Agentic AI: The New 'Groundbreaking Technology' of 2025

Summary

In 2022 it was Chain-of-Thought, in 2023, it was Retrieval Augmented Generation, in 2024, it was reasoning models and multi-agent collaboration, and with 2025, we have our newest hot topic: Agentic AI, and it’s not politely waiting for an invitation. It’s already barged in, rearranged the furniture, and made itself at home. 5 months into this chaos, it’s about time we take a look at what stuck and why we should care. Or not.

Agentic AI: What It Is, and Why It’s a Big Deal

Agentic AI is the first step into AI being something more than a glorified autocomplete. These systems don’t just generate content, they pursue goals, adapt to changing circumstances, and make decisions. They’re built to operate with autonomy, handle ambiguity, and collaborate with other agents, all while navigating a world that’s messy and unpredictable.

This is not a trivial upgrade. For decades, AI was the world’s most diligent intern: fast, tireless, but fundamentally reactive. Agentic AI is the intern who starts their own company, hires a few friends, and tries to automate your job. It’s the difference between a chess engine that suggests moves and one that decides to play a tournament, trash-talks the competition, and books its own travel.

The historical parallel is the early Internet. Remember when every network was its own little island, and nothing talked to anything else? TCP/IP and HTTP came along, and suddenly, everything was connected. Agentic AI is at that same crossroad: fragmented, chaotic, and on the cusp of something huge.

Example: The Travel Planning Circus

Let’s say you want to plan a five-day trip from Beijing to New York. In the old world, you’d have a single agent querying flights, hotels, and weather, one after the other, like a very polite but slightly dim assistant. With agentic protocols, you get a circus: specialized agents (flight, hotel, weather) negotiating, collaborating, and occasionally squabbling to build you the perfect itinerary. Some protocols (MCP) keep things centralized and tidy; others (A2A, ANP) let agents cut deals across organizational lines; the most ambitious (Agora) translate your vague requests into structured protocols that the agents can actually use.

The result? More flexibility, more resilience, and a system that can adapt on the fly when your plans (inevitably) change.

The Many Faces of AI Agents: Not All Interns Are Created Equal

If you want to understand agentic AI, you have to get comfortable with the idea that “agent” is a spectrum, not a monolith. We are nowhere close to perfecting this tech, which means at the shallow end, you’ve got simple reflex agents (think Roombas and rule-based chatbots). But on the deeper end, you have multi-agent systems, where swarms of specialized agents negotiate, collaborate, and occasionally bicker their way through complex tasks.

Most agentic systems today are a Frankenstein’s monster of:

Perception: Sucking in data from every available source.
Memory: Juggling both short-term context and long-term knowledge, sometimes with the grace of a goldfish, sometimes with the recall of an elephant.
Planning: Breaking down big, hairy tasks into bite-sized chunks.
Tool Use: Calling APIs, querying databases, or even controlling physical devices.
Action Execution: Actually doing stuff in the world, not just talking about it.
Learning: Adapting over time, sometimes in ways their creators didn’t expect.

One thing I want to point out is that just like the majority of machine learning as we know it, we’ve taken a page out of God’s blueprints in how we design and improve these frameworks. A lot of research on LLM memory deals with System 1 and System 2 thinking and Theory of Mind, while things like tool usage came from human limitations and specialization. And that’s not even mentioning the elephant in the room with neural networks.

Roadblocks and Headaches: Why Agentic AI Isn’t Running the World (Yet)

So why isn’t agentic AI running everything already? Because it’s hard. Really hard. Here’s what’s in the way:

Standardization: Let’s say that we all have our agent (or team of agents). The thing is, all these agents need to talk to each other and work together to bring out their true potential. But right now, they’re speaking in dialects so different, even Google Translate would throw up its hands. The lack of standardized protocols is the bottleneck, the Achilles’ heel, the thing keeping agentic AI from taking over the world (for now).
Security and Privacy: The more autonomous the agent, the more you have to worry about what it’s doing with your data. Authentication, encryption, access control, these aren’t optional extras, they’re table stakes. Something something with great power comes great responsibility.
Reliability: Agents are only as good as their last meltdown. Remember that robot helper that drove itself down a flight of stairs after a rough day at work? In high-stakes domains, you need systems that don’t just work most of the time, but all the time.
Evaluation: There’s no Consumer Reports for agentic AI (yet). Benchmarks are scattered, and everyone grades their own homework. Competitors are forced to call each other out, and corrections and revisions to official reports happen all the time.
Dynamic Tool Integration: Plug-and-play is still a fantasy. Most integrations are brittle, manual, and about as fun as assembling IKEA furniture with missing instructions. You’re left with a mini helicopter that runs on three pig hooves on a train track. It might do the job at the moment, but it’s only seconds away from breaking down.

And let’s not forget the human factor: trust, governance, and the uneasy feeling that we might be building something we can’t fully control. We’ve all watched too many sci-fi movies, and it doesn’t get any easier from there.

The Road Ahead: What Needs to Happen Next

Short Term:
We need to know what these things are capable of without trying to brute force our way up th leaderboard. Quantiative certification and robust benchmarks on performance, security, and robustness. Policies need to keep up with this as well, putting clear guidelines early on about what agents should be able to do and not do.

Mid Term:
Layered protocol architectures, LLMs with built-in protocol knowledge, and the integration of ethical and legal constraints. The agent ecosystem will start to look less like a patchwork and more like an actual ecosystem-dynamic, adaptive, and (hopefully) resilient.

Long Term:
The real prize is collective intelligence: networks of agents that can solve problems no single agent (or human) could manage. Think of it as the Internet, but for cognition. We’ll see the rise of agent data networks dedicated infrastructures for structured, intent-driven information exchange.

Personal Thoughts

What excites me most about agentic AI isn’t the buzzwords or the VC pitches, it’s the intentionality. We’re finally building systems that aren’t just reacting to a prompt, but negotiating goals, juggling constraints, and adapting mid-run. That’s not just impressive engineering, it’s a conceptual shift in how we define “intelligence” in machines. For the first time, it feels like we’re sketching the cognitive scaffolding of something more than a tool. Something almost organismic.

But I’m also wary.

The term “agent” gets thrown around a lot these days, usually without much rigor. Is a function-calling LLM with a planning loop and memory an agent? Technically, sure. But so is a glorified macro if you squint hard enough. The real challenge, I think, is not just in building more capable agents, but in building agents that understand the boundaries of their own competence. That’s where the real frontier lies: agents that can say “I don’t know,” defer, escalate, or revise their own plans. Intellectual humility, not just ambition.

And let’s be honest, much of what’s being paraded right now as agentic AI is just a rebranding exercise on old ideas: finite-state machines wrapped in Python with a GPT glued to the front. Cool demos, brittle backend. If we want this tech to survive the hype cycle, we need fewer sizzle reels and more system audits. Less vibes-based evaluation, more principled certification.

Still, I’m optimistic. Because behind the noise, the researchers who are actually in the weeds, the people building new protocols, defining new evaluation metrics, and fixing the stupid bugs at 2am, they are the ones pulling this field forward. Not for the clout. Not for the paper count. But because they see the outline of something real and strange and beautiful.

And I want to be there when it unfolds.