Speaker 1: Okay, imagine your AI assistant doing way more than just
Speaker 1: answering questions. Yeah, Like what if it could actually plan
Speaker 1: your entire week, figure out what you need before you
Speaker 1: even ask, and maybe even work with other AIS to
Speaker 1: get really complex stuff done.
Speaker 2: Right, We're talking about a pretty big leap beyond you know,
Speaker 2: the typical chatbot experience you might have.
Speaker 1: Now, exactly what if AI wasn't just this tool we use,
Speaker 1: but more like an autonomous collaborator, something that can reason, adapt,
Speaker 1: strategize almost alongside you.
Speaker 2: That's precisely what we're here to unpack today. This whole
Speaker 2: duc dit is about agentic AI systems.
Speaker 1: Agentic AI okay, Yeah.
Speaker 2: We're exploring AI that doesn't just spit out content, but
Speaker 2: can actively reason, plan, adapt and act with quite a
Speaker 2: bit of autonomy. It can even reflect on its own
Speaker 2: experiences to get better.
Speaker 1: So our mission today is basically to take you on
Speaker 1: a journey to understand what this agentic AI really is.
Speaker 1: We'll look at how these well intelligent systems are built,
Speaker 1: the principles behind how they make decisions and.
Speaker 2: Learn, and where there actually be use the real world
Speaker 2: applications across different industries.
Speaker 1: And importantly, we have to get into the crucial stuff
Speaker 1: around trust, safety, ethics, all of that.
Speaker 2: Absolutely. Think of this as your shortcut to really getting
Speaker 2: a handle on this pretty transformative moment in AI.
Speaker 1: Okay, sounds good. Where do we start.
Speaker 2: Well, let's maybe start with a quick refresher on generative
Speaker 2: AI just to set the stage, and then we can
Speaker 2: bridge that gap to what makes an AI system truly agentic.
Speaker 1: Perfect. So, for anyone following AI, generative models are probably
Speaker 1: familiar territory, but just for everyone, let's quickly clarify what
Speaker 1: is generative AI at its core.
Speaker 2: At its heart, generative AI is all about creating brand
Speaker 2: new synthetic content content like like text, images, audio, video,
Speaker 2: basically anything that looks like the real world data it
Speaker 2: was trained on. It's different from older AI that just
Speaker 2: you know, classifies or identifies things right. Generative models learn
Speaker 2: the underlying patterns and the data, the structure, and then
Speaker 2: use that knowledge to produce completely novel instances.
Speaker 1: It's like training it on faces and it makes a
Speaker 1: new face.
Speaker 2: Exactly faces of people who don't actually exist, but they
Speaker 2: look incredibly real. That ability to create is really the
Speaker 2: first critical step towards AI that can eventually act on
Speaker 2: its own that's.
Speaker 1: A powerful idea. And we've all heard of models like
Speaker 1: GPT for text or maybe daily and stable diffusion for images.
Speaker 1: What's the magic behind those?
Speaker 2: Well, a lot of them, especially the big large language
Speaker 2: models like the GPT series, use something called the transformer architecture. Okay,
Speaker 2: think of it as just a very efficient way for
Speaker 2: the AI to understand and generate sequential data like language
Speaker 2: like sentences makes sense. This architecture lets the models process
Speaker 2: just huge amounts of text and then predict what word
Speaker 2: is most likely to come next, building up coherent, relevant responses.
Speaker 2: It's what drives that incredibly human like text we see.
Speaker 1: Okay, so we have these powerhouse models that can generate stuff,
Speaker 1: But how do we get from an AI that just
Speaker 1: makes a convincing picture, write some text to one that
Speaker 1: actually acts independently, makes decisions, pursues goals. That's the big
Speaker 1: lead to agentic systems, right.
Speaker 2: That's the fundamental shift exactly. Agentic systems go beyond just
Speaker 2: generating content. They are really designed for active decision making, planning,
Speaker 2: and goal oriented behavior. They operate with a clear purpose.
Speaker 1: And what gives them that sense of purpose. We're talking
Speaker 1: concepts like self governance, agency.
Speaker 2: Autonomy, precisely, self governance is the agent's ability to operate
Speaker 2: based on its own internal principles and goals without needing
Speaker 2: a human constantly telling.
Speaker 1: It what to do. Okay.
