How to design state for Jev
Structure Jev state as compact decision evidence: remove noise, preserve labels and units, and keep policy out of the model input.
1. Start from the decision boundary
Write the exact Choice, Score, or Noul question first. Then include only facts that could reasonably change that answer. More context is not automatically better context.
2. Prefer labeled records over prose dumps
Stable field names make the evidence easier to audit and reduce accidental ambiguity. Preserve identifiers, dates, units, and source labels instead of flattening everything into one paragraph.
3. Filter deterministically before Jev
Use ordinary code for permissions, exact arithmetic, allowlists, deduplication, and size limits. Jev should handle the semantic judgment that remains after deterministic checks.
4. Test state changes independently
Keep a labeled test set and vary one field at a time. Log the normalized state shape so regressions can be traced to input design rather than hidden prompt changes.
Work through a complete example and its failure paths
The complete file below has no placeholder functions. It prints a suggestion or review state; it does not update tickets, spend money, or publish content.
Verification: pinned-package type/syntax checks and mock transports, not live API performance or accuracy tests. Real SDK runs require your own server-side key and may incur charges. Calibration and input examples run offline.
A poor input serializes the ticket, secrets and all history. A better input allowlists message and failure_band. An allowlist does not redact secrets inside free text. Missing data requests context; oversized messages go to review rather than silently losing evidence.
state-design.py
"""Deterministic prefilter; no model call and no secrets in returned state."""
def build_state(ticket):
message = ticket.get('message')
if not isinstance(message, str) or not message.strip():
return {'action': 'request_context', 'reason': 'missing_message'}
if len(message) > 4000:
return {'action': 'review', 'reason': 'oversized_message'}
failures = ticket.get('failure_count')
if not isinstance(failures, int) or isinstance(failures, bool) or failures < 0:
return {'action': 'request_context', 'reason': 'invalid_failure_count'}
return {'action': 'evaluate', 'state': {
'message': message.strip(),
'failure_band': 'repeated' if failures >= 3 else 'isolated',
# Explicit allowlist: api_key, email and unrelated history are not copied.
}}
if __name__ == '__main__':
print(build_state({'message': 'Checkout fails.', 'failure_count': 4, 'api_key': 'do-not-send'}))
print(build_state({'failure_count': 4}))
# