Why Is Everyone Talking About Agents in 2025?
What is an agent exactly? When do you need one? Why agents now? Where are we going with "agents?
Earlier this year, Microsoft quietly made a strategic pivot: instead of betting everything on proprietary models, it began positioning itself as the platform for building AI agents (the A2A protocol and more). As reported by The Information, Microsoft now sees agents, not just raw model horsepower, as the key to long-term growth in AI.
But Microsoft isn't alone. Across the industry, other heavyweight players are doubling down on agent-focused roadmaps:
OpenAI introduced its Responses API and Agent SDK, enabling businesses to build custom autonomous agents capable of web browsing, file manipulation, and task execution—their CEO, Sam Altman, even proclaimed that "2025 is the year AI agents enter the workforce."
Google, Amazon (AWS), Anthropic, and IBM are actively building multi-agent systems, platform protocols, and agent-tooling layers—making autonomy first-class design even if subtly.
Clearly, this isn’t just a buzzword marketing play. It's a coordinated industry-wide bet on autonomy as the next big leap in AI.
So, what is an agent?
The word “agent” gets thrown around so loosely these days that it’s lost much of its meaning. Most so-called agents are just fancy wrappers around a prompt—an API call here, a polished UI there. They reply to you, but they don’t really act.
To understand what a real agent is, it helps to contrast it with something far more common: the workflow. A workflow is a hardcoded process—LLMs and tools strung together by a developer in advance. The model might write SQL, summarize a result, or fetch a document, but the path is fixed or loosely variable with prompting and fuzzy syntax. Even clever routing or feedback loops don’t change the fact that it’s following a script.
An agent, on the other hand, chooses its own path. It doesn’t just execute instructions—it creates them. It decides what to do, adapts when things go wrong, and revises its plan on the fly. Give it a goal and a toolbox, and it tries to solve the problem like a junior colleague might: with initiative and memory.
Anthropic draws the line clearly in their research on effective agents:
Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
Agents are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
OpenAI echoed this at the AI Engineer Summit 2025, defining an agent as:
An AI application consisting of a model equipped with instructions, access to tools, and encapsulated in a runtime with a dynamic lifecycle.
That “dynamic lifecycle” is the heart of it: where workflows follow paths we map out, agents decide what path to take, often reasoning, revising, and retrying along the way.
Even I did mention this in my 2025 workflow breakdown:
Workflows are predictable and consistent. Agents are flexible, reflective, and capable of acting independently.
This isn’t just a semantic difference. If workflows are task-doers, agents are role-takers—and building one demands a completely different design mindset.
Do You Really Need an Agent?
That said, not every problem justifies the leap to full autonomy. In many cases, a well-structured workflow will do the job faster, cheaper, and with fewer surprises.
So before diving in, ask yourself these three questions:
Is the task complex enough that the variability is worth the cost?
Agents loop, plan, and revise. More steps mean more tokens—and more chances for things to go wrong. If a misstep is costly, you might prefer a workflow’s predictability.
Is the tool usage context-dependent?
If you already know what tool should be used when, just encode that logic. But if tool choice varies depending on subtle inputs or evolving user goals, agents become much more valuable.
Is the path to the goal ambiguous or user-specific?
When the process can’t be mapped out in advance—or when the output depends on dynamic context—agents offer the flexibility needed to respond in real time.
In short, don’t reach for agents because the term is trending. Reach for them when your problem is too complex to hardcode, but still structured enough that a model can learn to solve it. That’s a narrow, but growing middle ground. And it’s exactly where we’re starting to see agents show up.
💡Curious whether your use case needs an agent or just a workflow? We’re building a hands-on course that helps you answer that—by actually building both. Just DM me or reply to this if you’d like to get in. We have a few slots we can open to extra motivated people!
The State of Agents in 2025: What Works, What Doesn't
If you want to see what real agents look like in practice, start with Devin, the AI software engineer introduced by Cognition. Devin was designed to autonomously resolve GitHub issues, build and debug code, and even submit pull requests—essentially functioning as a junior developer.
It can reason, plan, and execute in a terminal environment. And it does work on well-scoped, linear tasks. But as Hamel Husain’s testing showed, Devin struggles with ambiguity. It overcomplicates simple problems, chooses infeasible paths, and fails to course-correct. For complex challenges, the reliability just isn’t there yet.
Anthropic’s own agent system, which had full access to a computer interface, showed similar promise—and similar problems. It could click, navigate, and manipulate files on a machine. But its consistency was so poor that even Anthropic quietly moved on from it to focus on their terminal (CLI) code agent.
These systems point to what’s coming. But they also remind us that current LLMs still have limitations when it comes to independent reasoning, memory, and long-term planning. The pieces are coming together, but the glue is still fragile.
Why agents, and why now?
Yes, the image is blurred!
A full video is coming on that topic — stay tuned and follow the YouTube channel updates! :)
Where It’s Going (and How to Get Started)
The future isn’t fully autonomous yet. Most systems today that call themselves agents are still sophisticated workflows in disguise. But the arc is clear: as models get smarter, tools get easier to plug in, and the cost of iteration keeps falling, autonomy isn’t just viable—it’s becoming the default expectation.
2025 won’t be the year agents perfectly work. But it will be the year teams stop asking “should we use an agent?” and start asking “how do we build one that actually runs?”
That’s exactly why we built a course designed not just to explain agents, but to help you build and ship your own—step by step, code in hand, feedback loop included.
A few weeks ago, we launched early access to our new course, Full-Stack Agent Engineering, in collaboration with Paul Iusztin of Decoding ML. We asked our Discord community what they wanted to learn and how, and the response filled up our early-bird $49 tier in minutes.
The next 100 seats at $99? Gone too.
Why am I sending this? First, because I love agents and workflow, and I hate hype. So I want to set things clear on what is what. Second, because we still have slots for very motivated people who want to help us build this course. We are offering 40% off the final release price ($399). Which means, get it now for $240 and help us iteratively improve it as we send you each lesson one by one, or wait for the full price when released.
It gets you lifetime access, direct input on what we build next, and everything you need to go from "I've seen the diagrams" to "I deployed my own agent system."
DM me or reply to this if you are interested!
This is not a tutorial playlist. It's the same framework we use to help companies implement agentic stacks at enterprise scale (often paying >$50K for the same architecture). Here's what you’ll learn to build:
Real agent foundations: memory, routing, tool use, feedback loops
Planning strategies like ReAct, Plan-and-Execute, and multi-agent delegation
Full-stack deployment with FastAPI, Docker, and runtime observability
A production-ready capstone: one agent researching, another writing, both working together
Inside, you’ll also get real-time Q&A support, live office hours, and access to a private Discord where your feedback directly shapes future modules. Early access isn’t just cheaper—it’s collaborative.
If you’ve been watching agents evolve and wondering how to make the leap from hype to hands-on, now’s your chance. Not convinced yet? Find out more information about the course here: https://academy.towardsai.net/courses/agent-engineering
I would like to attend the course