Ask an engineer to list their agent's failure modes and you'll hear about hallucinations, wrong tool calls, and bad JSON. Ask about time and you get a shrug. Yet the single most common thing a production agent does when it goes wrong is not fail loudly — it just takes too long. It loops. It retries a flaky tool. It waits on a model call that never streams a first token. And your eval suite, which runs after the fact on whatever output eventually showed up, grades it green.
This is the blind spot: we treat latency as an SRE dashboard concern, divorced from correctness. But for an agent, a deadline miss is a correctness failure. A summary that arrives 90 seconds late is often worse than no summary — the user already left, the downstream job already timed out, the retry already double-charged them. Time belongs in your evals, and it belongs at the very bottom of the stack.
Time is Tier 1 evidence
If you've followed the tier doctrine behind agent-eval, you know evidence ranks on an independence axis — from evidence the agent can't forge, to opinion it shares a substrate with — not a cost axis. Three tiers:
Tier 1 — externally observable proof the agent can't fake: valid JSON, the file exists, the code compiled, tests passed, it finished within the deadline, the output is non-empty.
