What it looks like
It started with one orchestration framework, adopted because the demo was slick and everyone else seemed to be using it. Then came the vector database, the agent layer, the observability platform for the agent layer, and a monthly bill that grows faster than usage. When someone finally asks what all of it contributes, the uncomfortable answer is: indirection. The model does the work. The stack mostly moves the work around.
I know this pattern from the inside: my own agent setup once cost $250 a week, and the output was still poor. The fix was not a better framework. It was removing the framework.
What is actually happening
The AI tooling market is young, loud, and incentivised to convince you that calling a model is too hard to do directly. In reality the model APIs are among the simplest interfaces in modern software, and each layer placed between you and them adds cost, latency, failure modes, and a vendor’s roadmap to your critical path.
Teams adopt middleware to avoid engineering decisions, then discover they have outsourced exactly the decisions that determine quality: what context the model sees, how outputs are verified, when to escalate to a human. Those decisions come back anyway, now expressed through a framework’s configuration language instead of your own code.
The intervention shape
Audit the stack by contribution: for each layer, what would break if it were removed? Strip anything whose honest answer is “the diagram would look less impressive”. Most production AI systems need a model API, a place to keep state, and disciplined prompts and verification. The money saved is real and immediate; the quality gain from owning your own context and verification logic is usually larger.