Tool-using language agents are flexible but non-deterministic: an orchestrator LLM re-plans on every request, adding latency, cost, and paths that are hard to audit. We demonstrate TraceForge, an on-premise system that observes how an agent actually solves a recurring task and, on operator request, hardens it into a deterministic, immutable workflow DAG. TraceForge attaches to the OpenTelemetry traces an agent already emits (zero code changes), reconstructs each run’s execution DAG, and runs a human-gated loop: a statistical aggregation gate (proposes a DAG only once enough runs agree), a Judge-LLM reflection step, and a human freeze gate that writes a versioned, immutable specification with lineage and rollback. The pipeline is topology-agnostic—applied to single-agent tool-callers and multi-agent orchestrators alike—and all components run on-premise with no external dependencies. On held-out requests, frozen workflows reproduce the dynamic agent’s outputs exactly while removing every planner-LLM call and cutting latency 1.39x. The entire loop is driven from a browser console with no coding, and is shown end-to-end in the accompanying screencast.