Digital Minions or Digital Dream Teams? The Future of AI Collaboration

The distinction between AI agents and agentic AI might sound like semantic hair-splitting, but it represents one of the most significant evolutionary leaps in artificial intelligence development. While interest in both has exploded since late 2022, understanding their fundamental differences unlocks a clearer vision of where AI technology is heading.

TLDR:

• AI agents are autonomous software programs designed for specific tasks with minimal human supervision
• Agents leverage powerful foundation models like LLMs and LIMs as their cognitive engines
• Agentic AI represents a leap forward through coordinated teams of specialized agents
• Multiple agents working together can tackle complex problems through goal decomposition
• Real-world applications range from customer support to medical decision support
• Current limitations include lack of causal understanding and difficulties with long-horizon planning
• The evolution from single agents to collaborative teams mirrors human approaches to complex tasks

AI agents function as autonomous software programs designed for specific tasks in digital environments. They operate independently with minimal human oversight, excel at narrowly defined jobs, and can adapt to changing conditions. Powered by foundation models like GPT-4 and DALL-E, these digital workers become even more capable when "toolmented" – connected to external tools and APIs that expand their abilities beyond their internal knowledge.

Agentic AI takes this concept to an entirely new level. Rather than a single agent juggling everything, agentic systems deploy teams of specialized agents collaborating toward shared complex goals. Think of it as the difference between a lone smart thermostat managing temperature and an orchestrated smart home ecosystem handling everything from weather forecasting to security, energy optimization, and scheduling through coordinated specialist agents.

Both approaches are already transforming industries. Individual AI agents excel at customer service automation, email management, personalized recommendations, and scheduling assistance. Meanwhile, agentic AI systems tackle significantly more complex challenges – coordinating robotics in automated warehouses, providing collaborative medical decision support, automating research processes, and managing adaptive workflows for legal or cybersecurity applications.

Despite their impressive capabilities, significant challenges remain. From limited causal understanding and planning difficulties in individual agents to amplified complexity and unpredictable emergent behaviors in agentic systems, researchers are actively pursuing solutions through improved memory architectures, better coordination frameworks, and stronger ethical guardrails. The potential implications for scientific discovery, global project management, and human-AI collaboration are profound – if we can navigate the technical hurdles responsibly.

What aspects of this collaborative AI evolution do you find most promising or concerning? Join the conversation and share your thoughts on the future of AI agent technology.

Research: AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges 

Chapters

• 0:00 Introduction to AI Agents & Agentic AI
• 2:16 Understanding AI Agents: Core Concepts
• 6:54 Tool-augmented AI: Enhanced Capabilities
• 9:25 Agentic AI: Collaborative Systems Explained
• 12:23 Real-world Applications and Use Cases
• 18:09 Challenges and Limitations
• 22:09 Future Directions and Research Focus
• 25:29 Key Takeaways and Final Thoughts

Transcript

Speaker 1: 0:00

It's really something how fast this whole AI conversation is moving, isn't it? I mean, it seems like yesterday, chat GPT was the big thing, but now you look at Google Trends, we've got figure one showing this and interest in AI agents and agentic AI has just exploded since late 2022.

Speaker 2: 0:19

It really has.

Speaker 1: 0:20

Makes you wonder what's next.

Speaker 2: 0:21

Well, what's really interesting about that trend, that spike, is it shows a real shift, you know, a shift in how we're thinking about building intelligent systems.

Speaker 1: 0:30

How so.

Speaker 2: 0:31

We started with like the fascination with generating text, generating images, but that naturally leads to asking OK, how do we make these things more autonomous, more goal driven?

Speaker 1: 0:42

Right. How do we get them to actually do things Exactly? And that's exactly what we're going to dive deep into today. Our goal here is to unpack these two terms AI agents and agentic AI.

Speaker 2: 0:51

Yeah, try and clarify the differences.

Speaker 1: 0:53

And give you, the listener, a really clear understanding of these well cutting-edge concepts, without just drowning you in jargon.

Speaker 2: 1:01

We're aiming for the essential takeaway.

Speaker 1: 1:02

Exactly Distilled down.

Speaker 2: 1:04

And to help us do that, we're leaning on a pretty insightful research paper. It's called AI Agents vs Agentic AI a conceptual taxonomy, applications and challenges.

Speaker 1: 1:16

Sounds comprehensive.

Speaker 2: 1:17

It really lays out a good framework for understanding this space, which is changing so fast.

Speaker 1: 1:21

Okay, and now it's probably worth remembering this idea of autonomous agents. It didn't just spring up with chat GPT.

Speaker 2: 1:28

Oh, definitely not no.

Speaker 1: 1:29

It's got roots way back right In things like multi-agent systems, mas they often call them an expert systems too.

Speaker 2: 1:37

You had early work thinking about how individual actions lead to social outcomes or how to structure these multi-agent systems Foundational stuff.

Speaker 1: 1:47

So people are thinking about independent digital things operating for a while.

Speaker 2: 1:50

Absolutely, but that historical context is key. Those early systems, while really innovative for their time, they operated under much tighter rules.

Speaker 1: 1:59

Limited, you mean.

Speaker 2: 2:00

Yeah, very limited. Their autonomy was often just pre-programmed logic. They couldn't adapt much, needed a lot of human hand-holding. They didn't have the kind of dynamic learning or the situational awareness we're seeing now with modern AI.

Speaker 1: 2:11

Okay, so let's start with the building blocks, then AI agents. How should we think about these?

Speaker 2: 2:16

Okay, so AI agents think of them as basically economist software programs designed for specific jobs in like a digital environment.

Speaker 1: 2:26

Right and figure four in our source material kind of diagrams, this highlighting three main things Autonomy, being task specific and being reactive, maybe adaptive.

Speaker 2: 2:36

Yeah, those three really capture. Autonomy means you know, once you deploy it it can mostly run on its own, minimal human nudging needed.

Speaker 1: 2:42

Okay.

Speaker 2: 2:42

Task specificity is key for efficiency. They're built for narrow defined tasks Filtering email, querying a database, coordinating calendars. That lets them be precise. And then reactivity and adaptation that's their ability to respond to what's happening around them User commands, maybe API responses, and sometimes they can even learn a bit through feedback or simple rules.

Speaker 1: 3:10

So for autonomy, maybe like a customer support chatbot. You set it up on the website and it handles basic questions without a person jumping in every time.

Speaker 2: 3:14

Perfect example. Or a scheduling assistant managing your calendar, booking things based on rules you set.

Speaker 1: 3:20

And because they're task specific just answering FAQs, just booking meetings they get really good within those lines.

Speaker 2: 3:27

Exactly, very efficient.

Speaker 1: 3:29

And that reactivity piece if a customer asks something weird.

Speaker 2: 3:32

Well, the underlying model tries to understand and figure out a decent response. Or if your scheduling bot finds a calendar clash, it reacts, maybe suggests other times. Some even have little feedback loops. They learn from past interactions, tweak how they respond.

Speaker 1: 3:47

Okay. So what's actually powering these agents? What's the brain?

Speaker 2: 3:50

It's mainly these big foundational models, the really sophisticated AI that understands language, sometimes even images.

Speaker 1: 3:56

Ah, like the LLMs, large language models, gpt-4, pol-m, those things Exactly. They handle the text understanding, planning, LLM, those things.

Speaker 2: 4:01

Exactly. They handle the text understanding, planning, generating responses, but also don't forget LIM's large image models, things like CIP or BLIP2. They handle the visual side.

Speaker 1: 4:14

Right image processing.

Speaker 2: 4:15

Yeah, these foundational models are absolutely central. Llms give them the smarts for language understanding, nuance planning steps, talking back like a human.

Speaker 1: 4:23

Like that GPT-4 powered support agent you mentioned, it can really understand the customer's problem.

Speaker 2: 4:28

Precisely and links add the sense of sight, Analyzing images, identifying objects super important for robotics, autonomous cars.

