Agentic AI

March 10, 2025

This session explores the emerging field of Agentic AI, focusing on autonomous systems designed for complex tasks. It differentiates Agentic AI from traditional and generative AI by emphasizing its adaptability, decision-making, and self-sufficiency. The authors, Acharya, Kuppan, and Divya, examine Agentic AI’s foundational concepts, methodologies, and applications across healthcare, finance, and adaptive software. The study also addresses ethical considerations, advocating for the safe and responsible integration of Agentic AI into society. The paper aims to provide a structured understanding of Agentic AI for researchers, developers, and policymakers. Ultimately, the survey serves as a guide for navigating the transformative potential of this advanced AI paradigm.

Speaker	Transcript
spk_0	Right, so, uh, let’s dive into agentic AI. Um, as data scientists, you guys are already working with machine learning, neural networks, LLMs, all that good stuff. But have you heard of agentic AI?
spk_1	Well, it’s really captivating, you know, Agentic AI represents a significant departure from classical AI systems. You know, your rule-based algorithms or expert systems, they’re very effective within a predefined set of rules, but when you introduce novelty,
spk_1	Like a dynamic environment, they tend to
spk_1	struggle.
spk_0	Yeah, and that’s where that agenic part really comes in. It’s designed to handle the real world where things are constantly changing.
spk_1	Exactly. Imagine a self-driving car. A traditional AI might rely on preprogrammed routes. An agentic AI, on the other hand, it can adapt.
spk_1	To unexpected detours, construction, even pedestrians, all while ensuring a smooth and safe journey to your destination. OK,
spk_0	so that’s a cool example. But how does an AI even go about setting its own goals? How does it define what it wants to achieve?
spk_1	That’s a great question. Agentic AI leverages goal-oriented architectures.
spk_1	Think of it this way. A large, complex objective is broken down into smaller, more manageable subgoals. This enables the AI to strategize, to prioritize tasks, and to anticipate outcomes. It can even modify its plans as needed. So it’s
spk_0	not just reacting, it’s thinking ahead, almost like strategizing.
spk_0	Like a human would
spk_1	precisely. And a core technology driving this is reinforcement learning, or RL. I’m sure you’re familiar with the concept of AI learning through trial and error. Yeah,
spk_0	RL is basically like giving the AI a playground to experiment in, awarding good moves and penalizing the bad ones.
spk_1	Exactly. In the context of agentic AI, RL allows the
spk_1	Agent to learn directly from its interactions with the environment through rewards and penalties. It refines its actions and strategies over time, becoming more adept at achieving its
spk_1	objectives.
spk_0	It’s almost as if the AI is developing its own form of intelligence, figuring things out through experience.
spk_1	That’s an excellent way to put it. Agentic AI systems are designed for continuous learning.
spk_1	They evolved based on their experiences, improving their performance and adapting to the ever-changing dynamics of the real world.
spk_0	I’m starting to see how powerful this could be. We’ve got goal-oriented planning and this continuous learning through RL. But how does this translate into real-world applications?
spk_0	What kind of problems can agentic AI actually solve?
spk_1	The applications are incredibly diverse, and many are directly relevant to your work as data scientists. Let’s delve into a few examples.
spk_0	OK, hit me with some real world agenic AI
spk_1	action. Let’s start with something familiar, recommender systems. As data scientists, you’re already working with these, using machine learning to suggest products or content.
spk_1	Now imagine an agenttic recommender system, one that goes beyond simple suggestions. It could actively learn a user’s evolting preferences, predict their future needs, and even proactively seek new information to enhance its recommendations.
spk_0	So it’s not just about reacting to past data, it’s about anticipating future needs and actively seeking out new information to stay ahead of the curve.
spk_1	Precisely. This proactive, goal-driven approach is what sets agenttic recommender systems apart.
spk_1	They’re not passively suggesting they’re actively working to maximize user satisfaction.
spk_0	That’s really interesting. So how would the agentic structure actually work in this case?
