AI Agent Latency
Benchmark
A transparent look at how we measure cold-start and warm-path latency for autonomous agent workflows.
Source: Computer Agents engineering benchmark post, published February 7, 2026.
Measurement principles
These are the core practices we use to make latency improvements reproducible, not anecdotal.
Trace every request end-to-end
Instrument timestamps from user action to first streamed token. Avoid aggregate-only metrics that hide bottlenecks in specific pipeline stages.
Separate cold and warm paths
Measure cold starts and warm starts independently. Most user experience quality is determined by warm path behavior in ongoing sessions.
Track backend overhead vs model generation time
Report platform overhead separately from model latency. This isolates what your infrastructure can improve from what the model vendor controls.
Eliminate duplicate execution and startup races
Deduplicate stream creation and prewarm calls at both frontend and backend levels to avoid accidental extra process creation.
Hide unavoidable latency behind user think-time
Use prewarm triggers when users open the app, select agents, or mount workflow surfaces so expensive setup work starts before message send.
Full engineering write-up with request trace and fixes
Read the full post for architecture details, bottleneck breakdown, and implementation notes behind the latency improvements.