Speaker 1: 4:37

Ah, okay, the source actually has this great example. In figure five right, an AI agent drone checking out an orchard. Yes, that's a fantastic illustration it uses a limb to spot diseased fruit or damaged trees. Just from the drone's camera feed.

Speaker 2: 4:52

And the cool part is it can then flag those issues, send alerts, all without a person having to sit there watching the video.

Speaker 1: 4:58

That's pretty powerful. The paper mentioned some research on this right, using AI for embodied agents, drones giving them perception.

Speaker 2: 5:06

It does. It really highlights how crucial that combination of autonomy and visual intelligence is. The drone sees, it reasons about what it sees, and then it acts by alerting someone, all on its own. And the fact that these powerful models are often available via cloud APIs open AI, hugging, face, google, gemini makes building these agents way more accessible for developers.

Speaker 1: 5:32

Right, you don't have to build the core model yourself.

Speaker 2: 5:34

Exactly Big shortcut.

Speaker 1: 5:35

Now the paper also talks about generative AI, like maybe the early chat GPT, as a sort of precursor, a stepping stone to these agents.

Speaker 2: 5:44

Yeah, that's a good way to think about it. Gen AI definitely had key traits. It reacted to your input, it could handle text and images, but it was very much prompt dependent.

Speaker 1: 5:53

Stateless, almost Didn't remember the last thing you said.

Speaker 2: 5:55

Exactly, that's a critical difference. Generative AI was amazing at creating stuff, but it couldn't act independently over time. It didn't maintain context or memory from one interaction to the next. You prompted it, it responded, then poof, clean slate for the next prompt. That limitation is really why AI agents the ones that can use tools and hold onto some context became the next logical evolution.

Speaker 1: 6:17

And this is where language models really became the engine right LLM specifically. They turned out to be great at reasoning.

Speaker 2: 6:24

Fantastic at it. They could understand a goal you set, figure out the steps to get there, even decide which external tools they needed.

Speaker 1: 6:30

Like auto GPT or baby AGI. The source mentions using GPT-4 to plan and execute.

Speaker 2: 6:37

Precisely. It's like giving the LLM a to-do list and the permission to figure out how to do it, including using outside help.

Speaker 1: 6:43

OK, so that brings us to toolmented AI agents. That sounds important.

Speaker 2: 6:48

It's a huge step up in capability. By letting these agents connect to external tools and APIs, they overcome some core LLM limits.

Speaker 1: 6:56

Like making stuff up, hallucinations or just not knowing recent events.

Speaker 2: 7:00

Exactly Static knowledge hallucinations tool use helps address those.

Speaker 1: 7:04

So how does this tool augmentation work? The paper mentions tool invocation and result integration. Can you break that down, sure?

Speaker 2: 7:11

Tool invocation is basically the agent realizing hey, I need something I don't have internally to finish this task.

Speaker 1: 7:18

Like needing the current weather or a stock price.

Speaker 2: 7:20

Right, or maybe needing to run some code or query a specific database. When it figures that out, it generates a structured call like JSON, maybe in SQL query, maybe Python code, to that external service or API.

Speaker 1: 7:33

So it sends out a specific request in a format the tool understands.

Speaker 2: 7:37

Exactly Okay. External tool give me this specific info or do this specific action. And then result integration info or do this specific action and then result integration. That's the flip side. The tool does its thing, sends back a response. The agent needs to understand that response and fold it back into its own thinking process. It parses the tool's output, uses that new info and continues working towards the original goal. The paper mentions the REACT framework here.

Speaker 1: 8:00

REACT.

Speaker 2: 8:01

Yeah, it stands for reason and act. It's this loop where the agent thinks about what to do, acts often by using a tool, observes the result and then reasons again based on that new observation. It constantly integrates tool results.

Speaker 1: 8:14

Ah, okay, that makes sense.

Speaker 2: 8:16

Yeah.

Speaker 1: 8:17

It's like asking a colleague to look something up. They tell you the answer and then you continue your work with that new piece of information.

Speaker 2: 8:22

That's a great analogy.

