Neurometric previews Auto-SLM Creator for agents

This is less “bigger model, better agent” and more “right-size the model to the task.” If the approach works beyond the demo, the real breakthrough is a repeatable pattern for making SLMs useful in boring, high-volume workflows.

• –The split between fine-tuning for deterministic tasks and tool orchestration for multi-step jobs is the most practical part of the idea.
• –The progress-tracking flags are a strong signal that small models need explicit state, not just better prompts.
• –Local inference is the business upside: lower cost, lower latency, and cleaner data-sovereignty story for regulated teams.
• –The best-fit use cases are procedural workflows like audit sampling, compliance checks, validation, and report generation.
• –The big question is generalization; a compelling preview is not the same as a broadly reliable production system.