Topic
LLM agents, planning, and autonomy.
Across 155 small-model experiments centered on Qwen 3.5 4B, the same thing kept working: give the model something executable it can check against the evidence it has, and it punches far above its benchmark weight. Here is the field guide — the levers that worked, how I know they're real, and the frontier they opened up.
Swapping the frontier model rarely moves your agent's success rate as much as fixing retries and context management — and the one thing competitors can't clone is your evaluation environment. A thesis on why agent evals, not weights, are where reproducible capability accrues.
If your workload has heavy shared prefixes — system prompts, few-shot exemplars, agent scaffolds — automatic prefix caching is close to free latency. This is where serving for agents diverges from serving for chat.