Speaker 1: 8:23

And the source gives some cool examples of what these tool-using agents can do Auto-GPT for market analysis.

Speaker 2: 8:30

Using web search data tools.

Speaker 1: 8:31

Yeah.

Speaker 2: 8:32

GPT engineer writing code by interacting with coding tools and paper QA doing research by querying scientific paper databases. Figure six even shows a news query agent workflow search web. Summarize answer.

Speaker 1: 8:47

Yeah, these examples really show the practical power. They're not just talking anymore, they're doing things by interacting with the digital world through tools.

Speaker 2: 8:54

All right, so that's AI agents autonomous task focused using tools. Got it?

Speaker 1: 8:59

But then we have agentic AI. What's the big leap here?

Speaker 2: 9:03

Agentic AI is really about tackling the next level of complexity. It addresses the fact that a single AI agent, even with tools, hits limits when problems get really big or require serious cooperation.

Speaker 1: 9:14

So it's about multiple agents.

Speaker 2: 9:16

Exactly. It's a conceptual jump from one agent working alone to a system of multiple agents working together in a coordinated way.

Speaker 1: 9:23

OK, so not just one agent trying to juggle everything, but a team, a team of specialized AIs.

Speaker 2: 9:29

That's the core idea. Agentic AI systems involve multiple agents, and maybe each with different skills or knowledge, collaborating towards a shared complex goal.

Speaker 1: 9:39

How do they collaborate?

Speaker 2: 9:40

Through structured communication, often using some kind of shared memory or knowledge base, and sometimes even taking on different roles dynamically as the task unfolds.

Speaker 1: 9:50

The paper mentions goal decomposition. What's that about?

Speaker 2: 9:54

That's fundamental. You give the agentic AI system a big, high-level goal. It doesn't just charge at it, no, it breaks it down, decomposes it into smaller, manageable subtasks Okay, then those subtasks getoses it into smaller, manageable subtasks.

Speaker 1: 10:04

Okay.

Speaker 2: 10:05

Then those subtasks get distributed among the specialized agents in the system. This usually involves multi-step reasoning planning across the agents.

Speaker 1: 10:13

So they have to talk to each other, coordinate.

Speaker 2: 10:15

Definitely they communicate, maybe through specific protocols, they might engage in reflective reasoning, looking back at what worked, what didn't, and they use shared memory to stay on the same page and learn collectively.

Speaker 1: 10:27

Well, it sounds a lot more like how a human team tackles a big project actually.

Speaker 2: 10:32

That's a perfect way to think about it. Everyone has their role. The big task is broken down. There's communication, learning, adaptation.

Speaker 1: 10:39

Right and the source uses that smart home analogy in figure seven to make this difference clear.

Speaker 2: 10:43

Yeah, it's a great visual. On one hand, you have a single AI agent. This smart thermostat does its one job control temperature.

Speaker 1: 10:52

Simple enough.

Speaker 2: 10:53

But then you have the agentic AI system managing the whole smart home. Simple enough, but then you have the agentic AI system managing the whole smart home. It's looking at weather forecasts, your calendar, optimizing energy handling security. With different agents doing different parts of that Exactly A weather agent, a scheduling agent, an energy agent, a security agent, all collaborating, orchestrated together.

Speaker 1: 11:11

Okay, yeah, that really clarifies it. Single agent, single task, agentic system coordinating multiple tasks for a bigger outcome.

Speaker 2: 11:18

Precisely, and the paper summarizes these key differences nicely in Table 1. It looks at things like their basic definition, how autonomous they are, the task complexity they handle, collaboration, learning, applications.

Speaker 1: 11:30

Really highlights that shift from solo execution to team problem solving.

Speaker 2: 11:34

It does and it goes even deeper with tables two, three and four, offering much more granular comparisons.

Speaker 1: 11:41

Yeah, I saw those. They get into initiation type goal flexibility, memory use, coordination strategies. It's like a detailed spec sheet comparing them.

Speaker 2: 11:47

Exactly. It's a detailed taxonomy across different dimensions conceptual, cognitive, architectural showing the progression from basic gen AI to AI agents, to these more complex agentic AI systems. Danielle.