Speaker 2: Agency is its capacity to act on behalf of someone,
Speaker 2: maybe a user or another system. It defines objectives, gets
Speaker 2: the information it needs, and takes steps to achieve them.
Speaker 1: And the autonomy.
Speaker 2: Autonomy is really that ability to operate independently, making decisions,
Speaker 2: taking actions without direct human control at every single step.
Speaker 1: This is where it gets really interesting. Let's use that
Speaker 1: flight booking example from the source material. It really brings
Speaker 1: it home up. Imagine you want to butt a trip,
Speaker 1: say San Diego to San Francisco, next Friday to Sunday. Okay,
Speaker 1: you start super vague, book me a flight from San
Speaker 1: Diego to San Francisco and next Friday to Sunday.
Speaker 2: Right, And this AI assistant, which is an l empowered agent,
Speaker 2: it knows that's not enough detail it needs more, so
Speaker 2: it asks. It might come back with something like, okay,
Speaker 2: do you have a preferred airline or are you open
Speaker 2: to any and what class of service were you thinking of?
Speaker 1: And you reply, I prefer morning flights, no airline preference
Speaker 1: economy is fine.
Speaker 2: And the bot processes that. It says, okay, thanks for
Speaker 2: the details. I'll look for morning flights economy class across
Speaker 2: all airlines for those dates.
Speaker 1: Give me just a moment, and then it comes back
Speaker 1: with options.
Speaker 2: Exactly and might say, okay, I found a few options.
Speaker 2: Here are the best morning flights, and list maybe option
Speaker 2: one on United Alaska for three hundred and twenty five dollars,
Speaker 2: option two on Delta Southwest for three hundred and ten dollars.
Speaker 2: Which one works best for you?
Speaker 1: That exchange that really shows agency and autonomy and action.
Speaker 2: It absolutely does. The AI isn't just generating text responses.
Speaker 2: It's actively asking for information, using that info as parameters
Speaker 2: for what we can imagine our back end tools or
Speaker 2: APIs maybe a flight look up tool, then later a
Speaker 2: book flight tool. It's making decisions based on the conversation flow,
Speaker 2: like independently searching for the best options, and it's even
Speaker 2: ready to kick off the booking process, maybe send a
Speaker 2: payment link. It's genuinely acting on your behalf.
Speaker 1: That is a huge shift. It's not just talking to you,
Speaker 1: it's acting for you. Okay, So how do these agents
Speaker 1: actually well think? How do they learn to manage these tasks.
Speaker 1: What's their internal map of the world look like.
Speaker 2: Yeah, good question. They need structured ways to store and
Speaker 2: organize information. We call this knowledge representation.
Speaker 1: Okay.
Speaker 2: One really powerful approach is using semantic networks. Imagine a
Speaker 2: huge sort of interconnected web of concept by going map,
Speaker 2: kind of like a giant mind map. Yeah, each concept
Speaker 2: is a node like dog or animal or reads air,
Speaker 2: and lines connect them. Showing relationships is a type of causes.
Speaker 1: Is part of So if it knows animals breathe air
Speaker 1: and dogs are animals.
Speaker 2: We can automatically figure out or infer that dogs breathe there.
Speaker 2: These networks allow them to connect the dots and derive
Speaker 2: new facts.
Speaker 1: That's pretty intuitive. What about frames you mentioned those two.
Speaker 1: They sound a bit like digital index cards.
Speaker 2: That's actually a great way to put it. Frames are
Speaker 2: more structured. Think of a car frame. It has specific
Speaker 2: slots or attributes like make, model, year, color. You fill
Speaker 2: in the values for each specific.
Speaker 1: Car, so it groups related information together exactly.
Speaker 2: It mirrors how we humans often conceptualize things, grouping properties
Speaker 2: together into a single unit.
Speaker 1: And for situations where you absolutely need precision, like mathematically precise.
Speaker 2: That's where logic based representations come in. These use formal
Speaker 2: logic like propositional or first order logic to encode facts
Speaker 2: and rules.
Speaker 1: Like in math class pretty.
Speaker 2: Much much you might represent all humans are mortal in
Speaker 2: a strict mathematical way. This rigor is super important and
Speaker 2: feels where errors are costly. Think software verification, maybe even
Speaker 2: legal analysis. It ensures every conclusion is logically sound.
