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SUMMARY:SIGIL4Py: Harmonic Integration for Large-Scale Python Systems via 
 Compositional Plural Programming - Jara Juana Bermejo Vega
DTSTART;TZID=Europe/Madrid:20261107T153500
DTEND;TZID=Europe/Madrid:20261107T161500
DTSTAMP:20260726T231805Z
UID:pretalx-pycones-2026-HBRANC@pretalx.com
DESCRIPTION:How can Python systems remain stable when many valid states co
 exist?\n\nPython succeeds because it allows many valid ways to build syste
 ms. It fails when those valid states cannot be coordinated\, validated\, o
 r made to converge.\n\nThis talk introduces SIGIL4Py\, a practical approac
 h to making Python systems more reliable by structuring how different part
 s interact. Rather than replacing existing tools\, SIGIL4Py provides a thi
 n execution layer that coordinates them\, ensuring that changes are tracea
 ble\, admissible\, and consistent across environments.\n\nWe frame this as
  compositional plural programming: many voices—libraries\, workflows\, d
 evelopers\, tools\, humans\, and agents—working together as one system w
 ithout forcing a single implementation path. The result is harmonic integr
 ation at scale: faster development\, fewer integration failures\, and more
  trustworthy execution.\n\nSIGIL4Py integrates across the Python stack by 
 structuring interaction rather than rewriting components. It enables large
 -scale coordination of concurrent systems through ordered transformations\
 , explicit validation\, and reproducible execution traces. A key contribut
 ion is the unified treatment of human and agent actions within the same in
 put/output workflows\, allowing systems to incorporate multiple sources of
  execution while maintaining consistency and control.\n\nAt the system lev
 el\, SIGIL4Py is realized through composable components: a minimal runtime
  layer structures execution flow\; a trace subsystem records state transit
 ions for replay and debugging\; validation gates enforce admissible transi
 tions\; typed schemas preserve data integrity\; and a composition layer en
 sures compatibility between independently developed components. These are 
 extended with modules for CI/CD reproducibility\, AI workflow stabilizatio
 n\, deterministic notebook execution\, and deployable kernel orchestration
 \, supported by KUIR and KUBE.\n\nThe goal is not to impose one correct wa
 y to build Python systems. The goal is to make plural systems runnable\, i
 nspectable\, reproducible\, and safe to evolve.
LOCATION:Track 03 - Manychat
URL:https://pretalx.com/pycones-2026/talk/HBRANC/
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