Product research
Bounded Workflows Beat Brute-Force Scale
Specialist agents do not need to win every benchmark. They need to win the workflow they were hired to complete.
General intelligence is not the unit of deployment
A business deploys a refund flow, a claims check, a product search, or a document review. Each has a limited tool surface, a known policy, and an outcome that can be scored. Treating every request as an open-world reasoning problem wastes money and makes failures harder to diagnose.
A bounded workflow lets the team define the environment before training: which tools exist, when they can be called, what requires approval, and what a successful completion looks like.
Specialization changes the economics
Once the workflow is measurable, a smaller model can be post-trained against real task rewards. The goal is not to claim that small models are universally better than frontier systems. The goal is to reach the required reliability with a model that can run on an edge device, in a customer VPC, or on a lower-cost hosted stack.
This shifts the conversation from tokens consumed to tasks completed. It also makes hybrid systems possible: frontier models can handle rare, ambiguous cases while specialist agents absorb the repeatable majority.
Verification is part of the product
Simulation and evaluation cannot be a launch-week checklist. They are the compiler tests for the workflow. Every policy change, tool update, or new failure example should be replayable against the agent before deployment.
That feedback loop is where operational knowledge becomes a durable model capability instead of another prompt buried in an application repository.