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    Everything you need to know about Agentic AI

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    • Aishwarya Saran
      Without data you are just another person , with an opinion.
    22-January-2025
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    • AI
    • Gen AI
    • Analytics Consulting

    Editor’s note: Agentic AI is here to change the game! From agents that handle vendor quotes to those managing complex workflows, this technology is changing how businesses operate. With major companies like Google and NVIDIA pushing the boundaries, the shift toward Business-to-Agent (B2A) relationships is no longer a sci-fi fantasy—it’s happening right now.

    Wondering how Agentic AI systems might impact your work? Keep reading to learn more!

    The Journey of Agentic AI

    Right now, what we are witnessing is a major AI leap. And honestly it feels straight out of a sci-fi novel where tech isn’t just a tool but an awesome sidekick in action. (Hi J.A.R.V.I.S. is that you?).

    However, if you’re feeling a sense of déjà vu with all the thrill around these 'new' technologies, you’re not alone. In recent years, there’s been a lot of buzz around innovative advancements in GenAI. But this time, the excitement is absolutely justified! To understand why, let’s start by understanding what’s agentic ai vs generative ai.

    Agent Evolution
    Discussion of Agentic Evolution in Microsoft AI horizon

    And Google’s release of Gemini 2.0 and NVIDIA's AI Blueprints platform further puts the icing on the cake by validating the directions of agentic growth by providing enterprise-grade infrastructure for agentic ai systems. These aren’t small updates because they signal a major shift in how AI will be used in an enterprise environment. So, fasten your seatbelts to get its core principles and explore how it can revolutionize your workflow. Welcome you to the world of Agentic AI.

    What is Agentic AI?

    Agentic AI refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and using available tools in a given environment.

    Let’s understand this better with an example. An inventory rebalancing agent, for example, can continuously monitor inventory levels across stores and warehouses. Once inventory hits a low point, the agent can step in and find the best vendors using vendor, request quotes, and compare them based on price, quality, delivery, and reliability.

    It even provides a report with recommendations, so you can approve the order quickly. This increases the flexibility and efficiency of inventory and procurement processes. What makes Agentic AI so powerful is that it doesn't just draw from databases and networks—it learns from user behaviour over time. This makes it more effective at handling complex, multi-step tasks.

    This means that as users engage with these AI agents, the agents become more attuned to their preferences and behaviours, enabling them to handle complex, multistep tasks more effectively.

    But how exactly does this happen?

    Into the brain of Agentic AI: Agentic AI Frameworks

    Now as mentioned above; to reach a level of autonomous decision-making and action, agents draw upon a combination of various technologies. These techs include machine learning, natural language processing, and automation. Now this intricate blend allows the AI to understand and respond to complex situations in a way that mimics human-like reasoning and adaptability.

    For better understanding let’s divide the functioning into four steps.

    agentic ai architecture
    Agentic AI architecture

    1. Perception Module: More Than Just Data Ingestion

    Unlike traditional AI systems or even LLMs that wait for human input, the perception engine actively combines and merges the information from multiple sources simultaneously. Think of it as the AI's sensory cortex with brings multi-modal processing to combine text, visual, and structured data through transformer-based architectures.

    For example, with the Inventory agent mentioned above, the perception engine would about keep a track of the inventory levels with access to the warehouse inventory data & flag the system when it goes down.

    2. Reasoning Core: The Strategic Brain

    Given the growing efficiency in the reasoning and planning capabilities of LLMs, they from the core of agents. It should be able to:

    • Break down objectives into manageable sub-goals
    • Identify potential roadblocks before they occur
    • Formulate alternative approaches when initial strategies fail

    Let’s go back to the example, formulating an action plan once the inventory levels have gone down, deciding on the suppliers, analyzing the possible timeline and shortlisting them etc. would fall under the purview of the reasoning agent

    3. Action Orchestration – Execution with Intelligence

    Traditional AI or even automations have a predefined workflow on which their systems work. But agents have the independence to take actions by being connected to multiple systems and tools. They maintain API connections with multiple systems simultaneously, implement sophisticated rollback mechanisms for failed actions, and take action based on the need.