Speaker 1: Okay, so the agent builds this complex internal knowledge map.
Speaker 1: How does it then use that map to draw conclusions
Speaker 1: or figure out new things? That's reasoning right, Precisely.
Speaker 2: Reasoning is how agents manipulate that knowledge to get insights.
Speaker 2: One type is deductive reasoning. This is very top down,
Speaker 2: top down, meaning you start with general rules or premises,
Speaker 2: and you arrive at specific conclusions that must be true
Speaker 2: if the premises are true. The classic example, all men
Speaker 2: are mortal.
Speaker 1: Socrates is a man, Therefore Socrates is mortal exactly.
Speaker 2: It's logically inescapable. You see this in math, logic proofs,
Speaker 2: verifying software anywhere certainty is key.
Speaker 1: It's like a guaranteed logical chain. But what about when
Speaker 1: things aren't so certain? When agents need to find passatterns
Speaker 1: or make educated guesses.
Speaker 2: That's where inductive reasoning is vital.
Speaker 1: This is more bottom up, so starting with specifics, right.
Speaker 2: You look at specific observations and you try to form
Speaker 2: probable generalizations, like the sun has risen every single day
Speaker 2: for as long as we know, so it will probably
Speaker 2: rise tomorrow exactly. It's not a logical certainty, but it's
Speaker 2: a very strong probability based on evidence. This is fundamental
Speaker 2: to science and especially to machine learning, finding patterns in
Speaker 2: data to make predictions.
Speaker 1: Okay, deduction for certainty, induction for probability. What about figuring
Speaker 1: out the cause of something like plain detective.
Speaker 2: Ugh, that's abductive reasoning. It's often called inference to the
Speaker 2: best explanation.
Speaker 1: Inference to the best explanation.
Speaker 2: Yeah, you observe an effect and you try to figure
Speaker 2: out the most plausible cause. If you see the lawn
Speaker 2: is wet, a good abductive inference is it probably rained
Speaker 2: last night.
Speaker 1: It's not the only possibility. Maybe the sprinklers are all
Speaker 1: but rain.
Speaker 2: Is often the simplest, most likely explanation. This is super
Speaker 2: useful and fields like medical diagnosis figure earing out the
Speaker 2: disease from symptoms or fault detection, or even forensics you're
Speaker 2: piecing together clues.
Speaker 1: Okay, So agents can represent knowledge, they can reason about
Speaker 1: it deductively, inductively, abductively, But how do they get better?
Speaker 1: How do they adapt over time? That has to involve
Speaker 1: learning mechanisms.
Speaker 2: Learning is absolutely fundamental for any agent that needs to adapt.
Speaker 2: There are several key types. You have supervised learning.
Speaker 1: That's learning from labeled examples, right like seeing pictures labeled cat.
Speaker 2: Or dog exactly, or predicting house prices based on features
Speaker 2: where you have the actual prices for your training data
Speaker 2: input output pairs.
Speaker 1: Okay, Then there's unsupervised.
Speaker 2: Unsupervised learning is about finding patterns in data that isn't labeled.
Speaker 2: Think about grouping customers into segments based on their buying
Speaker 2: habits without knowing the segments beforehand. The AI finds the
Speaker 2: structure itself.
Speaker 1: And reinforcement learning RL.
Speaker 2: That sounds interesting, RL is fascinating. It's learning through trial
Speaker 2: and mirror. The agent takes actions in an environment, and
Speaker 2: it sieves rewards or punishments based on the outcomes.
Speaker 1: Like training a dog or a game AI.
Speaker 2: Very much like that game AI is a classic example.
Speaker 2: The AI learns to play chess or go by playing
Speaker 2: millions of games and getting rewarded for winning. Robotics uses
Speaker 2: it a lot too, for learning how to walk or
Speaker 2: grasp objects.
Speaker 1: And lastly, transfer learning.
Speaker 2: Transfer learning is really efficient. It's about taking knowledge gain
Speaker 2: from one task and applying it to a different, but
Speaker 2: related task. It means the agent doesn't have to start
Speaker 2: from scratch.
Speaker 1: Every time, okay, knowledge reasoning learning puts the agent in
Speaker 1: a position to actually make choices and figure out what
Speaker 1: to do next. How do they handle decision making and planning.
Speaker 2: For decision making? A key concept is the utility function.
