What is Terminal-Bench?
See terminal-based agent testing.
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Learn what AI agent benchmarks measure and why agentic workflows can drive higher token usage, latency, retries, and AI compute demand.
An AI agent benchmark tests whether a system can plan, use tools, take multiple steps, and complete a task rather than return one response to one prompt.
Memory trick: Agents spend compute over steps, not just responses.
Agents are tightly connected to compute economics because one request may generate repeated model calls, large tool results, retries, verification rounds, and long runtime.
A coding agent can inspect files, plan a patch, invoke tools, run tests, revise work, and verify an outcome. That chain consumes more serving capacity than a single generated answer.
Example figures are illustrative calculations, not current quoted market prices.
Do not price agent workloads as if they were single-turn chat or treat a model-only score as an end-to-end agent cost.
Practical takeaway
Estimate model calls, tool rounds, input and output tokens, retries, elapsed time, and completion rate before budgeting an agent deployment.
Decision check: have you costed the full agent chain (calls, tools, retries, runtime) rather than pricing it as single-turn chat?
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Step 22 of 25: What is an agent benchmark