Over the past few years, we have been completely mesmerized by the vast potential of Generative AI. It has been able to demonstrate the incredible ability to write poems, debug programming code, and create images seemingly out of nothing. However, as intelligent as Generative AI may be, it continues to remain largely passive and will only provide a response to the input provided to them. It has been designed to ‘wait’ for the user.
- What is Agentic AI?
- Transforming Workflows and Decision Making
- The Rise of Asynchronous Management
- Data-Driven Decisions at Speed
- Industries on the Brink of Revolution
- Healthcare: From Triage to Treatment Planning
- Finance: Active Risk Management
- Marketing: the End of a/b Testing?
- Benefits of the Agentic Approach
- The Challenges We Face
- The Alignment Problem
- Consequences of Hallucination
- Integration Challenges
- The Future: Human-Agent Collaboration
- Conclusion
But that is a dynamic that is quickly changing.
We are in the process of entering a new epoch in technological advancement, Agentic AI. A completely new generation of technology that is now fully capable of operating without human input and, instead, takes autonomous actions. It can perceive its surroundings, reason through its environment, and develop action plans to fully execute the tasks needed to be accomplish its intended purpose. It is the difference between a simple chatbot that provides instructions on flight booking, to a fully autonomous agent that takes the initiative to book the flight for you, add it to your calendar, and email your itinerary to your family.
This new advancement from simply “chatting” to now “doing” is not an “incremental improvement.” It is a complete paradigm shift to how an entire business will function.
This shift is part of our broader AI news and industry trends, where we track how emerging technologies reshape real-world systems.
What is Agentic AI?
As with any revolution, distinguishing between models and agents is crucial.
A Large Language Model (LLM) is a knowledge engine that can predict what word is likely to come next in a sequence. An AI Agent, however, “wraps” that model in a system that gives it “arms and legs” – typically some combination of API access, web browsing, and memory.
Consider AI that operates in loops. Given a high-level objective, such as “analyze our competitor’s pricing and suggest how we should adjust our strategy,” it goes beyond generating a bland list of advice. Instead, it:
Plans: outlines sub-tasks for the goal (scraping a website, comparing data, accessing pricing data internally).
Acts: leverages software to collect the relevant data.
Observes: monitors the outcomes of its actions.
Refines: When data is unstructured, blocked, or messy, it changes its approach.
Executes: makes the necessary changes or submits a final decision. This autonomy, the defining feature, is a significant shift from software acting as a tool we wield, to software as a worker we manage.
Transforming Workflows and Decision Making
Integrating agentic AI improves workflow efficiency in a way different to traditional automation, such as robotic process automation (RPA). Bots follow a set of linear rules, meaning that if a button moves three pixels to the left, the bot will break.
Agentic AI, on the other hand, is adaptable. Understanding context helps organizations to reshape their thinking about decision-making.
The Rise of Asynchronous Management
Currently, managers spend a great deal of time synchronizing with their teams to make sure the right tasks are getting done. With agentic AI, you can set a goal for an agent and analyze the outputs later. This diminishes human work from “execution” to “supervision.” People are the architects of the workflow, and agents take care of the structural engineering.
Data-Driven Decisions at Speed
Unlike human analysts, agents have the ability to process continually shifting large volumes of data. In the context of supply chain logistics, an agent can identify a weather pattern that will delay a shipment, pinpoint alternate routes, assess the cost of each, and even re-adjust the shipment before a human manager finishes their morning coffee.
Industries on the Brink of Revolution
Although all sectors will experience the effects of agentic AI, three in particular are experiencing rapid change.
Healthcare: From Triage to Treatment Planning
Administrative burden has been a major issue in healthcare. On average, providers spend as much time with EHRs as with their patients.
Agentic AI can relieve some of those administrative tasks.
Intelligent Triage: An agent can obtain patient symptom data, do a preliminary cross-review of the patient’s medical history, and assess the urgency of the appointment, beyond just a first-come, first-served basis.
Autonomous Follow-up: Post-op, an agent manages the patient’s self-reported data (through wearables/apps). If any vitals are outside the safe zone, the agent promptly informs the care team to avoid a readmission.
