Ever since AI began entering public view, one of the most common things people said was, "frontend is dead," "translation is dead." And yet from 2024 to 2026, we saw the opposite: there were more roles, not fewer. That is not because AI failed to replace execution. It is because new execution demand emerged even faster. The thing being repriced was something else.
Once Agents take over the execution layer, the real differentiator becomes judgment: knowing what to trigger, and why to trigger it that way. More specifically: judgment is the ability to orchestrate Agentic Primitives.
The Nature of SaaS, and What It Is Losing
In early 2026, Anthropic released Cowork. The stock reaction that followed split companies into two categories very clearly: ServiceNow fell more than 23%, Salesforce 22%, Snowflake 20%, and Intuit 33%. At the same time, after Anthropic's enterprise briefing on February 24, the companies named as partners rebounded sharply. Thomson Reuters was up more than 11% in a single day, while Salesforce, DocuSign, LegalZoom, and FactSet all moved higher as well.
This does not mean SaaS is dead. It means the ecosystem is bifurcating.
To understand that fault line, you first need to ask what traditional SaaS really delivers. It does not deliver business capability itself. It delivers a UI interaction system. To complete a business objective, you first have to learn how to operate that system. At its core, that means humans adapt to software.
Once an Agent can understand intent directly and execute against it, that middle UI layer stops being "the product" and starts becoming friction. The moat SaaS built through operational complexity and feature accumulation begins to fail in front of Agents. Companies whose entry points are bypassed by Agents are gradually losing contact with users, often without users even noticing.
The logic behind the stock rebound of companies admitted into Anthropic's ecosystem is straightforward: their business capabilities are being called directly by Agents rather than bypassed. To be inside the ecosystem means your capability becomes part of the Primitives layer. To be excluded means your UI entry point is being replaced.
Why Anthropic Stepped Back
What is worth noting is that Anthropic itself made its positioning clear when it introduced Cowork: "a platform, not a product, trying to own every workflow."
Even one of the companies most capable of shipping full Agents chose to step back. That is the part worth examining.
What Anthropic is doing is not writing every Agent for everyone else. It is standardizing the smallest capability units that make up an Agent system: Memory, Tool Use, Planning, Execution. These are not product features. They are infrastructure interfaces. Standardizing Primitives creates a deeper moat than any vertical application, because every vertical application ultimately builds on top of this layer.
I felt this very directly while building z0's @z0/memory module.
I spent a large amount of time on a question I should not have had to decide myself: how many layers of memory should an Agent have? What should be persisted? What should be discarded after a session ends? When should retrieval be active rather than passively injected? There is no standard answer to any of this, so I designed my own three-layer architecture and introduced adversarial retrieval to improve recall quality.
It worked well, but that fact itself reveals the problem. An Agent engineer should not have to spend this much energy designing the infrastructure of Memory. Every team building Agent systems is forced to rediscover the same pitfalls because there is no standard telling them how to do it.
The contrast becomes obvious when I compare that experience with building @z0/connector. MCP is currently the only protocol in the Tool Use layer that is close to a standard. @z0/connector can connect directly to any MCP server with very low implementation cost because that layer already has standardization. Memory, Execution, and plugin orchestration still do not.
What Anthropic is doing is turning the foundational questions that should not have to be re-decided by everyone into reusable interfaces. MCP has already demonstrated the value of this. They are trying to extend that logic to all Primitives.
Why AaaS Is Still Not Trustworthy
As AI adoption accelerates, AaaS, Agent as a Service, is entering mainstream attention. But its actual success rate is still far from sufficient.
The core problem is not that models are not capable enough. It is that errors compound across an Agent chain. A human employee who makes a mistake at step three may notice and correct it. An Agent that goes wrong at step three often amplifies that mistake again at step seven and step ten, eventually delivering something that looks complete but has already drifted away from the original goal. By the time the user notices, they often no longer know where the failure began. This silent propagation of error is one of the main reasons AaaS remains difficult to trust.
The deeper issue is the absence of a trust mechanism. The product shape of AaaS assumes users are willing to hand over full task control to an Agent. But trust takes time to build, and trust requires explainability, auditability, and predictable failure boundaries. Most AaaS products today provide none of the three.
This is another reason why standardizing Agentic Primitives matters. Once Memory, Execution, and Tool Use all have standard interfaces, errors gain traceable boundaries, audits gain clear insertion points, and trust gains a foundation on which it can actually be built.
What Remains for Humans
After "Vibe Coding" became popular, people started realizing they could build products without understanding code. But the people who ultimately proved valuable were not the ones who used AI to write the most code. They were the ones who knew when not to use AI.
In the past, if a product manager wanted to do data analysis independently, they needed at least basic SQL, an understanding of database structure, and familiarity with BI tools. Now all they need to know is that "data analysis" exists and at which decision point it should be triggered. The details of execution have been absorbed by AI.
But the act of triggering judgment has not been absorbed. You still need to know when an Agent should use long-term memory, when it should retrieve in real time, and when it should stop and hand control back to a human. That kind of judgment cannot be replaced by AI, because it depends on your understanding of business goals, your sensitivity to risk boundaries, and your feel for the difference between "sufficient" and "excessive."
Agents have taken over the execution layer. What remains for humans is the starting point of intent, and the boundary of judgment.