Speaker 1: Utility function sounds economic, it kind of is.
Speaker 2: It's a way to quantify the agent's preferences. It maps
Speaker 2: different possible outcomes to numerical values representing how desirable each
Speaker 2: outcome is to the agent.
Speaker 1: So like our travel agent example.
Speaker 2: Exactly, the travel agent's utility function might weigh factors like price, comfort,
Speaker 2: travel time convenience. Maybe a budget airline has a low
Speaker 2: price score but also low comfort. A road trip might
Speaker 2: be cheaper overall, but take.
Speaker 1: Longer, and the function helps it choose right.
Speaker 2: It calculates the total utility for each option based on
Speaker 2: the weight's assigned to price, comfort, et cetera, and picks
Speaker 2: the option with the highest score. It allows for rational
Speaker 2: choices based on defined goals, even when those goals conflict
Speaker 2: like cost versus speed, So.
Speaker 1: It picks the best option according to its values, not
Speaker 1: just any option.
Speaker 2: Wow.
Speaker 1: And once it decides what it wants, it needs a
Speaker 1: plan to get there. That's planning algorithms exactly.
Speaker 2: Planning algorithms figure out the sequence of actions needed to
Speaker 2: reach the desired goal state. There are many types, simple
Speaker 2: graph searches like finding a route on a map, more
Speaker 2: complex heuristic searching, oh, the chess programs yeah, or things
Speaker 2: like Monte Carlo tresearch, which is great for games or
Speaker 2: situations with uncertainty. But what's really interesting for these LM
Speaker 2: based agents we're talking about, yes, is that sometimes the
Speaker 2: LLM itself can act as the planner. It uses its
Speaker 2: language understanding to formulate a plan. And another powerful approach
Speaker 2: is hierarchical task network planning or htn HTM. It breaks
Speaker 2: down a big complex goal like planifacation, into smaller nested
Speaker 2: subtasks find flights, book hotel, plan activities. This hierarchical approach
Speaker 2: fits really well with how lllm's process information and handle
Speaker 2: complex instructions.
Speaker 1: That makes a lot of sense. Okay, we've got a
Speaker 1: good handle on the building blocks. Now let's talk about
Speaker 1: how these systems really start to shine in practice.
Speaker 2: Yeah.
Speaker 1: One capability that sounds very human is reflection and introspection
Speaker 1: agents thinking about themselves.
Speaker 2: It really is quite human like. Reflection is the agent's
Speaker 2: ability to monitor its own performance and adapt its behavior
Speaker 2: based on that monitoring. It's like human metacognition, thinking about
Speaker 2: your own thinking.
Speaker 1: Why is that so important for an AI agent?
Speaker 2: Well, several reasons. It leads to much better decision making
Speaker 2: because the agent can essentially replay past choices in their
Speaker 2: own outcomes, learning from mistakes and reinforcing successes.
Speaker 1: So it learns from its own history exactly.
Speaker 2: It also enables better adaptation. Think about our travel agent again.
Speaker 2: The travel industry changes constantly, Prices fluctuate, new routes appear.
Speaker 2: Reflection allows the agent to notice these changes and adjust
Speaker 2: its strategies accordingly.
Speaker 1: And I imagine there are ethical angles too, Definitely.
Speaker 2: Reflection can help ensure the agent's actions stay aligned with
Speaker 2: human values or ethical guidelines over time, and it can
Speaker 2: even improve how humans interact with the AI, maybe by
Speaker 2: allowing the agent to adapt its communication style based on
Speaker 2: perceived user frustration or confusion.
Speaker 1: How does this actually work under the hood? How is
Speaker 1: reflection implemented.
Speaker 2: Some key techniques include meta reasoning, where the agent literally
Speaker 2: analyzes its own reasoning process. Did my previous strategy work well?
Speaker 2: Why or why not? There's also self explanation, the agent
Speaker 2: generates explanations for its own decisions. This isn't just for
Speaker 2: the user. It helps the agent itself understand and learn
Speaker 2: from its choices, blanes to itself in a way yes.
Speaker 2: And self modeling, where the agent updates its internal understanding
Speaker 2: of its goals, its capabilities, and the world based on
Speaker 2: new experiences and the results of its reflections.
