What is Agentic AI and How is it Different From AI Agents?

Think about your GPS. When you tell it where you want to go, it gives you directions. It just guides you. Now, consider a self-driving car. If you tell it the same, the car will analyze the traffic, road, and weather conditions and suggest the best route. It will drive itself and take you there.

Agentic AI is a lot like self-driving cars. It takes action, makes decisions, and moves toward your goals. Unlike traditional AI solutions, which need human supervision and help, agentic AI works independently. But how exactly does it work? And more importantly, how can your business benefit from this cutting-edge technology?

What Does Agentic Mean in AI?

In the context of AI, "agentic" refers to the ability of an AI system to act autonomously and make decisions on its own, rather than merely responding to predefined commands or following rigid instructions. Going by this meaning, the definition of agentic AI is simple. It is an AI designed to pursue specific goals, learn from its environment, and take proactive actions to achieve those goals.

Let's take the example of the supply chain where traditional AI is used. Tools for inventory optimization might track inventory and alert someone when supplies are low.

But agentic AI doesn’t just track. It predicts demand based on what your customers are expecting next, adjusts inventory and even reorders supplies as needed, all on its own. It learns from the data, adapts to the situation, and moves forward.

In short, agentic AI goes beyond reacting. It makes decisions that help businesses achieve long-term goals, just like a human would, but faster and more accurately.

How is Agentic AI Different?

These core features make agentic AI stand out from the crowd.

1. Goal-Oriented Autonomy

Agentic AI isn’t just a tool that reacts to inputs. It has a clear goal and works toward it. For example, in healthcare, an AI system might start by gathering patient data but then decide to shift to recommending the best treatment based on that information. It knows what it needs to do and makes decisions accordingly.

This goal-oriented autonomy allows AI to handle complex tasks independently, reducing the need for human oversight.

2. Adaptive Learning

Agentic AI gets smarter with time. It learns as it gathers more data and experiences. The more it encounters, the better it becomes at making decisions.

For example, a predictive maintenance system in a factory might begin by noticing equipment failures based on temperature readings. Over time, as it learns from new data, it can predict failures more accurately, adjusting its models to become even more reliable.

The ability to adapt ensures that agentic AI is always improving, making it more efficient and effective the longer it operates.

3. Multi-Agent Collaboration

In many situations, no single AI can handle everything. That’s where multi-agent collaboration comes in. Multiple autonomous agents can work together, each handling part of a task to achieve a shared goal.

For instance, traffic control systems, public transportation AI, and environmental monitoring systems work together in a smart city. Each system has its goal like reducing congestion, improving efficiency, and reducing pollution. By working together and sharing real-time data, these systems can achieve far better results than any one of them could alone.

4. Ethical Guardrails

With autonomy comes responsibility. How can we ensure agentic AI makes ethical decisions? By building ethical guardrails into its design. These guardrails ensure the AI avoids harmful decisions, like reinforcing bias or making unfair choices.

In healthcare, for example, an AI might recommend treatments. It must be free of racial, gender-based, or socio-economic biases. The AI must be constantly monitored, tested, and audited for Fairness to ensure this happens. These ethical guidelines ensure autonomous systems benefit everyone without causing harm.

Where Will Agentic AI Make an Impact?

1. Healthcare

Agentic AI's ability to adapt to different environments, understand human emotions, and offer empathy makes it perfect for roles that require soft skills—like healthcare and caregiving.

Hippocratic AI, a California-based healthcare company, is leading the way. They’ve created a lineup of AI agents, each designed to support different aspects of healthcare and social services.

One of these agents is Sarah. An agentic AI programmed to engage with patients in a friendly, caring, and approachable way. Sarah can also check in with patients, ask how their day is going, organize meals and transport, and even send reminders for medication.

Judy, another AI agent, helps patients navigate pre-operative procedures. Judy can remind patients about their appointment times and locations, provide guidance on fasting before surgery, or alert them about which medications to stop taking.

2. Manufacturing

Agentic AI can manage everything from production lines to product customization and design.

One notable example is Juna.ai, a German AI startup pushing AI's boundaries in manufacturing. Juna.ai deploys a variety of AI agents to manage virtual factories. With Juna, they aim to maximize productivity, improve quality, and cut down on energy consumption and carbon emissions.

