If you take one thing from this page: this model does not do customer service. It watches something else do customer service and vetoes bad steps. Everything below walks you through that idea, then the evidence, then the exact commands. Imagine a support agent (human or AI) working a ticket. The work is a loop: look up the caller, check their history, open a case, log the call. The dangerous mistake is doing steps out of order — logging a call when no case exists means the record vanishes. This model sits beside that loop and answers four questions about the current situation: 1. nexttool — "which step comes next?" (look up / check history / open case / log call) 2. safetolog — "does a case…
Independent publisher
Chad Hendren
chendren
Models in Library2
Datasets in Library0
Models on Hugging Face14
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Published tasks: Text classification.
Reported licenses: apache-2.0, mit. Open a record to review its specific license and conditions.
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Models
2,400 LLM-inferred (state, question, answer) rows in strands-decider Example format (kind/state/instructions/options/label/task/weight/instructionvariants), split evenly: 120 rows × 20 decision types, all states unique, all labels in range, all rows schema-validated. Generation: four parallel subagents, each inventing realistic 2026 CX states (subscriptions, SSO, renewals, invoices, chat, fraud, SLA, PII) and judging the correct label by model inference. No scripts, no templates, no rule-based synthesis. Score labels spread evenly across levels (unlike v1's urgency rows, which were 99% one class). Continual-train checkpoint for a strands-decider model (append to trainfiles; the 20 task…