Speaker 1: Fascinating. So as agents get smarter about themselves, they also
Speaker 1: need to interact with the outside world more effectively. This
Speaker 1: brings us to enabling tool use. Getting agents to use
Speaker 1: external resources right.
Speaker 2: Tool use is fundamental for making these agents truly practical.
Speaker 2: It means an LM agent leveraging things outside itself like APIs, databases,
Speaker 2: software functions to add to its own abilities.
Speaker 1: So it can do more than just what it was
Speaker 1: trained on exactly.
Speaker 2: It allows agents to as the source material puts it,
Speaker 2: transcend intrinsic limitations. They're not stuck with only their internal knowledge.
Speaker 2: They can fetch real time information, perform calculations, interact with
Speaker 2: other systems, even control hardware.
Speaker 1: How does an AI know how to use, say, a
Speaker 1: specific weather API. This is just figure it out.
Speaker 2: Not quite magically, but intelligently. The key is that the
Speaker 2: agent needs a good description of the tool A description, yeah,
Speaker 2: usually provided by the developer. It needs to know the
Speaker 2: tool's purpose, what kind of input it expects, what parameters
Speaker 2: it takes. Often this is written right into the code
Speaker 2: using something called a dock string.
Speaker 1: Okay.
Speaker 2: Once the LLM understands what the tool does and how
Speaker 2: to call it, it can intelligently decide when using that
Speaker 2: tool is the right step to achieve its current goal.
Speaker 1: So an agent could use a weather API for forecasts,
Speaker 1: connect to a payment system for a transaction, maybe query
Speaker 1: a database for specific information.
Speaker 2: Or even interact with hardware interfaces in a robotics context.
Speaker 2: The possibilities are huge.
Speaker 1: Yeah, the significance seems massive, then it really is.
Speaker 2: Tool use is what lets agents tackle complex real world problems.
Speaker 2: Think about a healthcare agent using up to the minute
Speaker 2: medical databases or interacting with diagnostic tools. It's a complete
Speaker 2: game changer.
Speaker 1: Okay, so we have individual agents that can reflect and
Speaker 1: use tools, but the real power often comes from teamwork,
Speaker 1: right even for ais. Let's talk about multi agent systems
Speaker 1: or MS.
Speaker 2: Yes. Masays are where you have multiple autonomous agents interacting, cooperating,
Speaker 2: maybe coordinating to achieve goals that might be too complex
Speaker 2: for any single agent. It's about distributed problem solving, and.
Speaker 1: There are ways to organize these teams of agents.
Speaker 2: Definitely. One really effective model mentioned in our sources is
Speaker 2: the coordinator worker delegator model or CWD CWD.
Speaker 1: Okay, break that down for us. Coordinator, worker delegator.
Speaker 2: Right. The coordinator is like the project manager. It oversees
Speaker 2: the whole workflow, sets priorities, tracks progress towards the main goal.
Speaker 1: You got it, the boss sort of yeah.
Speaker 2: Then you have the workers. These are specialized agents, each
Speaker 2: expert at a specific task. In our travel example, you
Speaker 2: might have a flight booking worker, a hotel booking worker,
Speaker 2: maybe a data analyst worker looking for deals specialists exactly,
Speaker 2: and finally, the delegator. This agent sits between the coordinator
Speaker 2: and the workers. It takes the high level plan from
Speaker 2: the coordinator and breaks it down and concrete tasks, assigning
Speaker 2: them to the right workers and managing resources.
Speaker 1: Okay, let's apply CUD to the travel example. Again, user
Speaker 1: asks for a trip.
Speaker 2: Right the coordinator agent receives the request and forms a
Speaker 2: high level plan book flights, book hotel, find activities for
Speaker 2: San Francisco trip.
Speaker 1: Then the delegator.
Speaker 2: The delegator takes that plan and creates specific tasks. Task
Speaker 2: one find morning economy flights SD to SF, next freysun
Speaker 2: assigned to flight worker. Task two find three star hotel
Speaker 2: near downtown SF for those dates, assigned to hotel worker,
Speaker 2: and so on. Maybe it assigns tasks to an analyst
Speaker 2: worker to check for package deals or a reflector agent
Speaker 2: to review the plan's.
Speaker 1: Logic, and the workers just do their jobs.
Speaker 2: The workers execute their specialized tasks, possibly in parallel, and
Speaker 2: report results back up. The delegator or coordinator integrates everything that.
