For a long time, the first question when evaluating a new AI tool was about the ceiling: what’s the most impressive thing it can do? The benchmark, the demo, the showcase task. A lot of us started there. After enough exposure to teams who had adopted impressive tools they couldn’t actually use, the question changed.
The insight that cuts through the noise
The AI agent landscape has become genuinely complex. There are agents that run locally on your own machine with your own API keys. Cloud services that accept a description of an outcome and return a finished deliverable. Consumer-grade tools packaged for people who would rather not think about infrastructure. Safety-hardened versions of all of the above. Every one of them has a compelling demo. They don’t all fit the same person.
The reframing that worked for me was simple: the floor matters more than the ceiling.
The floor is what the product assumes about you — your technical background, your data situation, your tolerance for setup and troubleshooting. A tool that can do extraordinary things but requires you to configure API keys, manage network isolation, and vet untrusted extensions is not the right tool for a team without those skills. It is the right tool for a different team. The demo that got you excited was probably built by people from that other team.
Three questions, in this order
Before evaluating any demo or launch announcement, run through three questions:
Where does it run? Local execution keeps your data on your machine and gives you control over security — but it also means you own the maintenance and the risk when something goes wrong. Cloud execution means someone else handles the infrastructure — and means your data travels to their systems. Hybrid configurations (a mix of local and cloud) add convenience at the cost of complexity. There is no option that gives you everything without tradeoffs.
Who picks the model? A single-model product gives you consistency and simplicity. Multi-model routing (where the system automatically chooses among different AI models depending on the task) optimizes for performance — useful if you want the best output for each job, disorienting if you want predictability. Model-agnostic tools let you bring your own API keys and make your own choices — that is power if you know how to use it, and something that can go wrong fast if you do not.
What does this assume about me? This is the floor question, and the most important one. Not “what can our most technical person do with this?” but “what does the least technical person on this team need to have in place before this is even viable?” That question tells you more about day-to-day fit than any benchmark.
The compression problem
There is a second dynamic worth taking seriously: general-purpose agents have gotten capable enough that a lot of specialized tools are under pressure. Not always because agents do the job better — but because “good enough with no configuration” beats “best-in-class with setup overhead” more often than we expect.
People stick with slower, less capable tools precisely because those tools do not require them to install or configure anything. That is the floor working in reverse. And it is worth thinking about when you are evaluating whether to build or adopt a specialized solution: if a general-purpose agent can do your core task at seventy percent quality with zero switching cost, you need a clear answer to why your tool is worth the friction.
The products that have expanded their audiences fastest lately are the ones that assume the least about who is on the other end. That is not a coincidence.
What actually works now
Before any demo, the useful discipline is writing down honest answers to those three questions — not optimistic answers, realistic ones. Who is the least capable person who needs to use this? What does their actual data situation look like? What happens when something breaks and there is nobody technical to call?
Those answers narrow the field immediately. They also protect you from getting excited about something that is genuinely impressive for somebody else’s situation.
Going forward, the trend worth watching is where design energy is going in this space — and increasingly it is going toward lowering the floor, not raising the ceiling. The interface question (what the tool assumes the user can already do) is where the most consequential product decisions are being made right now. That is worth watching, whether you are adopting these tools or building them.
Text summarized and optimized using Anthropic’s models and reviewed by a human.