    Going back to the example, this is the part where the agent places an order with the supplier or creates an approval from the user for the order (based on what’s defined as the action plan).

    4. Learning Subsystems: Beyond Simple Pattern Recognition

    This is where agentic AI truly distinguishes itself. It’s the feedback loop or the “data flywheel” where the data generated from its interactions is fed into the system to enhance models. Here’s how it works:

    Experience Prioritization
    Here algorithm identifies which experience are more valued for learning

    Continuous Model Updates
    Adapts behaviour based on success rates and changing conditions

    Cross-Task Learning
    Applies insights from one domain to improve performance in others


    Given that the frameworks and processes around Agentic AI are still new, these 4 processes form the basic structure of creating an agent. The complexity grows with more need, let’s dive into the evolution now.

    The Strategic Evolution of Agentic AI: A Roadmap to Implementation

    This evolution typically occurs (as you can guess) across three distinct stages, each representing a leap in capability and autonomy.

    Stage 1 – Task-Specific Agents

    In first stage AI agents are designed to handle specific, well-defined tasks within a clear environment and boundaries. At this level, agents operate within a microservices-based architecture, typically leveraging - event-driven processing pipelines, REST API for system integration, containerized deployments for scalability, real-time monitoring and logging infrastructure.

    In quality control, for example, these agents continuously monitor production lines through computer vision, making real-time decisions about product quality and adjusting manufacturing parameters automatically. They don't just flag defects – they learn from patterns to predict potential issues before they occur, effectively reducing waste and improving efficiency. Another example is the inventory agent bot discussed above.

    Stage 2 – Multi-Domain Coordination

    Now scaling the task-specific agents across multiple functions or domains to coordinate amongst themselves with the help of an “orchestrator agent”. This agent can act as the medium to manage the tasks across agents or to assign tasks between them.

    For example, if there’s a passenger trying to rebook a passenger flight, the orchestrator agent can connect between the flight, seating, meals, and baggage agents.

    agentic ai system as flight book agent
    Example of Agentic AI system as a flight book agent

    What’s really interesting about this setup is how the orchestrator agent plays a crucial role. It acts as a vital link, making sure that each specialized agent can shine in their area of expertise. By doing this, it helps to minimize errors—what we often call "hallucinations" in the AI terms.

    And this is just the example, there’ll soon be agents that can connect between supply chain, procurement, finance, and customer service too.

    Stage 3 – Agents as an ecosystem

    Now taking the task specific agents outside its organizations’ boundaries is what makes this stage the most advanced (and ideal) agentic AI stage. Like in previous stages we saw how we built upon the user to agent capabilities. But this stage takes it a level up by introducing an interbot interaction ecosystem. Where our agents are not only working in User-to-Agent or Business-to-agent environment; It’s also working on Agent-to-Agent level.

    Now considering how this enables a multibot workflow ecosystem makes it a total game changer in business context. Because when you look at it, your agent has now extended beyond interacting solely with internal data—they actively engage with external stakeholders, such as customers and business partners, collecting real-time data and making decisions autonomously.

    What’s Next for Agentic AI

    We are seeing the history in making. While we are still at a nascent stage, things are advancing quickly. Its anticipated that by 2028, nearly a third of businesses will be integrating these AI agents into their daily operations. That’s just around the corner!

    Think of it this way: we're moving from having digital assistants to having digital colleagues. While it might sound a little farfetched at the point, organizations that wait too long to adapt might find themselves playing catch-up. So, whether you're just curious about what a task-specific agent could do for your customer service, or you're ready to dive into complex multi-domain solutions, now's the time to start exploring.

    And that's where we at Polestar Solutions come in. We're not just implementing technology; we're working on building such agents to help organizations navigate this new landscape with confidence and purpose. So, seize this opportunity to take action now before the chance slips away and your competitors gain the upper hand.


    About Author

    guide to understand agentic ai
    Aishwarya Saran

    Without data you are just another person , with an opinion.

    Generally Talks About

    • AI
    • Gen AI
    • Analytics Consulting

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