Speaker 1: Sounds incredibly efficient, much better than one agent trying to
Speaker 1: juggle everything.
Speaker 2: It really highlights the benefits efficiency through parallel processings, specialization,
Speaker 2: leading to higher quality results and distributed control, making the
Speaker 2: system more robust.
Speaker 1: And for this team to work, communication must be key.
Speaker 2: Absolutely critical. They need standardized ways to talk to each
Speaker 2: other protocols for coordination, like how to prioritize tasks, mechanisms
Speaker 2: for sharing knowledge effectively, and maybe even ways to negotiate
Speaker 2: if conflicts arise between agents, goals, or resource needs.
Speaker 1: This is all incredibly powerful stuff, but it also brings
Speaker 1: up some really significant questions about trust, safety, and ethics.
Speaker 1: This AI frontier needs careful navigation.
Speaker 2: That's paramount. Honestly, if users don't trust these systems, they
Speaker 2: just won't be adopted, or worse, they'll be misused. Trust
Speaker 2: isn't just one thing. It covers reliability, transparency, Knowing the
Speaker 2: AI aligns with your expectations and.
Speaker 1: Values, and lack of trust leads.
Speaker 2: To skepticism, resistance, maybe even people trying to work around
Speaker 2: the system, negating its benefits.
Speaker 1: So what are some of the big risks or challenges
Speaker 1: we really need to grapple with? As these agentic systems
Speaker 1: become more capable and widespread, they can act now, which
Speaker 1: feels different.
Speaker 2: It is different. The risks get amplified. Take misinformation and hallucinations.
Speaker 2: If a simple chatbot makes something up, it's annoying. If
Speaker 2: an agentic system hallucinates, say, incorrect flight details or faulty instructions,
Speaker 2: for a physical task and then acts on that.
Speaker 1: Information that could have real consequence.
Speaker 2: Various consequences because it might make booking, spend money, or
Speaker 2: control machinery based on flawed data, potentially without immediate human oversight.
Speaker 2: Then there's data privacy.
Speaker 1: We hear about data breaches all the time.
Speaker 2: Right, but here it's not just about accidental inclusion of
Speaker 2: personal info and training data, although that's still a risk.
Speaker 2: Agentic systems might actively gather, process, and potentially misuse sensitive
Speaker 2: data while performing tasks like.
Speaker 1: Our travel assistant figuring out confidential business travel plans.
Speaker 2: Exactly, or memorizing personal details shared in conversation, which some
Speaker 2: models have unfortunately been shown to do. It demands extremely
Speaker 2: careful design around data handling.
Speaker 1: And permissions and intellectional property IP.
Speaker 2: Risks also evolve. Generative AI already raises questions about copyright
Speaker 2: for AI created content, but agentic systems are active creators
Speaker 2: and manipulators of information. They might combine sources, modify existing works,
Speaker 2: or generate novel designs in ways that challenge current IP frameworks.
Speaker 2: We need clarity on ownership and infringement.
Speaker 1: Okay, these are serious concerns. How do we actually go
Speaker 1: about ensuring these systems are safe and responsible. What are
Speaker 1: the practical steps?
Speaker 2: It requires a multi layered approach. Strong technical safeguards are crucial.
Speaker 2: Action boundaries are one.
Speaker 1: Key elements setting limits.
Speaker 2: Precisely defining strict operational limits on what the agent is
Speaker 2: allowed to do. This could be through policy rules or
Speaker 2: maybe using rule based access control RBAC like we do
Speaker 2: for humans. Ensuring agents can only access the tools and
Speaker 2: data they absolutely need.
Speaker 1: For their specific function makes sense.
Speaker 2: What else? Decision verification and human in the loop designs
Speaker 2: are vital, especially for highst.
Speaker 1: Actions, so human checks the work before the final step exactly.
Speaker 2: For critical decisions, maybe large financial transactions, medical diagnoses, or
Speaker 2: controlling physical systems, you need a human to review and
Speaker 2: approve before the agent proceeds. It builds in a crucial
Speaker 2: safety check and continuous real time monitoring.
Speaker 1: Is essential, watching the agents while they.
Speaker 2: Work, constantly tracking their performance, looking for biases creeping in,
Speaker 2: identifying anomalies or unexpected behaviors. This helps catch problems early
Speaker 2: before they escalate.
