Think: AI powered agents are making your workplace responsibilities easier, getting you real-time insights of work-related calls and helping you track the performance of agents at work. Convenient, isn’t it?
These are just some of the possibilities opened by the agentic AI in the present era. In fact, according to Gartner’s technology trends for 2025, adaptive AI systems — including agentic models — are expected to play a central role in transforming enterprise operations.
So, what’s more? Probably an ecosystem at work powered by adaptive AI agents capable of evolving and autonomously optimizing business processes to ensure smarter business decisions.
While the scope of traditional agentic AI assistants was limited and role-based, the present offerings are empowered to act on independently. But what exactly is agentic AI? The answer lies ahead!
Agentic AI: overview
Agentic AI refers to AI systems and models trained to act autonomously to achieve goals without constant human intervention. These agents have in-built capability to understand the goal of users and the right context of the problem ensuring an output aligned to primary vision. There are innovative tech stacks at the back of the system including NLP and ML helping it to act autonomously.
Curious about how agentic AI works? Here is the deal!
Before we can understand how agentic AI works, it’s important to grasp the underlying agentic architecture. At its core, agentic AI learns continuously from feedback as it performs various tasks. This architecture shapes how AI agents behave and enables them to adapt to different situations. As a result, they can act autonomously, interact with diverse systems, and make informed decisions based on ongoing data processing.
Agentic AI operates in a similar pattern as humans think, process information, and act. The architecture of agentic AI harnesses the following capability ensuring operations in a dynamic way:
- Goal-orientation: The ability to define objectives and formulate actionable strategies to achieve them.
- Real-time Monitoring and Adaptation: Continuously tracking performance and making timely adjustments as needed.
- Learning and Improvement: Using feedback to refine actions and improve behavior over time.
These elements help agentic AI systems to handle complex tasks and operate autonomously in a real-world setting. Agentic AI thus can easily integrate with various data sources through APIs ensuring it has the right information to deliver an informed decision.
Although agentic AI systems leverage the creative capabilities of generative models like ChatGPT, they distinguish themselves in several key aspects.
Agentic AI Vs generative AI Vs traditional AI: How do they differ?
Aspects | Agentic AI | Generative AI | Traditional AI |
Objective | Focuses on task execution and decision-making | Works by offering text, images and videos as per the prompt given | Trained to work on pre-defined rules. |
Autonomy | Based on real-time data, agentic AI can independently perform tasks | Requires human guidance and prompts to generate output in the form of text, image and videos | Relies on pre-programed algorithms to perform tasks. |
Capability | Capable of interacting and adapting with new information and internal system | Capable of offering creative output but lacks decision-making | limited adaptability and no autonomous goal-setting. |
Knowledge base | Learns from real-time feedback, uses reinforcement learning (RL) for autonomous decision making | Improves its content generation capability through reinforcement learning from human feedback (RLHF) | typically learns in a static, supervised, or rule-based way, without real-time adaptation |
Integration | Easily integrates with 3rd party systems, CRMs | Interacts with creative tools, CMS ensuring enhanced content creation | Interacts in a task-specific manner. It responds to inputs based on training or rules (pre-defined) |
Use case of agentic AI in real world
Streamlining operations in HR and recruitment
The recruiting industry continually requires shortlisting, interviewing and finalizing candidates. This requires an advanced level of scrutiny. Ensuring the tasks are being performed as intended requires a lot of effort. This is where agentic AI comes to bridge the gap.
The new age agentic AI can shortlist profiles depending upon specification of a role. It can get recruiters the advantage in the form of added bandwidth for performing other jobs as agentic AI can take the entire responsibility of shortlisting profiles and candidates while helping recruiters with better profiles ready to go through the interview process.
Agentic AI for customer service (call centers)
Call centers on average have 20-30 calls per hour. It is possible for human agents to miss critical information while taking all these calls in one go. There might be scenarios when human agents are not able to attend all these calls resulting in a bunch of unsatisfied customers.
In such situations, agentic AI can assist by analyzing a large volume of calls while extracting key insights and identifying critical information that might otherwise be missed. By doing so, it supports human agents in making informed decisions and ensures better customer experiences even with higher call volumes.
Enhancing sales outcomes
Agentic AI can significantly enhance sales processes by automating routine tasks and analyzing customer interactions. It can identify buying signals, track customer sentiment, and suggest the most effective next steps based on historical data and ongoing conversations.
By handling repetitive tasks like follow-ups, data entry, and lead qualification, agentic AI frees up sales teams to focus on building relationships and closing deals, ultimately improving both efficiency and conversion rates. Moreover, in-depth call analysis can help sales teams with better customer insights to win over complicated deals.
Agentic AI for internal employee training
Agentic AI can serve as an internal intelligence system across multiple industries. It is especially valuable for enterprises focused on training their workforce. Training modules can be customized and delivered through role-based access. This ensures employees receive relevant, job-specific knowledge efficiently. Agentic AI also supports smooth onboarding for new hires. Additionally, it assists customers in real time by addressing their queries effectively.
In-depth call Insights and analysis
Agentic AI plays a crucial role in generating deep insights from customer calls. The calls are being automatically analyzed for sentiment and recurring issues. A deeper call insight can detect patterns that help to find out areas of concern while also highlighting opportunities for improvement. By providing summarized call notes agentic AI enables businesses to better understand customer needs, improve agent performance, and drive more informed decision-making across support and sales teams.
Managing insurance claim processes
Agentic AI can rewrite the rules of insurance industry by shifting all the manual work to automation. Right from processing claim to handling the final payout, the entire process can be streamlined and managed in a better way. Automation at work can reduce errors and time while ensuring a smooth experience for customers. AI-powered agents can instantly access the validity of a claim while communicating in an empathetic manner. This ensures delivering a highly personalized service along with reducing the administrative burden on human agents.
Signing off
The recent advancements in AI have already paved the way for various industries to add agentic AI potential in businesses. Since AI agents are capable of autonomous decision making, the future holds the promise of AI agents that act with greater independence and context-awareness.
Whether it’s streamlining recruitment, personalizing healthcare, or optimizing customer service, agentic AI is at the core of a smarter, more autonomous enterprise.