Speaker 1: 12:01

Pletka, and figure eight then shows the architecture evolving right From the core AI agent parts perception, reasoning, action, basic learning.

Speaker 2: 12:09

Marc Thiessen Uh-huh, With examples like lane chain or auto GPT representing that structure. Danielle.

Speaker 1: 12:13

Pletka To the agentic AI architecture, which adds things like ensembles of specialized agents.

Speaker 2: 12:18

Give me a GPT yeah.

Speaker 1: 12:19

More advanced reasoning like React or Chain of Thought, persistent memory, and these orchestration layers or meta agents to manage the whole thing.

Speaker 2: 12:27

Like in chat dev, for instance.

Speaker 1: 12:28

It's like seeing the blueprints get more complex as the capabilities grow.

Speaker 2: 12:32

It is, and what's key is that agentic AI isn't just more agents, it's how they're organized, how they talk, how they're managed to achieve things. A single agent just couldn't.

Speaker 1: 12:42

Okay, this is making sense. We've got the concepts, the architecture, but what about the real world? What can these things actually do? Figure 9 in the source gives a nice overview, right?

Speaker 2: 12:51

Absolutely, they're popping up everywhere. For AI agents first, the paper breaks it down into four main use cases, with examples in figure 10.

Speaker 1: 12:59

Okay, let's run through those AI agent applications. First up, customer support automation and internal enterprise search.

Speaker 2: 13:05

Yeah, the chatbots answering website questions or internal systems, helping employees find company info fast. Think Salesforce, einstein, intercom, fin, notion, ai.

Speaker 1: 13:14

The paper gives that e-commerce example one agent for customer order status, another helping HR find benefits. Info.

Speaker 2: 13:21

Right Shows the dual use external customer facing internal employee support Big time savers.

Speaker 1: 13:26

OK, second, email filtering and prioritization.

Speaker 2: 13:30

Tools like Outlook Superhuman, using AI agents to sort your inbox, pull out tasks. Suggest replies. Figure 10B shows how they learn your habits.

Speaker 1: 13:39

Definitely see the appeal there like an intelligent inbox assistant.

Speaker 2: 13:41

Huge productivity boost for many.

Speaker 1: 13:43

Third application personalized content recommendations and basic data reporting.

Speaker 2: 13:48

We see this constantly right Amazon, youtube, spotify, suggesting things based on what we've clicked or watched or listened to, and in business tools like Tableau, p, pulse or Power BI Copilot use agents, so you can just ask questions about data in plain English and get reports. Figure 10C has examples.

Speaker 1: 14:07

Making data insights easier to get for non-experts.

Speaker 2: 14:10

Exactly Tailored experiences, more accessible data.

Speaker 1: 14:13

And the last AI agent category autonomous scheduling assistance.

Speaker 2: 14:17

Yeah, tools like XAI, reclaim, ai. They handle meeting scheduling, rescheduling, dealing with conflicts by understanding your commands and learning your preferences. Figure 10T shows one. Coordinating across time zones Takes the headache out of scheduling Tetris.

Speaker 1: 14:30

Oh, no or.

Speaker 2: 14:31

Euloria Frees up a lot of time, and Table X in the source lists a bunch more representative AI agents from the last couple of years and what they do.

Speaker 1: 14:39

OK, so those are solid applications for individual agents. Now agentic AI. What kind of advanced stuff can it handle? Figure 11 gives some examples.

Speaker 2: 14:48

Right Agentic AI tackles much bigger collaborative jobs. First category multi-agent research assistance.

Speaker 1: 14:54

Research assistance, like AI doing research Kind of.

Speaker 2: 14:58

Using frameworks like Autogen or Crew AI, you can set up teams of agents to automate parts of research literature reviews, drafting grant proposals, batting searches. Each agent gets a specialized role.

Speaker 1: 15:10

Wow. Figure 11a shows that NSF grant proposal example agents for retrieval. Checking requirements formatting.