Speaker 1: So we need technical guardrails. But how do we build
Speaker 1: that fundamental trust? How do we design these systems so
Speaker 1: people feel comfortable using them?
Speaker 2: That comes down to several core design principles. Transparency and
Speaker 2: explainability are huge.
Speaker 1: Letting people see inside the black box.
Speaker 2: As much as possible, Yes yeah, providing insights into why
Speaker 2: the AI made a certain recommendation or took a particular action.
Speaker 2: This could be visual like saliency maps showing which parts
Speaker 2: of an image led to a classification.
Speaker 1: Or just explaining it in plain language.
Speaker 2: Right like a simple explanation. I recommended this flight because
Speaker 2: it meets your morning preference, is within your budget range,
Speaker 2: and has a good on time record according to recent data.
Speaker 2: It bridges that gap between the machines, process and human understanding.
Speaker 1: So it's not just here's the answer, but here's why
Speaker 1: this is the answer. That helps build confidence, It really does.
Speaker 2: We also need robust methods for handling uncertainty and bias.
Speaker 2: Agents should be able to say I'm not sure or
Speaker 2: quantify their confidence level, and we need techniques to detect
Speaker 2: and mitigate biases in data and algorithms, often involving careful
Speaker 2: data balancing or human oversight.
Speaker 1: And how the AI communicates its output matters too.
Speaker 2: Absolutely. Effective output communication means clearly labeling AI generated content,
Speaker 2: being upfront about data sources or limitations, and avoiding overstating
Speaker 2: capabilities and critically, user control and.
Speaker 1: Consent, putting the user in the driver's seat.
Speaker 2: Giving users meaningful control over the process, allowing customization and
Speaker 2: ensuring explicit, informed consent for data usage are non negotiable
Speaker 2: for building trust, and all of this needs to be
Speaker 2: embedded in ethical development practices from the start, things like
Speaker 2: privacy by design, data minimization, fairness reviews.
Speaker 1: Okay, that's a lot to consider on the safety front.
Speaker 1: Now let's shift to the really exciting part seeing agentic
Speaker 1: systems in action. Where are they making a difference today?
Speaker 1: What are some common use cases?
Speaker 2: They're starting to pop up in some fascinating areas in
Speaker 2: creative and artistic applications. For instance, they're moving beyond just
Speaker 2: generating a static image or piece of music. How so,
Speaker 2: think about film pre visualization. You could have a multi
Speaker 2: agent system where one agent represents the director's creative vision,
Speaker 2: another acts as a technical supervisor checking feasibility like physics, simulation,
Speaker 2: budget constraints, and a third agent is the visualization expert
Speaker 2: actually generating storyboards or animatics. So they collaborate exactly, They
Speaker 2: work together, negotiating between the artistic goals and the technical realities.
Speaker 2: Iterating much fast, faster than a human team might alone.
Speaker 2: It's active collaboration.
Speaker 1: That's a cool example. What about in language like chatbots,
Speaker 1: but more.
Speaker 2: Definitely in natural language processing and conversational agents. Agentic systems
Speaker 2: are enabling much more sophisticated interactions. They can maintain context
Speaker 2: over really long conversations and execute complex multi step tasks
Speaker 2: based purely on dialogue, like.
Speaker 1: A superpowered customer service bot.
Speaker 2: Or think about enterprise knowledge management. You can have a
Speaker 2: team of agents. One understands the user's query query understanding agent,
Speaker 2: another navigates vast internal databases and external sources knowledge navigation agent,
Speaker 2: and a third synthesizes the findings into a personalized, context
Speaker 2: aware answer response synthesis agent. It's way beyond simple FAQ.
Speaker 1: Bots and moving beyond screens into the physical world.
Speaker 2: Robotics Absolutely, robotics and autonomous systems are a huge area.
Speaker 2: Agentic AI allows combining sophisticated language understanding with physical control
Speaker 2: and perception. Give me example, imagine a flexible manufact xturing plant.
Speaker 2: Instead of rigid, pre programmed robots, you could have an
Speaker 2: agentic system orchestrating the work cell. A planning agent adapts
Speaker 2: the workflow based on the specific product being built. Robot
Speaker 2: control agents manage the manipulators, a quality optimization agent monitors
Speaker 2: output and suggests improvements, and an exception handling agent deals
Speaker 2: with unexpected issues like a perk jam.
