Debug KeyError on missing version in the model-run dictionary

Do not start with a speculative fix for the model-run dictionary. Force the failure on purpose, save the before output, then change one cause at a time. Retries are allowed only for transient conditions—not for bad input that will fail forever on python-dictionaries.

run={'name':'ready-v1','accuracy':0.91,'ready':True,'owner':'ml-lab'}
try:
    print(run['version'])
except KeyError:
    print('missing version ->', run.get('version','unversioned'))

Expected evidence: missing version -> unversioned. If you cannot reproduce on demand, you do not yet control the failure mode for python-dictionaries.

After repair, rerun the exact reproduction command. Keep the failing fixture as a regression seed for the observability page. For the model-run record dictionary, remember the claim you are restoring: required keys name/accuracy/ready/owner exist and optional version falls back safely.

Store a one-command reproduction for: KeyError on missing version, or silent overwrite of accuracy with a string. The command should use name='ready-v1', accuracy=0.91, ready=True, owner='ml-lab' or a minimal mutant of it. Paste the failing output into notes/failure-before.txt (or your shell scrollback as copied text). After the fix, paste notes/failure-after.txt and keep both.

Retries belong only on transient faults. If the failure is bad input, a bad allowlist, or a logic bug in the model-run dictionary, retrying will amplify cost without repairing trust around store one training run so later code can read named fields without positional guessing.

Classify the failure as prevent, detect, contain, or recover—using this lab’s language, not a generic poster. For python-dictionaries, the first fix should usually be detect+prevent at the boundary, because KeyError on missing version, or silent overwrite of accuracy with a string is cheaper to stop early than to explain in production prose.

At the debugging stage for python-dictionaries, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about name='ready-v1', accuracy=0.91, ready=True, owner='ml-lab' that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: a handwritten field list before coding the dict.

For this page specifically, success looks like before/after evidence for the characteristic failure while still centering the user decision to store one training run so later code can read named fields without positional guessing. If you cannot point to a file, command, or assertion that proves that for the model-run dictionary, stay on this page instead of advancing.

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