Every few months, someone declares SaaS dead. The argument has gotten louder in 2026: why pay for vertical tools when an LLM and a GitHub Action ca...
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That's well said. I'm of the mind that if AI upgrades everyone's productivity, it just raises the bar for SaaS.
Yes, software as a service which fundamentally offers something easily replicated by AI is not very valuable, however there's a big opening for just offering... better SaaS. The bar is raised, but you have the opportunity to perfect and grow a tool through iteration which is further enabled by AI. If you keep up the focused investment, there will be clear value people can't just get for free with agents.
Completely agree. Bar is higher. SaaS products will embrace the AI. AI and agents are compliment, not replacement to SaaS and other software tools
Great point!
That’s also the conclusion I reached after testing OpenClaw for four weeks. Using AI without the goal of moving beyond AI isn’t the right way to use it, in my opinion.
AI that automates a series of actions is great. But AI that builds software to automate that series of actions independently of calling an LLM during execution (SaaS or local only) is even better!
To echo your conclusion: AI should be used to set up the plumbing system, not to redo the plumbing system every time a user turns on the faucet.
Yes, AI and agents are flexible so easy to build something just to validate the idea and iterate. Build proper systems to go to production. We just need to verify AI outputs only once.
Yeah I don't believe it either, that people are going to "vibe code" ("in an afternoon", even) anything more complex than a trivial piece of software, and in the process making SaaS obsolete - fairy tales of la-la land ;-)
Great read — I like how you describe SaaS as the “plumbing” that keeps fragile agents reliable. It makes sense that flashy demos often fail once they hit real production.
I’d love to hear your thoughts on this: in 2026, what are some successful SaaS products or models that really show how agents and workflows can work together effectively?
The problem with SaaS in 2026 is funding availability, not the current state of AI agents, which can't yet completely replace existing solutions with in-house versions. There's no way that I can reasonably ensure consistency today from multiple agents answering subtly different questions and building the infra as they go - but that's not why SaaS is in trouble. SaaS is in trouble because many funding sources think there's a chance AI will be able to do vastly more in 3 to 5 years, which is the time horizon they use to expect a payback.
Can you build a SaaS product in 2026 and get some customers, delivering them significant ROI? Yes.
Can you expect to fund the rollout of that product in 2026, using established sources of startup funding? Maybe, depends on how much you need, but it's way tougher.
If some distant future is consistent, on-demand, and constructed applications that perfectly solve the immediate requirement, then people/orgs selling software are fundamentally decimated. That future is possible but not ensured. What is clear is that the barrier to entry for a new player into the market of a pure SaaS company is dissolving right now. That's only going to get worse, and that is what drives the availability of funding.
I agree with the sentiment and funding for pure SaaS is hard. I think SaaS will evolve and embrace AI. Maybe a new paradigm of agents as a service. I think it still make sense to buy a tool to solve common problems instead of building everything in house
Well said. Thanks for advocating for sanity amidst all the AI hype and FUD (fear, uncertainty, and doubt).
the whole point of saas is building something that users (companies) can just use without needing to maintain it themselves.
And people who say saas is dead because everyone has AI now so everyone will build their own tools from now anyways so saas won't be need. These people are utterly briainless.
If that would happen then a company will spend most of its time in building and maintaining its internal saas and won't be able to do the actual needed work.
So SAAS is very much alive and will remain forever.
Strongly agree with your "agents learn faster as shared tools" point. Internal tools cap out on edge cases indeed. How are you handling cold start issues though without enough customer base for the flywheel to kick in?
I'll make it work for first customer, learn from it, make it work on second customer and so on until the fly wheel kicks in.
silent failures are the SaaS killer argument most skip until customers notice. ran into this with scheduled agents - demo fine, then an upstream rename breaks output silently. what do you use to catch that kind of drift?
This is where we can utilise existing tools we built so far. Monitoring and Alerting. Regular testing and QA. If output is not deterministic we can use LLM to verify as well.
monitoring catches the noisy failures fine. the silent ones don't - wrong format, stale data logged as fresh, half-finished workflow reporting success. that's where LLM-as-judge earns its latency cost.