Speaker 2: 15:17

Yeah, it's a big jump from one agent finding facts to a coordinated team writing a complex document.

Speaker 1: 15:22

Definitely Okay. Second category intelligent robotics coordination.

Speaker 2: 15:26

Think automated warehouses, drone swarms for agriculture, robotic harvesting places where multiple robots need to work together smoothly.

Speaker 1: 15:33

How do they coordinate?

Speaker 2: 15:35

Usually an orchestrator system managing tasks, specialized robots using shared data, real-time sensor info. Figure 11b has that apple orchard example.

Speaker 1: 15:43

We're at drones, mapping, robot pickers, transport bots, all working together.

Speaker 2: 15:47

It's moving from one robot doing one thing to an entire robotic workforce, orchestrated and autonomous.

Speaker 1: 15:53

Okay, third area for agentic AI.

Speaker 2: 15:56

Collaborative medical decision support. This is really interesting.

Speaker 1: 15:59

How does that work?

Speaker 2: 16:00

Specialized agents collaborating to help doctors with diagnostics, monitoring ICU patients, suggesting treatment plans. They sync up their findings to give a coherent picture.

Speaker 1: 16:11

FIDGIR 11c shows that ICU example agents for vitals history treatment ideas.

Speaker 2: 16:17

Working together to give clinicians a comprehensive view, potentially improving efficiency and maybe even outcomes.

Speaker 1: 16:23

Sounds incredibly powerful, potentially.

Speaker 2: 16:26

It really does. And the final category mentioned is multi-agent game AI and adaptive workflow automation.

Speaker 1: 16:32

Game AI like smarter computer opponents.

Speaker 2: 16:35

Yeah, making non-player characters in games more dynamic, interacting realistically with each other, but also automating complex business workflows.

Speaker 1: 16:43

Like what kind of workflows?

Speaker 2: 16:44

Things like legal document review or managing cybersecurity incident responses. Figure 11D shows that cybersecurity example agents collaborating to spot a threat, analyze it, respond.

Speaker 1: 16:54

So decentralized systems, handling complex, evolving tasks.

Speaker 2: 16:58

Exactly and again. Tablex Mario lists some representative agentic AI models and their applications, showing how fast this area is moving too.

Speaker 1: 17:06

These applications really do show a different scale of problem solving compared to the single AI agents.

Speaker 2: 17:12

Absolutely. The collaboration and orchestration unlock possibilities for tackling challenges way beyond what one agent could handle.

Speaker 1: 17:20

OK, this all sounds incredibly exciting, but there have to be downsides, right Challenges. The paper definitely talks about those. Figure 12 summarizes them.

Speaker 2: 17:28

Oh, absolutely. It's crucial to be realistic. These technologies are powerful, but they come with significant limitations and challenges. Figure 12A focuses on the issues with AI agents specifically.

Speaker 1: 17:39

Let's walk through those AI agent limitations. First one lack of causal understanding. What does that mean exactly?

Speaker 2: 17:46

It means they're great pattern spotters, but they don't really get why things happen. Cause and effect.

Speaker 1: 17:50

So they see correlations but not the underlying reason.

Speaker 2: 17:54

Precisely Like an agent might notice. People buy coffee filters and coffee beans together often, but it doesn't understand why that you need beans to make coffee with the filter. This makes them brittle if the situation changes unexpectedly.

Speaker 1: 18:08

Okay, that makes sense. Next, inherited limitations from LLMs, so the problems with the underlying models bleed through.

Speaker 2: 18:16

They do Hallucinations, making stuff up that sounds plausible, sensitivity to how you phrase the prompt, sometimes shallow reasoning, high computational cost Right. Plus, their knowledge can be outdated and they can definitely reflect biases from the data they were trained on.

Speaker 1: 18:32

And all that impacts the agent's reliability.

Speaker 2: 18:34

Hugely, especially if you're using them for something critical.

Speaker 1: 18:37

The paper also mentions incomplete agentic properties. Sounds like they aren't fully agent-like yet.