Speaker 1: So the whole system is much more adaptable, far.
Speaker 2: More adaptable to changes in products, materials, or unforeseen disruptions.
Speaker 1: And finally, what about helping humans make better decisions? Decision support?
Speaker 2: That's another key area. Decision support and optimization. Agentic systems
Speaker 2: can augment human capabilities by understanding complex situations, analyzing vast
Speaker 2: amounts of data, and reasoning about trade offs. Like in business,
Speaker 2: consider global supply chain management. It's incredibly complex, balancing costs, speed, risk, sustainability.
Speaker 2: You could have a multi agent system with a strategic
Speaker 2: planning agent looking long term, an operational optimization agent managing
Speaker 2: daily logistics, a risk management agent monitoring for disruptions, and
Speaker 2: maybe a sustainability agent tracking environmental impact, and.
Speaker 1: They work together to find the best balance.
Speaker 2: They analyze scenarios, simulate outcomes, and provide real time recommendations
Speaker 2: to human planners, helping them navigate those conflicting goals far
Speaker 2: more effectively than they could with spreadsheets alone.
Speaker 1: Wow. We have covered a ton of ground today seriously,
Speaker 1: from the basics of generative AI and what agency really.
Speaker 2: Means, right through the inner workings, how agents represent knowledge,
Speaker 2: how they reason, learn, plan than into.
Speaker 1: The practical side, reflection, using tools, multi agent teams like
Speaker 1: that CWD.
Speaker 2: Model, and exploring real world applications from creative fields to
Speaker 2: complex supply chains.
Speaker 1: And maybe most importantly, we really dug into those critical
Speaker 1: issues of trust, safety and ethics. I think we definitely
Speaker 1: accomplished our mission to unpack this transformative moment in AI.
Speaker 2: It feels like we did, and you know, as we
Speaker 2: look ahead, the conversation naturally rifts towards the ultimate goal
Speaker 2: or maybe myth of artificial general intelligence AGI.
Speaker 1: Right, the idea of AI that can think and learn
Speaker 1: pretty much like a human across almost any task exactly.
Speaker 2: And while we should be clear true AGI is still
Speaker 2: very much a distant goal. There's no practical implementation on
Speaker 2: the horizon yet. But the breakthroughs we're seeing now in
Speaker 2: agentic systems, things like reflection, planning, tool use, collaboration, they're
Speaker 2: all laying some really crucial groundwork. They're like stepping stones,
Speaker 2: and you.
Speaker 1: Can see trends pushing us in that direction, can't you
Speaker 1: like multimodal intelligence AI understanding text, images, audio all at once,
Speaker 1: more like how we perceive the world.
Speaker 2: Definitely, and the leaps in advanced language comprehension models that
Speaker 2: need less data to learn few shot learning understand context
Speaker 2: much better, even develop domain specific expertise.
Speaker 1: Plus that experiential learning we talk about with reinforcement learning
Speaker 1: AI figuring out completely new strategies in complex games or
Speaker 1: robots adapting to new environment with minimal handholders.
Speaker 2: Absolutely, but even with all that progress, the really big
Speaker 2: challenges for AGI remain pretty daunting, such as things like
Speaker 2: teaching AI to truly grasp abstract concepts, to have common sense,
Speaker 2: to learn, how to learn more effectively, and building systems
Speaker 2: that can genuinely understand and navigate the messy, unpredictable, ambiguous
Speaker 2: real world, not just clean structured data.
Speaker 1: It's a long road. So maybe the final thought for
Speaker 1: everyone listening as these AI agents become more and more capable, reasoning, acting,
Speaker 1: even reflecting, like we discussed what kinds of new human
Speaker 1: ingenuity might they unlock, what new partnerships could emerge?
Speaker 2: And tied to that, what responsibilities do we have now
Speaker 2: as the creators, as the users of these increasingly powerful systems.
Speaker 2: How do we shape a future where intelligence, both the
Speaker 2: artificial kind and our own humankind can genuinely flourish together.
Speaker 1: That's a big question to ponder.
Speaker 2: It really is. It invites us all to keep exploring,
Speaker 2: keep learning about this incredibly dynamic field, and to think
Speaker 2: critically about how these powerful tools will integrate into our work,
Speaker 2: our lives, and our society. The journey is really just beginning.