Yes it has it's own place. Not discounting that
yeah - latency cost is worth it when the alternative is bad data making it downstream silently.
This hits really well.
The “agent vs workflow” framing is something I’ve been thinking about too, especially when building real products. Agents are great for figuring things out, but once you move into production, you really feel the need for structure and repeatability.
I also liked the point about maintenance cost. That part usually gets ignored in the hype, but it becomes the real bottleneck pretty quickly in real systems.
In age of AI where building is so easy, it is easy to ignore maintenance cost, maybe because it is "problem for future". But it does accumulate
100%. At first it feels like speed, but a few weeks later you realize you’ve basically built something you have to babysit 😅
I’ve started trying to lock down workflows earlier now, even if it feels slower upfront.
Building SaaS in 2026 hits different because of AI agents. I'm building FlowBoard.dev, a project management tool for small dev teams, and the biggest bet right now is making the backlog agent-executable.
The idea: you write your tasks with enough context (acceptance criteria, dependencies, tech notes), and AI agents pick them up overnight. Dev agent writes code, tester agent validates, reviewer agent checks quality. You wake up to PRs.
We already have a REST API (14+ endpoints) and an MCP server so AI tools like Claude can read and update tasks directly. The infrastructure for agent-driven development is there, the question is just how autonomous you let them be.
If you're building SaaS now, I'd think hard about whether your product can be both human-facing AND agent-facing. That's where things are heading.
On my third startup now and the 2026 case for SaaS is more interesting than the discourse suggests. AI didn't make SaaS easier to build — it made distribution harder, because the supply of decent software is going up faster than buyer attention. The teams winning aren't the ones with the best AI features; they're the ones with the most concentrated take on a real workflow problem and the discipline to ship fewer things, deeper. Most of the "AI commodifies SaaS" takes assume a buyer who's evaluating purely on capability. The actual buyer is overwhelmed and is buying the team that understands their job, not the model behind it.
Agree on buyer mindset. They are not buying because they cannot build it, they buy because they want someone else to take care of it. Focus on their own secret sauce and outsource common stuff
The line about agents being "a reasoning layer wrapped around hope" is going to stick with me. It captures something I've been circling but hadn't put words to: the difference between a system that produces a correct answer and a system that produces a plausible answer isn't visible in the demo. Both look the same. The distinction only shows up over time, in the edge cases, on Tuesday morning when the API changed and nobody noticed.
What I think you're pointing at with the "agent first, workflow second" pattern is really a design philosophy about where uncertainty belongs. Agents are useful precisely because they handle ambiguity—messy tickets, unstructured context, things that don't fit a schema. But once you've resolved the ambiguity and know what the output should look like, keeping the agent in the loop is just paying for uncertainty you've already eliminated. The workflow is the artifact of a decision already made.
The trap I see teams fall into isn't that they use agents. It's that they treat the agent as the permanent solution rather than the exploration phase. It's like leaving the scaffolding up after the building is finished because the scaffolding was impressive and people liked watching it go up. The scaffolding served its purpose. The building is what you actually need to live in. Curious how you decide when an agent-driven workflow has stabilized enough to harden into a concrete pipeline. Is it a time threshold, a confidence metric, or just the moment you realize you've been running the same prompt for two months without changing it?
Agents are exploration tools. The mistake is shipping the exploration. Reliable software starts where the agent conversation ends.
Agree
Agent fragility with changing APIs is critical. For health AI, cross-referencing an Ayurvedic 'kashayam' (herbal decoction) against modern data hits a complex 'upstream'. Fragile agents mean clinical nonsense.
This structural moat - US/EU health AI avoiding traditional remedies due to liability - demands robust, specialized SaaS. Ad-hoc agents risk deep cultural knowledge.
Building reliable, context-aware validation for diverse health practices makes specialized SaaS indispensable. (I'm building GoDavaii.)
True. Also it is still evolving field and people are trying many things to make it reliable