance, conduct complicated, multi-staged tasks, and do everything without any direct human intervention.

■ Agentic systems run state-of-the-art models that are fast, efficient, accurate, and capable of delivering results in real-time or otherwise.

■ High processing power, learning & knowledge-retention abilities, superb collaboration & adaptiveness enable agents to collaborate autonomously with minimal direct human intervention, thereby boosting productivity.

■ Agentic AIs can assess their performance, identify and rectify mistakes, and improve performance with every task & workflow.

■ Multi-agent systems can manage an array of repetitive, resource-intensive tasks independently. This allows human workers to focus more on managerial, problem-solving, and strategizing while agents work on their behalf, behind the scenes.

■ Data-enabled decision-making, high scalability, and amazing adaptability make agentic AI systems ideal for specialized applications. With the right resources, companies can design bespoke agents that can carry out complex and unique tasks.

Agentic AIs are poised to become perfect sidekicks for humans, augmenting their capabilities and supercharging workflows. They ARE the future of human-machine collaboration as evident from the plethora of applications/use cases below.

• The Biggest Agentic AI Use Cases in 2025

- Automotive Industry

Self-driving cars are all the rage nowadays with the rise of Tesla, Waymo, and BYD upending global automotive markets. Exceptionally capable multi-agentic AI systems are behind the rising popularity of autonomous vehicles. From scrutinizing real-time traffic patterns, revising routes, and maneuvering obstacles without any human intervention, their ability to oversee safety-critical systems such as cars signals the excellence of agentic AI.

- Manufacturing

Siemens Robotics,’ Volkswagen’s, or Foxconn’s assembly lines – multi-agentic AI systems help optimize production, quality control, packing, logistics, and every key facet of their manufacturing workflow. Multiple agents manage and optimize different production stages while coordinating with one another. The result --- huge productivity boosts, better quality control, and industry-compliant operations.

- Customer Service

Agentic AI enhances traditional and even automated customer service bots with its dynamic, self-learning nature. Agents can capture customer intent quickly, recall from memory, and take independent steps to resolve queries & problems. They can manage tedious and routine tasks as well as complex tasks like resolving conflicts, partaking in complex interactions and/or transactions, or predicting customer behavior. Conversational agents with goal-oriented behavior, agent assist systems, omnichannel agents, and complex workflow automation – agentic AIs are essaying a variety of roles in the customer service domain with resounding success.

- Health & Social Care

Technology is a crucial cog in modern healthcare. CRISPR gene editing, 3D printed organs, artificial insemination, targeted cancer therapies -- remarkable innovations have transformed the domain in ways previously inconceivable. The agentic era is set to bring about another major revolution. Powered by LLMs and multi-modal agents, agentic AIs are processing massive datasets and delivering pinpoint actionable insights with urgency. The proactive, adaptable, and goal-driven nature of agentic systems can help alleviate cognitive burdens on doctors, augment workflows, and boost resource efficiency drastically.

- Finance

According to an article by the Citi Group, the financial services sector is the second largest consumer of generative AIU technologies after telecommunications and media. This trend is expected to continue with agentic AI as financial bodies like banks, NBFCs, etc., explore and look forward to implementation across myriad use cases. From customer relationship management, risk and underwriting, & fraud prevention to financial forecasting, onboarding, and operational efficiency, agentic AI use cases are as diverse as they are vital.

- Logistics & Supply Chain

Globalization, a burgeoning population, and rising customer expectations have increased supply chain complexities, exponentially. Agentic AI systems can accelerate logistical processes through highly effective & adaptive management solutions by improving efficiency, saving costs, driving data-driven decision-making, and enhancing agility. For consumers, agentic AI can enable real-time tracking, provide tailored recommendations, simplify processes, and solve problems proactively 24/7.

• Conclusion:

Agentic AI systems have the potential to transform human-machine collaboration. Their incredible abilities have the potential to drive growth by efficiency and business growth to greater heights. However, agentic AI development is still at a nascent stage and there is a lack of effective practical insights regarding how Agentic AI systems solve traditional workforce challenges. To attain greater specialization, innovation, and trustworthiness, businesses need to establish clear goals, formulate strategies, and define appropriate operational & decision-making boundaries given the diverse range & immense capabilities of agentic AI applications.

The rewards of leveraging Agentic AI can be great but so are the risks – the potential of greater productivity & innovation comes with the potential for high bias, inaccuracies, and inappropriate use. Prompt action and systemic strategizing by business & government leaders can set the right course for successful agentic AI development and implementation.

• How Can ITPN Help?

ITPN has leading-edge capabilities and top-class expertise in AI solutions development, MLOps, and AIOps. We have exceptionally skilled developers, system engineers, and managers in the domain who can deliver excellence across all levels and help businesses attain better ROI. Connect with us to learn more about what we offer or for any kind of assistance regarding our services.