Liguori defines frontier developers as engineers who write little code manually, interact with agents infrequently and keep several agents working in parallel. Amazon experiments ranged from a highly specialized Bedrock team to a focused Prime Video sprint and a broader 50-team retail pilot, where the strongest teams improved production deployment velocity by a median of 4.5 times.
The dividing line was not access to a particular tool. High-performing teams changed their daily habits: they captured institutional context in steering files, removed stale instructions as models improved, upgraded codebases and tools for clearer feedback, and accepted an initial productivity dip while building the environment agents needed.
Teams also learned to feed agents complete tasks rather than babysit conversational loops. Explicit specifications, small scoped work, local deterministic services, linters and comprehensive tests let agents self-correct for longer periods and return only after reaching a defined quality bar. That makes parallel work practical and moves humans out of the implementation loop.
The model creates new costs as well. Running multiple agents increases cognitive load, reviewing generated code can be harder than writing it, and organizations must allow time for new habits to form. Once code production accelerates, decision-making, launch review and other organizational processes become the new bottlenecks, so reversible decisions and faster governance matter more.
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