spk_1	Imagine the agent has a set of goals, understanding the user, predicting their needs, and finding the best recommendations. To achieve these goals, it interacts with its environment. This includes user data, available products or content, and even external sources of information. So it’s
spk_0	constantly gathering information.
spk_0	Analyzing it and making decisions based on its goals and the evolving context.
spk_1	Exactly. And through reinforcement learning, it continually refines its strategies, getting better and better at predicting what the user wants and finding the perfect recommendations to keep them engaged. OK,
spk_0	that makes sense. What other real world use cases can you
spk_1	share? Let’s shift gears from recommending products to managing resources. Imagine a smart energy grid powered by agentic AI. It would constantly monitor energy demand, optimized distribution.
spk_1	And even interact with individual households to balance consumption and production in real
spk_1	time.
spk_0	Well, that’s a really powerful application. So how would the agentic structure work in this energy grid scenario?
spk_1	The agents’ goals would be clear ensure a stable and reliable energy supply. While minimizing costs and environmental impact, it would interact with a very complex environment, power plants, transmission lines, weather patterns, and even individual energy usage.
spk_0	It’s like a giant
spk_0	chess game.
spk_0	But instead of chess pieces, it’s managing energy flows, anticipating fluctuations in demand, and making strategic decisions to keep the grid balanced and efficient,
spk_1	precisely. And through reinforcement learning, it would constantly adapt, getting better and better at managing the grid’s complexities and ensuring a smooth and sustainable energy.
spk_0	You
spk_1	flow.
spk_0	I’m seeing a pattern here. Agentic AI really excels at managing complex systems with constantly changing variables.
spk_1	You’re absolutely right, and this adaptability extends to another fascinating area, financial markets. Imagine an A agentic AI system designed for algorithmic trading. It wouldn’t just react to market changes, it would anticipate trends, assess risks, and execute trade strategically to achieve financial goals.
spk_0	OK, this is getting interesting. So in this case,
spk_0	What would the agent’s goals and actions be? Its
spk_1	goals would be defined by its investment strategy, whether it’s maximizing returns, minimizing risk, or targeting specific sectors. Its actions would involve analyzing market data, identifying trading opportunities and executing trades autonomously, all while adapting to the constantly changing financial markets.
spk_0	So it’s like
spk_0	having an AI financial expert working around the clock, constantly analyzing data, making strategic decisions.
spk_0	And adapting his strategies to stay ahead.
spk_1	Exactly, and this is just a glimpse into the potential of agentic AI from healthcare to manufacturing, transportation to scientific discovery, the applications are vast and still being explored.
spk_0	So we’ve seen how agentic AI can be applied.
spk_0	But I’m curious about the actual implementation. How do we go from these high-level concepts to building a working system?
spk_1	That’s where things get exciting for data scientists like you. Implementing agentic AI involves a fascinating blend of technologies, with LLMs playing an increasingly central role.
spk_0	LLMs right. Those powerful language models that are revolutionizing natural language processing, how do they fit into this a agentic AI puzzle?
spk_1	Well, consider the core capabilities of an agentic AI. It needs to understand its goals, reason about its environment, make decisions and take action. Traditionally, these capabilities were achieved through symbolic AI techniques like problem solvers and
spk_1	planners.
spk_0	Symbolic AI, problem solvers, planners, these aren’t exactly mainstream concepts in data science. Can you break those down for us?
spk_1	Absolutely. Symbolic AI stands in contrast to machine learning.
spk_1	While machine learning learns from data, symbolic AI uses symbols and logic to represent knowledge and reason about the
spk_1	world.
spk_0	So it’s a more explicit and structured approach compared to the data-driven nature of machine learning.
spk_1	Exactly. Within symbolic AI we have problem solvers and planners. A problem solver. You can think of it like an AI detective. It figures out how to get from an initial state to a goal state.
spk_1	By applying rules and logic.
spk_0	It’s like solving a puzzle, figuring out the sequence of steps needed to reach the solution.
spk_1	Precisely. And a planner takes this concept a step further. It creates a detailed plan of action to achieve a complex goal, taking into account various constraints and potential
spk_1	obstacles.
spk_0	So it’s like an AI strategist, mapping out a course of action to reach a specific objective.
