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Artwork for Agentic AI in DevOps
Agentic AI in DevOps · July 9

#14 — Loop Engineering in DevOps

Loop engineering solves the coding-agent babysitting problem by giving DevOps teams a way to run larger tasks with evidence, constraints, and a clear definition of done. Andrey Devyatkin, Vladimir Samoylov, and Fernando Gonçalves unpack outer loops around agents, context-window limits, unattended runs, greenfield versus brownfield work, alert batching with SNS and SQS, model mixing, token costs, and why bad code still gets worse when you automate it faster. Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai Episode page, show notes and links

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Loop engineering solves the coding-agent babysitting problem by giving DevOps teams a way to run larger tasks with evidence, constraints, and a clear definition of done. Andrey Devyatkin, Vladimir Samoylov, and Fernando Gonçalves unpack outer loops around agents, context-window limits, unattended runs, greenfield versus brownfield work, alert batching with SNS and SQS, model mixing, token costs, and why bad code still gets worse when you automate it faster.

Maybe try B.O.R.I.S, our context layer for AI agents: https://www.getboris.ai

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