Speaker 2: 18:43

Yeah, that's a good point. Even though we call them agents, they often lack true proactivity setting their own goals. Their reactivity might be limited. They aren't great at complex social interactions. Still need a fair bit of human guidance.

Speaker 1: 18:55

So less truly autonomous, more like very advanced tools for specific things.

Speaker 2: 19:00

That's often a fair description right now.

Speaker 1: 19:02

Then there's limited long-horizon planning and recovery. They struggle with multi-step tasks or if something goes wrong.

Speaker 2: 19:09

Yes, because they often work with a limited memory or context window. Planning complex sequences or recovering gracefully from errors is hard for them. They might get stuck or just keep trying the same failing action.

Speaker 1: 19:21

Don't have good error handling or Plan B capabilities built in yet.

Speaker 2: 19:25

Not robustly no.

Speaker 1: 19:26

And finally, reliability and safety concerns. Given all these other issues, it's hard to guarantee they'll always do the right thing.

Speaker 2: 19:33

Exactly Unpredictable behavior, lack of understanding, causality. It raises real questions about safety and reliability, especially in high stakes domains a big research area.

Speaker 1: 19:48

Okay, Now for agentic AI. It seems these problems get worse and new ones appear.

Speaker 2: 19:50

Figure 12B lays these out. That's right. When you have multiple agents interacting, the complexity just explodes, and so do the potential problems.

Speaker 1: 19:56

First is amplified causality challenges. So if one agent struggles with cause-effect, a whole team of them interacting is a recipe for confusion.

Speaker 2: 20:05

Pretty much One agent's action affects the others and the environment. Misunderstandings about causality can ripple through the system, leading to error cascades. A small mistake by one agent blows up.

Speaker 1: 20:17

Yikes, then communication and coordination bottlenecks. Getting them to talk effectively and stay aligned is hard.

Speaker 2: 20:24

Very hard. You need good communication rules, shared understanding of goals, ways to manage shared resources, resolve conflicts. Current systems often struggle here, leading to inefficiency or breakdown.

Speaker 1: 20:35

And emergent behavior and predictability. That sounds potentially scary, like they might do things you never intended.

Speaker 2: 20:41

It's a double-edged sword. Emergence can lead to cool, unexpected solutions, yeah, but it also means unpredictability. You might get unintended consequences, system instability that's a major safety concern.

Speaker 1: 20:52

Right Scalability and debugging complexity also listed. Hard to manage lots of agents, hard to figure out what went wrong.

Speaker 2: 20:59

Incredibly hard. Tracing the thoughts and interactions across many agents, each with its own state it's a nightmare to debug, makes scaling up really challenging.

Speaker 1: 21:08

And trust explainability verification even harder with agentic AI. If you can't explain one agent, how do you explain a team?

Speaker 2: 21:18

Exactly, the black box problem gets multiplied, understanding the collective reasoning, verifying the whole system is reliable and safe. We need major advances in XAI and formal methods here.

Speaker 1: 21:28

Security and adversarial risks also seem amplified. More agents, more ways to attack the system.

Speaker 2: 21:34

Definitely the attack surface is bigger. Compromise one agent and you might be able to disrupt the whole team, steal data, manipulate others. New vulnerabilities emerge from their interactions.

Speaker 1: 21:44

And finally, ethical and governance challenges and immature foundations. Basically, we're still figuring out how to build and manage these things responsibly.

Speaker 2: 21:52

That sums it up well. It's a new field. We lack standardized architectures, robust theories and the ethical questions accountability when things go wrong, bias, amplification across agents. Ensuring alignment with human values are huge and largely unsolved research gaps.

Speaker 1: 22:06

Okay, so a lot of challenges, but people are working on solutions, right? Where is this heading? Figures 13 and 14 give some ideas for the path forward.

Speaker 2: 22:14

Absolutely. Figure 13 highlights 10 key design strategies people are actively researching and implementing to try and tackle these limitations.

Speaker 1: 22:21

We see air rig retrieval, augmented generation popping up a lot, using external knowledge to ground agents, reduce hallucinations.