spk_1	Precisely. However, these traditional symbolic
spk_1	AI techniques. While powerful have limitations. They often struggle with the ambiguity and complexity of the real world, and they frequently require extensive handcrafted knowledge engineering. So
spk_0	they’re
spk_0	not as adaptable or scalable as data-driven methods like machine learning.
spk_1	Exactly. And that’s where LLMs come into play. LLMs with their remarkable ability to understand and generate human language, can bridge the gap between the structured world of symbolic AI.
spk_1	And the messy, unpredictable real world. OK,
spk_0	I’m
spk_0	starting to see how all this fits together, but how do LLMs actually enhance the implementation of agentic AI? Well,
spk_1	one
spk_1	way is by providing a more natural and flexible means of defining goals and constraints. Instead of relying on rigid symbolic representations, we can use natural language to describe
spk_1	What we want the AI agent to achieve.
spk_0	So it’s like giving the AI instructions in plain English, making it much easier for humans to communicate their goals. Precisely.
spk_1	LLMs can also contribute to reasoning and planning by leveraging their vast knowledge base and language processing capabilities. LLMs can help agentic AI systems to understand complex situations, anticipate
spk_1	consequences and even generate plans of action.
spk_0	It’s like
spk_0	giving the AI a team of advisors, providing insights and recommendations to support its decision making.
spk_1	That’s
spk_1	a fantastic way to put it. LLMs can even assist in the execution of actions, translating high-level plans into concrete steps that the agentic AI system can carry
spk_1	out.
spk_0	So it’s like having an AI project manager, breaking down complex tasks into manageable subtasks and ensuring that everything is executed smoothly, precisely.
spk_1	This collaboration between agentic AI and LLMs is unlocking incredible new possibilities for building AI systems that are truly intelligent and adaptable.
spk_0	This is
spk_0	all incredibly fascinating, but before we get too deep into the technical details, let’s take a step back and consider the bigger picture. What are the potential benefits and risks of this combination of agentic AI and LLMs?
spk_1	That’s a crucial question and one that deserves thoughtful consideration. The potential benefits are vast.
spk_1	From increased efficiency and productivity to personalized experiences and even groundbreaking scientific
spk_1	discoveries.
spk_0	So it’s like unlocking a whole new level of AI
spk_0	capability.
spk_1	Exactly. But as with any powerful technology, there are inherent risks. We need to be very mindful of issues such as bias, job displacement, and the potential for
spk_0	Right, because with great power comes great responsibility.
spk_1	Absolutely. So as we continue to explore and develop agentic AI and its integration with LLMs, we must proceed with a thoughtful and ethical approach, ensuring that these technologies are used responsibly for the benefit of
spk_1	all.
spk_0	I think that’s a great point to wrap up this part of our deep dive on. We’ve covered a lot of ground from the basic concepts of agentic AI.
spk_0	To real world examples and the possibilities of integrating it with LLMs.
spk_1	We’ve definitely laid a solid foundation for understanding this game changing technology.
spk_1	Welcome back. It’s great to continue our exploration of agentic AI. I’m particularly excited to delve deeper into the technical aspects, especially how agentic AI systems work with LLMs. It’s an area where we’re witnessing remarkable
spk_1	innovation.
spk_0	Yeah, definitely. Last time we talked about symbolic AI techniques like problem solvers and planners. They seem almost old school compared to all the hype around machine learning these days. But you mentioned they still play a crucial role, especially when combined with LLMs.
spk_1	You’re right, they might not be as trendy as deep learning.
spk_1	But symbolic AI techniques bring something unique to the table, a structured logical approach to thinking. That can be incredibly valuable for agentic AI. Think of it this way. LLMs are fantastic at understanding the nuances of language. They excel at extracting meaning from text, and even generating creative content, but they don’t inherently possess the ability to plan or solve problems in the traditional sense.
spk_0	So it’s like having a brilliant writer.
spk_0	Who can craft amazing sentences, but might not be the best at organizing a project plan or tackling a complex logic
spk_0	puzzle.