Finance: Active Risk Management
The use of algorithms in the financial sector is not new, but agentic AI is able to use reasoning as well as data.
Fraud Detection and Response: Legacy systems analyze and flag fraud, then send it to humans. An agent can analyze the context. If an agent detects a suspicious charge, it can cross-check the user’s location, the last few logins, and temporarily freeze the card while making a verification call, completing the security loop.
Personalized Wealth Management: Agents can manage portfolio investments by tracking global news. If a global event adversely affects a sector, the agent can rebalance a portfolio to fit the customer’s balance net and risk tolerance without having to do a quarterly review.
Marketing: the End of a/b Testing?
Marketing is a guessing game. With agentic AI, it becomes a real-time optimization science.
Autonomous Campaign Management: Rather than a marketer adjusting bids on Facebook or Google, an agent continually monitors ad performance. It automatically reallocates budget to ads that are converting well from those that aren’t.
Hyper-Personalized Journeys: Agents can design individualized customer experiences. For example, if a customer leaves the pricing page without converting, an agent can target that customer with a discount offer, a case study, or a demo conversion request email based on their behavioral history, instead of a generic campaign.
Benefits of the Agentic Approach
Why are marketers adopting these systems? The ROI relies on three main factors:
Intelligence Scaling: It’s hard to duplicate your best employee, but you can create a thousand copies of an AI agent that replicates the decision-making of your best employee.
Mental Offload: Multi-step tasks are complex and cognitively demanding. Delegating these tasks to agents allows human employees to conserve cognitive resources that can be utilized for more higher-level thinking such as creative problem-solving and relationship building.
Constant Productivity: Agents can work endlessly without tiring, losing focus, or needing to take breaks; unlike humans, they can work as focused machines.
The Challenges We Face
While there is potential in automation, the transition to a future with agents will not be seamless. Most modern automation is not as simple as setting the system and forgetting it.
The Alignment Problem
The primary concern with automation is the lack of control associated with aloof decision-making processes. Consider the process of more intelligently managing operational resources and assests. If you simply instruct the system to “maximize profit” without explaining all the conditions and outlining successful outcomes, the automated process could potentially fire all the employees and sell the office chairs. This is what we call the alignment problem which stems from vague objectives and low-level ethical reasoning.
Consequences of Hallucination
When a chatbot makes up answers, we can just think of it as a mildly annoying system that lacks sophisticated interactions – like when a system calls you to check in on your last purchase and misses the point of your question. An operational system that interacts with the world (via APIs) and is programmed to take action, can do a lot of damage to an organization. It can delete a crucial part of the system, execute a sale at a loss, or provide incorrect advice based on a decision that lacks human logic. Therefore, it is important to have a person supervising the system in a defined role.
Integration Challenges
The majority of enterprise applications are not designed for artificial agents. Older systems that lack an application programming interface (API) create barriers that are impossible for the agents to bypass. In order for companies to enable agents to have access to necessary tools, they will need to upgrade their technology systems.
The Future: Human-Agent Collaboration
Artificial Intelligence (AI) is not fully going to take over the workforce, however, a mixed model to include AI is the obvious future direction. The most accomplished practitioners of the next ten years will master the ability to manage a ‘squad’ of AI agents.
The changing of skill sets has already begun. As a result, prompt engineering will develop into Agent Orchestration. Interacting with AI to write code will be minimal. More likely, you will architect a process where one AI agent writes code, another reviews the code to identify any security weaknesses, while a third one writes the code documentation.
Conclusion
The Agentic AI revolution is moving the goalposts. We are no longer instructing computers to calculate, compute and communicate; now we require them to take actions. This provides business with all new levels of efficiency and agility that have never been possible before.
These ideas are already visible in real-world agentic AI deployments, where systems operate with increasing autonomy.
Nevertheless, there are new processes that upset the way you do business in order to capitalize on the new tools. Start with as little as possible and look for repetitive, multi-loop sequences in your processes and workflows. Those are the most suitable for your first AI agents.
The debate regarding AI has been around what AI can say. The debate has now changed to what we now wish to empower AI to do for us.