Speaker 2: 22:29

Trucial for both single agents and giving agent teams shared up-to-date context.

Speaker 1: 22:35

And tool-augmented reasoning. Letting them use tools seems fundamental and that agentic loop, react, reason, act, observe. That seems key for iterative behavior.

Speaker 2: 22:45

Yes, r-reg grounds them tools, give them capabilities, and the React loop allows for that dynamic, self-correcting cycle of thinking and doing.

Speaker 1: 22:53

Better memory architectures are also mentioned, helping agents remember more, plan longer term.

Speaker 2: 22:58

Essential for consistency in complex tasks, especially for agentic AI, where shared memory helps coordinate.

Speaker 1: 23:04

And for agentic AI, specifically multi-agent orchestration frameworks to manage the teams, plus reflexive or self-critique mechanisms so agents can spot their own mistakes.

Speaker 2: 23:14

Right Orchestration provides structure for collaboration. Self-critique improves reliability and quality. Agents checking their own work or each other's.

Speaker 1: 23:21

Programmatic prompt engineering pipelines, making communication more reliable, less fiddly.

Speaker 2: 23:27

Trying to make prompting less of an art, more of a science for more consistent agent behavior.

Speaker 1: 23:32

And looking at causal modeling and simulation-based planning, trying to give them that deeper understanding of consequences.

Speaker 2: 23:39

Exactly, Helping them anticipate outcomes better, especially in complex multi-agent setups. Make more robust plans.

Speaker 1: 23:46

And then monitoring, auditing, explainability, pipelines building, transparency and trust, plus governance-aware architectures for ethics and safety.

Speaker 2: 23:55

All absolutely vital for responsible development and deployment. We need to be able to understand, oversee and ensure these systems align with our values.

Speaker 1: 24:03

Okay, and figure 14 gives a glimpse of the future evolution For AI agents. It looks like more proactivity, better tool use, maybe real causal reasoning, continuous learning and a big focus on trust and safety.

Speaker 2: 24:14

Yeah, the trajectory is towards more capable, reliable, proactive cognitive assistance, moving beyond just reacting.

Speaker 1: 24:21

And for agentic AI scaling up better orchestration, persistent shared memory, simulation for planning, strong ethical frameworks and maybe domain-specific systems.

Speaker 2: 24:32

That seems to be the direction Towards large-scale coordinated machine intelligence, tackling really complex problems, but hopefully within strong ethical boundaries and tailored for specific needs.

Speaker 1: 24:44

Okay, so pulling it all together, the key takeaway really seems to be AI agents are like specialized autonomous workers using tools for specific jobs.

Speaker 2: 24:53

Individual contributors.

Speaker 1: 24:54

While agentic AI is about building collaborative teams, ecosystems of these agents, to tackle much bigger, more complex problems together.

Speaker 2: 25:02

That's a great summary. Both leverage powerful AI models, but agentic AI is a definite step towards more complex cooperative intelligence.

Speaker 1: 25:10

And clearly lots of challenges remain, but the research is pushing hard to make these systems safer, more reliable, more understandable.

Speaker 2: 25:17

The hurdles are significant, no doubt, but the focus on areas like causality, memory, orchestration and ethics is where the progress needs to happen and is happening.

Speaker 1: 25:25

Which leaves us and you, the listener, with a final thought to chew on. Which leaves us and you, the listener, with a final thought to chew on. Imagine what truly collaborative AI could mean, for say, accelerating scientific discovery or managing massive global projects, or even just simplifying our daily routines.

Speaker 2: 25:45

What are the fundamental breakthroughs still needed to really unlock that potential and, perhaps more importantly, what are the ethical guardrails we absolutely need to put in place as we navigate this future of increasingly capable human-AI collaboration?

Speaker 1: 25:55

Definitely a lot to think about there.

Speaker 2: 25:57

It certainly is. This whole area raises some profound questions.

Speaker 1: 26:00

Well, thank you for diving deep with us today. We really hope this conversation has helped clarify these important concepts of AI agents and agentic AI and where they might be taking us.