spk_1	That’s a perfect analogy. That’s where symbolic AI techniques come into play. They provide that structured framework for reasoning and planning, and when combined with the language understanding and generation capabilities of LLMs, it becomes a really powerful combination. OK,
spk_0	I see the synergy is starting to emerge. Can you give us a concrete example of how this works in a real world scenario?
spk_1	Absolutely.
spk_1	Let’s revisit the agentic recommender system we discussed earlier. Imagine it needs to recommend a gift for a user’s friend, someone the system doesn’t know much about. Now, an LLM could analyze social media posts, emails, maybe even shared documents to build a profile of this friend, understanding their interests, hobbies, even their current needs.
spk_0	OK, that’s pretty impressive.
spk_0	Using all that unstructured data to gain insights about someone.
spk_0	But how do we go from understanding a person to actually recommending a suitable gift?
spk_1	That’s
spk_1	where the problem solving aspect of symbolic AI comes in. The agentic AI system equipped with these techniques can utilize the information gathered by the LLM to reason about potential gift options, for instance, it might have a rule that says if the friend enjoys outdoor activities, Andy lives in a cold climate, then suggest a high quality winter jacket.
spk_0	So the LLM does the heavy lifting of understanding the person, and then the symbolic AI engine steps in.
spk_0	To apply logic and rules to arrive at a specific recommendation.
spk_1	Precisely. And here’s where things get really interesting. LLMs can actually be used to generate those rules themselves by learning from massive data sets about gift giving social trends and individual preferences.
spk_0	So the AI is constantly learning and evolving, becoming more sophisticated in its reasoning and decision
spk_0	making.
spk_1	Exactly. This dynamic interplay between LLMs and symbolic AI.
spk_1	Can be applied to a wide range of agentic AI tasks, from planning travel itineraries to managing complex financial portfolios. OK,
spk_0	this is starting to make a lot of sense, but let’s shift gears to practical implementation.
spk_0	As data scientists, what tools or frameworks do we need to build these agentic AI systems?
spk_1	That’s a great question. There are several key things to consider when building agentic AI systems. Firstly, you need a powerful LLM as your foundation. You’re probably already familiar with models like GPT-3 or BERT, but there’s a new wave of specialized LLMs emerging, specifically designed for reasoning and decision making tasks,
spk_0	right, because the LLM is essentially the brain of the system, handling all that complex language processing.
spk_0	But what about the symbolic AI side of things? Are there any specialized tools for that?
spk_1	Absolutely. There are quite a few frameworks and libraries specifically designed for symbolic AI development. Some popular options include Prolog, a logic programming language, and PDDL, a language for expressing planning problems.
spk_0	So Prolog and PDDL provide the tools for creating those problem solvers and planners, but how do we actually connect these symbolic AI components with the LLM?
spk_1	That’s where it gets a bit tricky.
spk_1	And it’s an active area of research. One approach is to use the LLM as a bridge between the symbolic AI engine and the real world, translating natural language goals and constraints into a format that the symbolic AI system can understand.
spk_0	So the LLM acts as a translator, converting human language into a language that the symbolic AI engine can work
spk_0	with.
spk_1	Exactly. Another approach is to use the LLM to generate the rules and knowledge base for the symbolic AI system.
spk_1	Learning from vast amounts of data and human expertise.
spk_0	It’s like training the symbolic AI engine with the help of the LLM, giving it a head start in understanding the domain it needs to operate in.
spk_1	Precisely. These are just two examples, and the field is rapidly aviing with new techniques and frameworks constantly emerging.
spk_0	It
spk_0	sounds like an exciting time to be working in this field. As data scientists, what skills should we focus on developing?
spk_0	To be successful in building these agentic AI systems.
spk_1	Well, a solid foundation in machine learning and deep learning is essential, of course, but you’ll also need to become familiar with symbolic AI techniques like logic programming and planning algorithms. So
spk_0	it’s about expanding our AI toolkit, adding those symbolic AI.
spk_0	tools to our existing machine learning expertise.
