AI Agents: The Most Overhyped or Most Underrated Trend of 2026?

Why I'm Asking This

Every timeline, every newsletter, every product launch this year has the word "agent" stapled to it. Agentic this, autonomous that. It's gotten to the point where "agent" has almost lost meaning — sometimes it means a genuinely autonomous multi-step system, and sometimes it just means "a chatbot with a slightly better system prompt."

So I wanted to actually sit with the question honestly: are AI agents the real deal, or is this the GenAI equivalent of "blockchain will fix everything" from a few years back?

The Case For "Overhyped"

Let's be fair to the skeptics first, because a lot of their points are valid:

• Reliability is still shaky. Ask an agent to complete a multi-step task and there's a real chance it goes off the rails somewhere in the middle — misreads a tool output, loops on the same action, or confidently does the wrong thing. For anything high-stakes, that's a dealbreaker.
• "Agent" gets slapped on everything. A huge chunk of products calling themselves "agentic" are really just a chatbot with function calling bolted on. The label is doing a lot of marketing work it hasn't earned.
• Cost and latency add up fast. A "simple" agent task can involve a dozen model calls chained together. That's a dozen chances for errors, and a real cost in tokens and time compared to just doing the task yourself.
• Trust is the real bottleneck, not capability. Most people aren't hesitant to use agents because the tech can't do it — they're hesitant to hand over their email, calendar, or payment info to something that might make a mistake they can't easily undo.

If you judge agents purely by the current gap between the marketing and the day-to-day reliability, "overhyped" is a fair verdict.

The Case For "Underrated"

But I think dismissing agents entirely misses something real happening underneath the hype:

• The trajectory matters more than the current snapshot. A year ago, agents that could reliably use even two or three tools in sequence were rare. Now multi-step tool use, self-correction, and long-running tasks are becoming genuinely usable. Judging the category by where it started, not where it's heading, undersells it.
• The boring use cases are already working. Not the flashy "AI runs my whole business" demos — the quiet stuff. Automated code review, scheduling, research summarization, customer support triage. These aren't headline-grabbing, but they're already saving real time for real teams.
• Tool ecosystems are maturing fast. A big reason agents struggled early on wasn't the model — it was the lack of structured, reliable tools for them to call. That infrastructure (APIs, connectors, structured tool definitions) is catching up quickly, and that's exactly the kind of unglamorous progress that tends to compound.
• The skeptics keep moving the goalposts. Every time agents get better at something, the bar for "real" agentic behavior gets raised again. That pattern usually shows up right before a technology actually becomes normal and boring — like how "is this really intelligent" questions faded once chatbots just became a normal thing people used daily.

Where I Actually Land

Here's my honest take: both things are true at the same time. The current wave of agent hype is overhyped in the short term — most "fully autonomous" demos are cherry-picked, and a lot of products slapping "agent" on their name are exaggerating what they actually do.

But I think it's underrated in the medium term, because the underlying trend — models reliably chaining tool use, correcting their own mistakes, and handling longer tasks without constant supervision — is genuinely improving, quietly, in the background, away from the demo reels.

The mistake is judging agents as one single thing. Narrow, well-scoped agents doing a specific job (triaging tickets, drafting reports, running a fixed workflow) are already quietly useful today. General-purpose "does anything autonomously" agents are still mostly hype. Conflating the two is where most of the overhype vs. underrated confusion comes from.

What This Means If You're Building With Agents Right Now

If you're experimenting with agents (like I am), the practical lesson is: don't chase the general-purpose dream yet. Build narrow. Give an agent one well-defined job, a small set of reliable tools, and clear boundaries on what it can and can't do without human approval. That's where the actual value is being captured right now — not in the "does everything" demos.

Closing Thought

Most transformative tech goes through this exact phase — overhyped in the short term because expectations run ahead of reality, underrated in the long term because people write it off right before the boring, unglamorous improvements start compounding. My bet is AI agents are somewhere in that transition right now, and the next year will make it a lot more obvious which side of the question actually wins.

Would like to know where other people land on this too — overhyped, underrated, or somewhere in between like me?