Beyond Chat: Why AI Agents Are the Next Frontier of Productivity
The Agent Hype Cycle
A lot of people are talking about AI agents right now, but do we really know what they are?
Over the past few months, I’ve seen the word “agent” get used to describe everything from a clever chat prompt to a fully autonomous system running multi-step workflows across apps. And while I get the excitement... I feel it too.. it’s also clear that we’re still in the early innings.
As someone who’s always looked for ways to work smarter, not just harder, I’ve leaned on automation wherever I could. Back when I worked in fashion retail, I built my own planning and reporting workflows to reduce repetitive manual work. Later, building Dayflash and now Surfn, my co-founder and I constantly automated whatever we could - first through basic logic, and now increasingly with AI. So I’ve been paying close attention to this agent wave. But I’ve also learned to question the hype.
Because the truth is, most “AI agents” today aren’t what people think they are. They’re not little digital employees managing your inbox or running your business on autopilot. They’re often just large language models (LLMs) dressed up with a few added instructions, maybe calling an API or two.
As Guido Appenzeller from a16z put it,
"The simplest thing that I've heard being called an agent is basically just a clever prompt on top of some kind of knowledge base."
It looks impressive from the outside.. but that doesn’t mean it’s doing any real thinking or planning underneath. That distinction matters. Especially if we want to move from novelty to something that actually drives productivity.
What Makes an Agent… an Agent?
At every stage of my career, I’ve leaned on automation. In retail, I automated the repetitive parts of planning and reporting so I could focus on the bigger picture—like forecasting demand or improving margins. As a founder, I’ve done the same with product operations. If something could be systematized, I’d systematize it. And now with AI, we’re taking that even further. But there’s a key shift happening that goes beyond automation.
Agents don’t just execute a task... they reason through it.
According to Google’s white paper on AI agents, the most important breakthrough isn’t just the model... it’s the orchestration layer. This is the part of the system that enables an agent to plan, make decisions, call tools, and adapt to new information in real time. These agents aren’t just responding to a single prompt—they’re chaining together multiple steps using reasoning techniques like ReAct, chain-of-thought, and tree-of-thought to reach a goal.
The best way to think about it might be this:
An AI assistant gives you an answer.
An AI agent takes action to get a result.
That includes fetching live data, making decisions based on it, updating systems, triggering follow-ups, and even collaborating with other agents. Tools like LangChain and platforms like Vertex AI are helping developers build this orchestration logic.. linking reasoning, memory, tools, and actions together into a single agentic flow.
"Agents extend [model] knowledge through the connection with external systems via tools."
— Google white paper via VentureBeat
This leap.. from response to execution... is what makes the agent shift so compelling. But it also makes it a lot harder to build.
Reality Check – What’s Working (and What’s Not)
There’s a real difference between what the market hopes AI agents can do... and what they’re actually doing in production today.
If you spend enough time online, you’d think every company has a fleet of autonomous agents running the business. But according to IBM and Deloitte, most “agents” today are still just large language models with basic tool use layered on top. There’s nothing wrong with that.. but it’s important to call it what it is.
As Maryam Ashoori from IBM puts it:
"What’s commonly referred to as ‘agents’ in the market is the addition of rudimentary planning and tool-calling capabilities to LLMs."
We’re not at the point yet where agents can think through multi-step workflows entirely on their own, collaborate seamlessly with other agents, or operate for long periods without human oversight. True autonomy.. especially across multiple agents with different roles... is still a work in progress.
And honestly, that’s okay.
It’s tempting to dream big, but even the best tools right now need serious orchestration, guardrails, and supervision to avoid costly errors. That’s not failure... it’s just where we are in the timeline. I’ve seen it firsthand: even with well-designed prompts and strong agent frameworks, you still need to monitor outcomes, set constraints, and define very clear success criteria.
So yes, the vision is exciting. But we should be clear-eyed about the current reality. Most of what’s being called “agentic AI” today is more like assisted automation with a bit of reasoning.. useful, but not autonomous.
From Sandbox to Systems – Agents in Action
Despite the hype and early limitations, some companies are starting to put agents to real use... and the results are promising.
We’re seeing early traction in a few key areas. In law, Harvey is helping firms draft legal documents and respond to client requests. In customer support, Sierra is powering always-on agents that don’t just reply, but resolve. In robotics, companies like Skild AI and Figure are building foundation models that bridge software and the physical world.
And in software development, tools like Cursor are showing what happens when agents move out of the sandbox and into production.
I’ve been closely watching Cursor because it’s one of the clearest examples of agents doing real work. With access to advanced models like Claude 4 and Gemini 2.5 Pro, developers are going from high-level specs to working features—faster than ever. These aren’t just prompts and code snippets. These agents read through repositories, propose solutions, edit code, and follow through on iterative feedback. It’s a glimpse of what’s possible when agents are actually deployed to do work—not just answer questions.
To power these experiences, companies are using orchestration frameworks like LangChain and integrating agents into enterprise platforms like Slack and AWS Bedrock. The goal isn’t experimentation anymore—it’s execution. Leaders at companies like Palantir and AWS are aiming for 3x, even 5x productivity gains as these systems scale.
As Sequoia put it:
"This year is a turning point: AI graduated from an answer engine to an action engine in the workplace."
And Lisa Lee, from Salesforce captured it even more bluntly:
"The AI agent of tomorrow will be a force multiplier unleashed."
It’s still early.. but we’re starting to see what that future might look like when agents stop demoing and start delivering.
The Real Opportunity – And How to Stay Grounded
There’s no doubt AI agents are evolving fast, but staying grounded is what separates builders from bandwagoners.
The real opportunity here isn’t some futuristic vision where fully autonomous agents run entire businesses without oversight. What excites me most is something more practical: orchestration. Agents that work within smart guardrails. Agents that take action, not just give answers. Agents that free up our time so we can focus on the parts of work that actually require creativity, strategy, and judgment.
That’s where the value is. And that’s what we’re building toward.
At Surfn AI, we’re starting with sales and marketing workflows.. especially for creators and brands who are overwhelmed trying to do everything manually. That’s the space we know. And it’s where we’ve already seen how much time and energy can be saved when an AI agent handles things like lead capture, appointment booking, or follow-up flows.... so the human behind the business can focus on growth.
There’s still a long way to go. As Deloitte puts it:
"Companies need to consider the challenges of gen AI, plus the complexity of building bots that can reason, act, collaborate, and create."
But we don’t need to wait for perfection to get started. The shift is already underway.. and the builders who stay grounded in reality will be the ones who create the most impact.
We’re just getting started. And I’m excited to keep building in the real world, not just the hype cycle.