spk_1	Exactly. And never underestimate the importance of domain knowledge. Agentic AI systems are often designed for specific domains, so a deep understanding of the problem you’re trying to solve is crucial,
spk_0	right? Even those advanced AI can’t replace human expertise, and understanding of real world context. Absolutely.
spk_1	And finally, I’d say that creativity and problem solving skills are paramount. Building agenttic AI systems is an iterative and experimental process. It requires thinking outside the box and coming up with innovative solutions. So
spk_0	it’s not just about crunching numbers and writing code, it’s about leveraging those skills to solve real world problems and create intelligent systems that can make a tangible impact.
spk_1	That’s a great way to put it.
spk_1	And I think this highlights the incredible potential of agentic AI for data scientists. It’s a field that demands both technical proficiency and creative problem solving, offering the opportunity to push the boundaries of AI and develop truly innovative solutions.
spk_0	This has been incredibly insightful.
spk_0	We’ve journeyed from high-level concepts to specific techniques and tools, and now I’m starting to grasp the broader picture of how agentic AI can be implemented and its potential impact across various domains.
spk_1	It’s a
spk_1	captivating field, and we’ve only just begun to explore its possibilities.
spk_0	I’m eager to hear more. What other exciting developments or real world examples can you share that showcase the power and potential of agentic AI?
spk_1	There’s one area I think you’ll find particularly fascinating, the intersection of agentic AI with robotics.
spk_1	Imagine robots that can not only perform physical tasks, but also reason, plan and make decisions autonomously in complex real world
spk_1	environments.
spk_0	That sounds like something straight out of a science fiction movie. Robots that can think for themselves and act independently.
spk_1	It might
spk_1	sound futuristic, but the reality is closer than you might think. Agentic AI is revolutionizing the field of robotics.
spk_1	Enabling the development of a new generation of robots that are more adaptable, intelligent, and capable than ever
spk_1	before.
spk_0	I’m all ears. Tell me more about these agentic robots and how they’re being applied in real world scenarios.
spk_1	Let’s start with an example that’s already making a significant impact autonomous warehouse robots. These robots are designed to navigate complex warehouse environments, locate and retrieve items, and even collaborate with human workers to fulfill orders efficiently.
spk_0	So it’s like having a team of
spk_0	Robotic assistants working alongside human employees, each playing a specific role in the overall workflow.
spk_1	Exactly. And agentic AI is what makes these robots so effective. They’re not simply following pre-programmed paths or reacting to basic commands. They’re constantly observing their surroundings, making decisions based on real-time information.
spk_1	And adapting their behavior to achieve their
spk_1	goals.
spk_0	So it’s not just about automation. It’s about creating robots that can think and act intelligently, almost like human colleagues,
spk_1	precisely. And this level of intelligence and adaptability is essential in dynamic environments like warehouses where conditions are always changing and unexpected events are
spk_1	common.
spk_0	Can you give us a specific example of how this agentic intelligence works in a warehouse setting?
spk_1	Imagine
spk_1	a robot tasked with retrieving a particular item.
spk_1	From a shelf using its sensors and AI brain, it navigates through the aisles, avoids obstacles, and identifies the correct shelf location. But what if the item is out of stock or there’s an unexpected obstacle blocking the way?
spk_0	That’s a great question. In a traditional robotic system, the robot might just stop and wait for human intervention.
spk_1	You’re
spk_0	absolutely
spk_1	right.
spk_1	But an agenttic robot with its problem solving capabilities can handle these situations more intelligently. It might check alternative shelf locations, communicate with other robots to see if they have the item, or even alert a human worker to the problem and suggest a solution.
spk_0	It’s like the robot is thinking ahead.
spk_0	Anticipating potential issues and coming up with creative
spk_0	workarounds.
spk_1	Exactly. And this level of adaptability and problem solving is a key differentiator for agentic robots.
spk_0	OK, I’m starting to grasp how powerful this technology can be, but warehouse robots are just one example. What other applications are there for agentic robots?
spk_1	The potential applications are vast, and we’re still discovering new ones. We’re seeing agentic robots being utilized in healthcare for tasks like patient monitoring and assistance.
