Observed arrival · 2026-10-01
Averth measures the costs hiding inside AI-agent runs
Averth analyzes batches of AI-agent runs to estimate their full cost per accepted outcome, including retries, external tools, and human review.
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- For
- Teams measuring the cost of AI-agent workloads
- Worth noticing
- In its 2,000-run test workload, the costliest 5% account for 61% of spend; the tail-shape graphic is labeled illustrative.
Field notes
The workflow accepts a sanitized batch of agent-run records and returns per-outcome cost, tail-spend share, and a five-layer breakdown. A separate offline replay compares a budget or routing policy with historical runs, including stopped runs, saved waste, and successful runs affected. The demonstration uses a loaded labor rate for human review and notes that each user's review cost will differ.
Observed signals
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●ProPolished or operationally mature
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