Are Vertical AI Agents still worth building?
I recently came across some news:
Claude for Financial Services officially launched in mid-July. It’s designed specifically for the finance industry, integrating data from platforms like PitchBook, Morningstar, Snowflake, S&P Global, and Databricks to support market research, due diligence, and investment decisions. During early testing, Claude Opus 4 hit 83% accuracy on complex Excel tasks.
It really makes me question how much room there is left for AI Agent startups ----LLMs are getting better at handling more and more tasks on their own.
For an AI Agent to have long-term value, it must be able to understand, remember, and adapt to a user's evolving preferences and context ---- something LLMs still struggle with due to their limited memory and continuity.
Also, not many vertical AI Agents seem to be breaking out yet. Off the top of my head, I can only think of Icon, telli, and Exa. Curious to see what other intereating vertical AI Agent products show up on Product Hunt.
Replies
Doraverse
I think LLMs will naturally expand into verticals where there's clear profit and productivity gain, like coding. It's already proving to reduce costs and speed up work.
But for narrower verticals, going too deep would cost them time and focus. LLM providers are in a constant race on general performance, so diving into niche domains isn't a top priority.
That leaves room for specialized AI startups to build where LLMs won't go (yet).
Codirigent
If you look at YC startups, it went from general AI solutions into more vertical AI agents that focusing on solving specific problems.
One of the example is insurance verification & billing at dentist clinics.
A lot of companies are implementing SLM instead of LLM due to the limitation of LLM and the cost of hosting it, in the end, all the products are aiming for make the work easier, faster and cheaper. If your product does all LLM offers but cheaper than why not?
Vertical AI Agents are absolutely still worth building — but only if they go beyond wrapping an LLM with a single workflow. The real opportunity now lies in deep specialization + system-level integration.
At Growstack, we’re doubling down on this. The agent itself isn’t the product — it’s just the interface. What matters is:
* Domain-specific context: Know the terminology, data structures, and quirks of the industry.
* Workflow automation: Handle multi-step tasks, not just chat.
* Memory & adaptation: Learn from usage, not just prompts.
Claude for Financial Services is a strong signal — not a threat. It validates the demand for agents that speak industry language. The difference is, startups can still move faster and go deeper in niches where big players won’t.
If you're building vertical AI agents, focus less on what the LLM can say and more on what the agent can do. That’s where lasting value is created.