Agents
Intelligence Layer
The layer between a company's data and its applications in which models, rules, and tools are put together: this is where it is decided which model gets which task, where the context comes from, and what is logged. Whoever owns this layer can switch providers like suppliers; whoever doesn't has many individual solutions.
An example
In most companies, AI today sits as an island next to the business systems: a chat window here, a text function there, each purchased separately and operated separately. An intelligence layer would be the opposite: a shared level between data and applications in which model access, permission checks, logging, and tool catalog are settled once and every business application draws on them.
A closer look
The layer sits between the individual capabilities and the application. It decides which building blocks a request needs, brings them together, and returns a result. The gain is that new use cases need no new integration.
Why it matters
The term comes from the vendor world and yet describes a real problem: when twelve departments solve the same thing twelve times (access, rights, logging, cost control), the result is twelve half-finished answers and no overview. The practical question is not whether to buy such a layer but which four or five things belong under central control and which stay in the business units. Access, permissions, logging, and cost tracking belong centrally. The use case belongs up front.
Commonly misunderstood
An intermediate layer pays off only beyond a certain number of building blocks and applications. Before that it is additional effort for a benefit that does not yet arise.