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SUMMARY:Building a software factory with Python and AI agents - Ravi Singh
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UID:pretalx-pycon-sweden-2026-UL7MQL@pretalx.com
DESCRIPTION:Most coding-agent demos end when the model produces a patch. I
 n production\, that is where the work starts.\n\nA coding agent on a lapto
 p is a good tool and a poor system: it runs as you\, on your machine\, die
 s when the lid closes\, leaves no record\, and its only gate is you pressi
 ng merge. We built a software factory in Python that gives a team what the
  laptop does not. A structured Jira ticket goes through specification\, re
 search\, estimation\, coding in an ephemeral E2B sandbox\, review\, a dev 
 deploy with a live check\, and a merge request that a person still has to 
 merge.\n\nThis talk is about why that architecture works: a durable Tempor
 al workflow that retries one stage and survives a worker restart\; typed P
 ydantic handoffs the workflow branches on\; one model gateway with a scope
 d key per agent\; prompts\, rubrics and context in a reviewed registry\; o
 ne run id across traces\, transcript\, branch and ticket\; and gates where
  a person acts with a scored spec and a verified deploy in front of them.
 \n\nThe evidence is one week in July: the same change run in three reposit
 ories. The first run failed and the records said why. We changed the input
 s\, not the model\, and the next two ran in 1 h 45 min and 28 min.\n\nYou'
 ll leave with an architecture you can adapt and a rule for deciding what t
 o automate\, retry\, measure\, escalate\, or hand to a person. Intermediat
 e Python\; no Temporal experience needed.
LOCATION:Auditorium
URL:https://pretalx.com/pycon-sweden-2026/talk/UL7MQL/
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