Same topic, related formats. Practice these next.
Each agent turn re-prefills the whole transcript then waits on the tool, so latency compounds with turn count; parallel calls collapse the dominant tool-time axis.
Imagine asking a research assistant a question that needs three library books. A slow assistant fetches one book, rereads everything written so far, walks back, fetches the next, rereads everything again, and repeats. Most of the wasted time is the walking, not the reading. If the three books are unrelated, you could send the assistant to grab all three in one trip instead of three. That single change saves far more time than asking them to read a little faster. Each round trip also forces a reread of everything so far, so the more trips you make, the worse it gets. Fewer, parallel trips beat a faster reader almost every time.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.
Everything important, quickly.
3 min: agent loop anatomy + why latency compounds with turns + tool time dominates + parallel calls collapse the tool axis + prompt caching collapses the prefill axis + dependency caveat.
Real products, models, and research that use this idea.
What an interviewer would ask next. Try answering before peeking at the approach.
Red flags and common mistakes that signal junior thinking. Click to expand.
Optimizing per-token decode speed (FP8 cache, smaller model) while ignoring that tool execution and network round trips, multiplied by the number of turns, dominate the wall clock.
The night-before-the-interview bullets. Scan these on the way to the call.
Primary sources. Skim if you want the original framing.