These AI agents are tailored to specific tasks, such as production agents that oversee the assembly process and quality agents that ensure each product meets high standards.

3. Customer Support

Unlike traditional automated customer service bots, which follow rigid scripts and offer limited responses, agentic AI-powered customer service agents are far more dynamic and intuitive.

A startup, Ema, based in California, is implementing this. Ema’s agentic AI chatbots can search through vast amounts of data from multiple databases and applications to provide relevant answers to customer queries.

Moreover, these chatbots learn from each customer interaction, continuously improving their responses and suggesting better solutions for human agents.

Ema also ensures that their AI constantly reviews its content for accuracy and compliance, making adjustments as needed. Additionally, the AI provides recommendations for improving the company’s knowledge base, ensuring customers get the most up-to-date information.

Challenges: The Hurdles to Overcome

While agentic AI is incredibly powerful, it comes with its own set of challenges. Here’s what 

businesses need to keep in mind:

1. Bias Amplification: Risk of Reinforcing Inequities

AI learns from data, and if that data is biased, the AI can reinforce those biases. For instance, if an AI is trained on biased hiring data, it may favor certain groups. This could lead to unfair outcomes in critical healthcare, finance, and hiring sectors. To address this, businesses must ensure their AI systems are trained on diverse, unbiased data and regularly audited to prevent bias.

2. Accountability: Who’s Responsible for AI’s Actions?

If an AI makes a mistake, who’s responsible? This question becomes crucial in areas like healthcare, where poor decisions can have serious consequences. Companies must establish clear lines of accountability, ensuring that the developers and users of AI systems are held responsible for their outcomes.

3. Regulatory Challenges: Navigating New Laws

As agentic AI becomes more widespread, governments are introducing new regulations. These rules focus on transparency, privacy, and safety. Companies must avoid these changes to ensure compliance, as different regions may have different laws. For businesses, staying on top of evolving regulations is crucial to avoid legal issues.

Agentic AI vs AI Agents: Understanding the Difference

AI agents were the recent advancement in AI but no longer after the introduction of agentic AI. Both sound similar in many ways but are different? While both are autonomous to some degree, key differences set them apart.

AI Agents: Reactive with Limited Autonomy

AI agents are typically designed to perform specific tasks based on predefined rules and data inputs. They can operate independently to some extent but usually require human guidance or intervention to perform tasks beyond their basic function. For example, a customer service chatbot is an AI agent. It responds to queries and gives answers but doesn’t make any proactive decisions.

In essence, AI agents are reactive. They follow instructions but cannot set goals or adapt significantly over time.

Agentic AI: Goal-oriented and Proactive

On the other hand, agentic AI is fully autonomous. It doesn’t just respond to inputs; it makes decisions and drives toward specific goals. This type of AI is self-learning, adaptive, and capable of adjusting its strategies based on changing circumstances or new data. Unlike AI agents, which may only handle one part of a process, agentic AI systems can evaluate entire environments, set goals, and take actions to meet those goals without ongoing human oversight.

To put it simply, agentic AI goes a step beyond AI agents. While AI agents can perform tasks with limited autonomy, agentic AI can make complex, self-directed decisions. It makes choices that align with its objectives and evolve.

How to Prepare Your Business for the Age of Agentic AI 

To prepare for this future, companies should:

• Invest in talent: Hire AI experts and data scientists who can build and manage these systems.
• Build ethical frameworks: Establish guidelines to ensure AI makes responsible, fair decisions.
• Stay informed: Keep up with regulatory changes to ensure compliance.

Agentic AI is a powerful tool. But, like any technology, it needs to be handled responsibly. By doing so, businesses can reap the benefits of a smarter, more efficient future.

FAQ: Common Questions About Agentic AI

Q: Is agentic AI the same as AGI (Artificial General Intelligence)?

No, agentic AI focuses more on specialization. It’s designed to perform specific tasks and make decisions within a set framework. AGI, however, could perform any intellectual task a human can. While agentic AI can make decisions autonomously, it focuses on specific areas.

Q: How can businesses prepare for agentic AI adoption?

Start by identifying areas where agentic AI can improve decision-making. Invest in AI infrastructure, ensure strong data governance, and build a culture of ethics and transparency.