AI Agent Token Burn Monitor for Production Engineering Teams

Best-fit situations

• A coding agent repeats search, install, or test commands without producing a useful diff.
• A consulting team needs to show a client why a task cost more than expected.
• A platform team wants daily and monthly token ceilings by project, model, and user.
• A lead engineer needs an early warning before a long-running agent burns through the sprint budget.

Operating steps

1. Sync agent logs from Claude Code, Codex, CI jobs, terminal wrappers, or internal runners.
2. Normalize token usage, model names, tool calls, timestamps, task IDs, PRs, issues, and client tags.
3. Set daily, monthly, and single-task limits by project, model, user, or customer.
4. Flag repeated tool calls, context bloat, no-test output, failed-test loops, and budget spikes.
5. Send alerts and produce a cost report that maps usage to business work instead of raw transcripts.

Common risks

• Raw provider dashboards show spend but not the task, repo, PR, client, or reason behind it.
• A runaway agent can look active while producing no tests, no diff, and no customer value.
• Context windows grow slowly until every retry becomes expensive.
• A team notices the cost issue only after the invoice arrives.