spk_1	In agriculture for precision farming and crop management and even in search and rescue operations to navigate dangerous environments and locate survivors. Wow,
spk_0	it seems like the possibilities are truly endless. Robots are becoming increasingly integrated into our lives, taking on roles that were once thought to be exclusive to humans.
spk_1	It’s a fascinating trend, and it raises some important questions about the future of work and the evolving relationship between humans and machines.
spk_0	Absolutely.
spk_0	But before we dive into those broader societal implications, I’m curious about the technical challenges involved in building these agentic robots. It sounds incredibly complex.
spk_1	It certainly is. Building agentic robots demands expertise across multiple disciplines from robotics and AI to mechanical.
spk_1	Engineering and software development.
spk_0	It’s a
spk_0	truly interdisciplinary field, bringing together specialists from various domains.
spk_1	Exactly. And one of the major challenges is integrating all these different components into a seamless and reliable system. You need to ensure the robots sensors are providing accurate data.
spk_1	That its AI brain is making intelligent decisions and that its actuators are carrying out those decisions effectively. It’s
spk_0	like conducting an orchestra of technologies, making sure each instrument is playing in harmony to create a beautiful and functional masterpiece.
spk_1	That’s a brilliant analogy. And this integration process becomes even more complex. When you consider that agentic robots often
spk_1	Operate in unpredictable real world environments. Right.
spk_0	The real world is messy and full of surprises, unlike the controlled settings of a lab.
spk_1	Precisely. So you need to design robots that can handle unexpected events, adapt to changing conditions, and make decisions in real time to ensure their safety and effectiveness.
spk_0	It’s like equipping the robot with a set of survival skills.
spk_0	Enabling it to navigate the complexities and uncertainties of the real world.
spk_1	Exactly. This adaptability is crucial for eugenic robots to perform their tasks successfully and integrate seamlessly into our
spk_1	lives.
spk_0	OK, this is all starting to come together, but I want to circle back to a topic we touched upon earlier, the role of LLA.
spk_0	and agent AI.
spk_1	Ah yes, those powerful language models that are transforming how we interact with machines. It’s an interesting question. How do they fit into the world of agentic robots?
spk_0	That’s exactly what I’m curious about. We’ve talked about LLMs in the context of recommender systems and financial trading. Yeah.
spk_0	But how do they enhance the capabilities of agentic robots? Well,
spk_1	one of the most promising areas is in human-robot interaction. LLMs can enable robots to understand and respond to natural language commands, making it much easier for humans to interact with them and give them instructions.
spk_0	So instead of having to program complex commands,
spk_0	Or use specialized interfaces. We can simply talk to these robots like we would talk to another person.
spk_1	Exactly. Imagine being able to say to a robot, Hey, could you bring me a cup of coffee from the kitchen? or Please tidy up this mess in the lab. LLMs are making these natural language interactions a reality.
spk_0	That’s
spk_0	incredible. It’s like having a robot that can truly understand and respond to our needs, almost like a human assistant, precisely.
spk_1	And beyond simply understanding commands, LLMs enable robots to engage in more sophisticated conversations. They can provide explanations, answer questions, and even offer suggestions. So it’s
spk_0	not
spk_0	just about giving instructions. It’s about fostering a more collaborative and interactive relationship with these robots. You got it.
spk_1	And this level of communication and collaboration is crucial for integrating robots into human environments.
spk_1	And ensuring they can work effectively alongside us.
spk_0	OK. I can see how LLMs can significantly elevate the capabilities of agentic robots. But beyond human-robot interaction, are there other areas where LLMs are making an impact? Absolutely.
spk_1	LLMs can also play a role in enhancing a robot’s internal reasoning and decision making processes. They can be used to represent knowledge about the world, provide contextual information, and even generate potential plans of action.
spk_0	It’s like giving the robot access to a vast knowledge base and a team of advisors to help it make better decisions.
spk_1	That’s an excellent way to think about it. And this integration of LLMs with agentic AI is opening up a whole new world of possibilities for developing robots that are more intelligent, adaptable, and
spk_0	capable. This is all fascinating stuff. But before we get carried away with all these futuristic possibilities, let’s bring it back down to earth for a moment.
