What Makes AI Agents Different (and Is It Worth the Hype?)
I’ve been thinking a lot about AI agents lately, not the hype, but what actually makes them different from the tools we’ve had before. There’s a lot of noise, and most of it sounds like a remix of chatbots or automation scripts. But I do think something has shifted; subtly, but meaningfully.
At a basic level, AI agents are just programs that can take a goal and try to achieve it using some mix of reasoning, memory, and tools. That part’s not new. What is new is how accessible and composable that setup has become. You don’t need a giant engineering team to prototype something that can read an email, decide what to do, query a database, and write a follow-up. That feedback loop—goal → tool use → adaptation—is starting to feel real.
So what’s the big deal? In my experience, the key shift is this: most software automates tasks. Agents aim to automate intent. You’re not just telling a system how to do something, you’re telling it what you want, and it figures out the rest. That sounds abstract, but it’s actually very practical. Instead of stitching together rigid workflows, you can start experimenting with systems that handle ambiguity, pull in context, and adapt on the fly.
That doesn’t mean agents are ready to replace humans or run entire ops teams. Honestly, most of the ones I’ve seen are still fragile. They get confused easily, misuse tools, and need a lot of guardrails. But when they work, even in narrow contexts, they unlock a way of building that feels qualitatively different from past waves of automation.
Is it worth the hype? Probably not yet, if you’re expecting production-ready replacements. But if you’re thinking in terms of prototyping, decision support, or building flexible internal tools, then yes, it’s a powerful new abstraction. I’ve started defaulting to agent-based setups for early PoCs, especially when the task involves decision-making, tool use, or chaining multiple steps.
It’s early. The edges are still rough. But something about the shape of it feels like the beginning of a shift—not just in what AI can do, but in how we think about building systems in the first place.
For example, I built a lightweight agent that monitors a shared inbox for incoming requests related to compliance audits. When a new email comes in, it classifies the request type (e.g. HIPAA, SOC 2, GDPR), pulls relevant documentation from internal wikis and Notion pages, generates a draft response with links, and logs the interaction in our CRM. If it’s missing info or confidence is low, it pings a human reviewer with a summary and suggested next steps.
It’s not flashy, but it saves the team hours of repetitive work every week and reduces errors we used to make when digging through documentation manually. More importantly, it’s not just a script, instead it reasons through the request, chooses which tools to use, and adapts depending on context.
If you’re curious about the kind of architecture this falls under, I’d recommend checking out this fantastic post by Lilian Weng at OpenAI:
It’s one of the clearest breakdowns of how agentic systems work under the hood, covering memory, planning, tool use, and reflection. I keep coming back to it.