GlassFlow is built for the workflows chat tools weren’t: agents that run for hours and chain hundreds of steps. Crash at step 190? You’ll know exactly where and why.
The waterfall view every tool uses breaks down around 100 events. A 20-minute run with 500 tool calls produces an unreadable waterfall. GlassFlow uses a timeline model with anomaly detection, so you can scan hundreds of steps at a glance and jump straight to the one that matters.
GlassFlow expects to hear from running agents on a regular interval. A frozen agent — one that stopped making progress without crashing — is flagged immediately. In every other tool it looks identical to a slow healthy one.
Cost, latency, and token consumption update continuously as the agent runs. In existing tools these are only calculated on closed traces — if 90% of your agents are running, your dashboards reflect the minority that finished.
For always-on agents there is no trace end. GlassFlow’s session model handles open-ended runs, updating metrics continuously and keeping runs queryable at any point — not just after they finish.