spk_0	What are some practical considerations when when it comes to implementing LLMs in eugenic robot systems?
spk_1	That’s a really important question. One of the key considerations is computational resources. LLMs, especially the larger ones, can be quite demanding to run computationally, and that can be a challenge for robots that need to operate in
spk_1	Real time, right,
spk_0	because robots often have limited processing power and battery life, unlike servers in a data center.
spk_1	Exactly. So there’s a lot of research focused on optimizing LLMs for these resource constrained environments using techniques like model compression and knowledge distillation.
spk_0	So it’s like
spk_0	trying to fit a powerful brain into a compact and efficient package, making sure the robot can think quickly and effectively without draining its battery in a few minutes.
spk_1	That’s a
spk_1	great analogy. Another important consideration is data privacy and security.
spk_1	LLMs often rely on vast amounts of data, some of which might be sensitive or confidential,
spk_0	right,
spk_1	because
spk_0	these
spk_0	robots are operating in the real world, collecting information about people and their surroundings.
spk_1	Exactly. So it’s essential to implement robust data privacy and security measures to ensure that the robot is handling data responsibly and ethically.
spk_0	It’s like making sure that these intelligent robots are also responsible citizens, respecting people’s privacy and safeguarding sensitive information.
spk_1	No.
spk_1	And lastly, it’s important to remember that LLMs aren’t a magic solution. They’re powerful tools, but they need to be used thoughtfully and in conjunction with other AI techniques to create truly effective agenttic robot
spk_1	systems,
spk_0	like any tool in our toolbox, it has its strengths and limitations, and it’s up to us to use it wisely and creatively to solve the problems we’re facing,
spk_1	precisely. And this highlights the exciting and challenging nature of working with agentic AI and robotics. It’s a field that requires both
spk_1	Technical expertise and creative problem solving, giving us the opportunity to push the boundaries of AI and develop truly groundbreaking solutions.
spk_0	This
spk_0	has been an incredible journey so far, exploring this fascinating world of agentic AI and its applications in robotics.
spk_1	It’s been my pleasure sharing these insights
spk_1	with you,
spk_0	but our exploration isn’t over yet. In the next part of our deep dive, we’ll shift our focus to the ethical considerations surrounding agentic AI, a crucial aspect to consider.
spk_0	As these intelligence systems become more integrated into our lives.
spk_1	Absolutely, the ethical implications of agentic AI are significant, and we need to approach them with careful consideration.
spk_0	So stay tuned for the final part of our deep dive, where we’ll delve into the ethical landscape of agentic AI and explore the challenges and opportunities that lie ahead.
spk_0	Welcome back to our deep dive on agentic AI. Yeah, we’ve explored the core concepts of real-world applications, even dove into the nitty gritty of how to actually build these systems. But there’s another layer we need to peel back, one that becomes super important as these intelligent systems get more and more woven into our lives.
spk_1	Absolutely. We can’t have a meaningful conversation about agenttic AI without talking about ethics. We’re dealing with powerful systems here, systems that can impact
spk_1	Individuals, whole societies, even the very way our world works. So understanding the ethical implications is paramount.
spk_0	Right? And we’ve touched on some potential risks like bias creeping into decision making, the possibility of jobs being displaced, even the potential for these systems to be misused.
spk_0	But what are some other ethical challenges we might bump into as agenttic AI becomes more commonplace? Well,
spk_1	one of the
spk_1	biggest concerns is transparency or maybe the lack of it. You know, as data scientists, you’re familiar with those black box models where even the people who created them might not fully grasp how the AI reaches its conclusions. With agentic AI, this lack of transparency becomes even more critical.
spk_0	Yeah, because we’re talking about systems making decisions.
spk_0	On their own, decisions that could have a real impact on people’s lives. If we don’t get the why behind those decisions, it’s hard to feel confident about who’s accountable.
spk_1	Exactly. Imagine an agenttic AI system running a hospital. It’s making calls about patient care, how resources are allocated, maybe even life or death situations. If we can’t understand the logic behind those choices, it erodes trust and raises some serious ethical red flags.
