Analytics

The Analytics section aggregates what all your automations are doing: how much they run, how often they succeed, and what they cost. It's the place to look before your users tell you something broke.

What you'll find

• Executions — run volume over time, per flow, across every channel (editor, chat, WhatsApp, API, apps).
• Success rate — completed vs failed executions; a dropping rate is your earliest incident signal.
• Credit consumption — where the money goes: which flows and agents burn the most credits, and the trend.
• Per-flow breakdowns — sort by cost or failures to find the outliers.

Reading it like an operator

1. Watch success rate first. Volume changes are business; failure changes are incidents.
2. Chase the expensive flows. A prompt tweak or a smaller model on one hot flow often cuts spend more than anything else. The LLM node lets you swap models per node.
3. Correlate with changes. Cost or failure spikes usually date to a flow edit — the editor's Versions history tells you what changed and lets you roll back.

From metric to root cause

Analytics tells you which flow misbehaves; the canvas tells you why:

• Each node shows Runs / Success Rate counters on its card.
• The node Console has the exact inputs, outputs and errors of recent executions — including the Function Calling Process for agent tool calls.
• The editor's process history (clock icon) lists past whole-flow executions.

Who can see it

In organizations, Analytics requires the view_analytics permission — so you can give finance and ops visibility without edit rights.