Max Headroom 
With the rise of agents, many people have been proclaiming that the age of software as a service (SaaS) is over. Who needs to subscribe to a service when you can create your own software with a few English-language prompts and a few dollars spent on tokens? Your own software, most likely a skill that runs in an agent, will have exactly the features you want: no more, no less.
But whenever someone talks about the death of SaaS, there’s something wrong with the picture. It’s simply that work is about groups and teams, and so far, programming with agents is about individuals. A related challenge is that SaaS companies are good at building dashboards and generating reports for humans, but agents need the raw data, not a representation of the data.
Think about the teamwork required for a good sales team. Someone needs a database to keep track of their customer info. It’s easy to get Claude, Gemini, or GPT to build that, using SQLite for a backend and putting a reasonable web frontend on it. You could also do that fairly quickly with Ruby on Rails, but AI makes it even easier. But what about the salesperson at the next desk? She needs similar CRM software, and she can create it with Claude, Gemini, or GPT. No problem. But it won’t be exactly the same; it will reflect her needs and preferences. Soon you have a team of salespeople in which everyone has their own personal CRM. They’re all similar, but slightly different. They may use different backends (Filemaker, SQLite, MySQL, or maybe a corporate Oracle instance); they have similar-but-slightly-different schemas (one has a single field for customer address, another has separate street, city, state, and country fields); and they don’t interoperate.
That’s the simplest possible case. How do you generate company-wide reports if everyone has their own version of the data? How do you know if you’re succeeding or failing if everyone on the team has their own version of the metrics? Everyone has become their own silo.
The company is not paying subscription fees to a vendor like Salesforce, but is this really progress? If anything, we need to make sharing data and metrics easier, not more difficult. On top of that, a product like Salesforce has hundreds of features. Most people don’t need most of them, but there’s a good chance that almost everyone needs one feature that nobody else needs. And there’s always the features you don’t know you need, ways to get value from data that you haven’t thought of. There’s value in buying a bundle that goes beyond your immediate requirements.
There’s certainly a lot good about enabling people to develop their own tools. I guarantee that if we had Claude Code 30 years ago, I would have vibe-coded my own skills for managing the authors I was working with. I would have vibe-coded some of the crazy tools I wrote to translate from one document format to another. (WordPerfect to troff? Why?) Now that we have agentic programming, I may never write my own tools again. But the SaaS scenario highlights something missing from the agentic picture. We don’t have tools for sharing or collaboration. Nobody buys a Salesforce subscription for themselves. It’s a departmental or corporate resource, shared between many people. And the ability to share easily is precisely what agentic programming lacks. I’ve built some of my own Claude tools and skills, but it’s very difficult to share them with other people at O’Reilly. ChatGPT Skills for Business and Enterprise hints at the ability to share skills among team members and some ability to generate them collaboratively, though it’s hard to find evidence that it delivers. I think we’re seeing a symptom of technological overreach. It’s easy to assume something is “easy” when it isn’t: “You just generate a .md file and put it in the corporate GitHub.” That process has a lot of friction, particularly for users who aren’t technical.
To make skills really useful across a company, we need:
- Sharing. This can be a Git server that’s registered as a private marketplace and then configured via a corporate administrative dashboard. Publishing skills to the marketplace would remain the province of Git-aware users, and that’s a problem.
- Requirements. We don’t want everyone to build a personal toolset; that’s the problem we’re trying to solve. How do you resolve differences between users who want slightly different things? What does the PRD for a skill look like?
- Collaboration. Aside from Google Docs, the current state of widely used collaboration tools is poor. Suffice it to say that working on different branches of a Git repo and merging changes may work for professional programmers, but not for anyone else.
- Testing. Tests and evals for agents (related, but not the same) are topics that we don’t yet understand well. But if you’re going to empower users to use and create agentic tools for creating projections and writing reports, you need to know they won’t backfire. Skills also behave like any other AI application: They drift over time. Even after they’re published, they need to be evaluated regularly to see if they still perform correctly.
- Versioning. Like any software—and we need to recognize that agentic tools and skills are software, even if they’re written in English—it will be important to update them as requirements change and as LLM behavior drifts. It’s important to keep track of versions and for users to update their skills to the latest version easily. Again, this is a matter of wrapping Git appropriately for nontechnical users.
- Security. Security for intelligent agents is still poorly understood. We know about prompt injection, but we also know that it’s a problem that can’t be solved yet. And attackers are still finding novel ways to inject malicious prompts. What vulnerabilities might agentic skills and tools have if they can access corporate data?
While the democratization of programming doesn’t threaten SaaS companies, intelligent agents pose a deeper challenge. In “The Salesforce of Agents Won’t Be Salesforce, the Google of Agents Won’t Be Google,” Jesus Rodriguez points out that the future for services like Salesforce and Google isn’t web UIs and dashboards; it’s APIs that are designed for agents. These APIs require a different kind of data: not something that a human can glance at to get a quick feel for what’s happening, but “structured state, task objectives, relationship graphs, permissioned memory, machine-readable sales playbooks, and reliable APIs for updating intent.” Humans need the data compression that you get from a dashboard. Agents want the data itself, and they’ll take care of the compression. SaaS companies can become the system of record that is responsible for delivering accurate data. What they need to recognize is that their real customer may not be a human user; the customer will be an agent, and that will affect everything from marketing strategy and product design to pricing.
I wouldn’t claim that Salesforce or Google can’t or won’t build APIs to help companies access their own data. SaaS remains relevant, but it’s a different kind of SaaS than we have now. Companies like Salesforce know what data is available and how to work with it. Designing and building the data infrastructure that’s needed to provide next-generation SaaS isn’t trivial, and doing the programming in English rather than C++ doesn’t make it easier. Companies like Salesforce and Google know what needs to be built. They’re likely to offer their own collections of agentic skills as a starting point, alongside APIs. But large, established companies are ripe to be blindsided if they move slowly—and it’s difficult for large institutions to move quickly.
SaaS companies have momentum—or inertia, which to a physicist is the same thing. They have to change, but they aren’t threatened by AI, agents, and user-defined skills. Providing APIs that have been designed to provide data in formats that machines can use should be an obvious next step. If they die, it will be because they don’t adapt. But there’s nothing new about that.
AI & ML, Commentary
Radar
