RetrySight Engineering teams commonly spend $200–600 per engineer per month on AI coding tools. Seat fees tell only part of the story. The rest hides in retry loops: test failures that trigger another agent turn, diffs that get rejected and rewritten, context compaction that re-reads thousands of tokens, and subagent spirals that never surface in a simple usage chart.
Vendor dashboards answer “how many tokens did we use?” They rarely answer “how many of those tokens were wasted on retries?”
What retry waste looks like
RetrySight classifies agent behavior that generic analytics miss:
- Edit loops — the agent revises the same file multiple times without forward progress
- Test failures — automated test runs that fail and trigger another attempt
- Diff rejected — proposed changes the developer or linter rejected
- Compaction events — context window resets that re-ingest prior conversation
- Subagent loops — delegated tasks that spin without completing
Each event carries token cost. Stack enough of them across a 100-person fleet and FinOps starts asking questions seat-based pricing cannot answer.
A framework for engineering leaders
- Measure retry rate, not just token volume — retry rate = retry events ÷ completed tasks. Track it by tool, team, and model.
- Compare across vendors — Cursor-only admin APIs cannot tell you what Claude Code is doing on the same project.
- Tie cost to sessions, not invoices — session timelines show where retries cluster, not just that spend went up.
RetrySight collects these signals from local IDE agent logs — no SDK in your application code — and rolls them up for fleet-wide FinOps and platform teams.
Next steps
- Download RetrySight Lite to see retry patterns on your own machine
- Self-host Enterprise Core for fleet visibility across Cursor, Claude Code, and Codex
- Start a Cloud trial if you want managed hosting