spk_0	So transparency and explainability aren’t just technical details, they’re ethical necessities in the world of agentic AI. How do we even begin to tackle that?
spk_1	There are a few paths being explored. One is to build what are called interpretable AI models, where the decision-making process is laid out in a way that humans can follow. Another is to create tools that can audit AI decisions, kind of like giving us a peek into the factors that led to a particular outcome.
spk_1	And of course we need to be upfront about the limitations of these
spk_1	systems.
spk_0	So it’s like a three-pronged approach. We need technical solutions, ethical frameworks, and open, honest communication about what agentic AI can and can’t do.
spk_1	Exactly. And alongside transparency, another big ethical hurdle is making sure these systems are fair. We’ve already seen AI systems that just perpetuate the biases that exist in society.
spk_1	Leading to discriminatory outcomes in things like hiring loans, even the criminal justice system. Yeah,
spk_0	it’s like if you train an AI on data that’s already skewed.
spk_0	It’s going to spit out biased decisions even if the developers didn’t intend for
spk_0	that to happen.
spk_1	Precisely with agentic AI, this risk is magnified because these systems are acting more independently and often dealing with really sensitive personal information. So tackling bias head on is absolutely crucial. OK,
spk_0	so
spk_0	how do we actually build fairness into agentic AI? What can data scientists do to avoid those traps?
spk_1	It all starts with awareness, recognizing that bias can sneak into AI in ways we might not even notice at first.
spk_1	Then it’s about being super careful with the data we use to train these systems, auditing it, identifying potential biases, and mitigating them. And finally, we need to develop algorithms that are designed with fairness in mind, algorithms that account for potential biases and strive for equitable outcomes.
spk_0	So it’s a multifaceted challenge. awareness data curation and algorithmic fairness. Mm.
spk_0	Sounds like data scientists have a huge responsibility here.
spk_1	You’re right, data scientists are in a powerful position when it comes to shaping the ethical landscape of agentic AI. They’re the builders, the architects of these systems, making choices about data algorithms and ultimately how
spk_1	These technologies impact the world.
spk_0	It’s a good reminder that data science isn’t just about the technical stuff. It’s about being ethically aware and committed to using our skills responsibly.
spk_1	Couldn’t agree more. And there’s another ethical dimension specific to Agenta AI that we need to grapple with.
spk_1	The question of control and autonomy. As these systems get smarter and more capable, how much freedom should we give them? Where do we draw the line between human oversight and letting machines make the calls?
spk_0	That’s
spk_0	a big one. It gets to the heart of what it means to be human in a world where intelligent machines are playing a bigger and bigger role. It’s almost like we’re writing a new set of rules figuring out the roles and responsibilities of both humans and AI.
spk_1	You’ve hit it right on the head.
spk_1	And there’s no easy answer. It’s going to take ongoing conversations, careful consideration of both risks and benefits, and a willingness to adapt as these technologies keep evolving.
spk_0	This has been a really thought provoking conversation. It’s clear that agentic AI isn’t just about the tech. It brings up some deep ethical questions that we as a society need to wrestle with.
spk_1	It’s a reminder that progress in tech should always go hand in hand with ethical reflection. We need to be committed to using these powerful tools for good.
spk_0	Well said.
spk_0	As we wrap up our deep dive into agentic AI, I’m left with this mix of excitement and caution. The potential benefits are huge, but we have a big responsibility to wield this power wisely.
spk_1	I’m right
spk_1	there with you. Agentic AI is a game changer with the potential to reshape our world. It’s our job to guide its development and make sure it benefits humanity.
spk_0	I think that’s a perfect place to leave it.
spk_0	Thanks for joining us on this deep dive into the world of agentic AI. It’s a fascinating and complex topic, and I hope our listeners have come away with a better understanding of what it’s all about, the potential it holds, and the ethical considerations we all need to keep in mind. The
spk_1	pleasure was all mine. Keep exploring, learning, and engaging in these conversations about the future of AI.

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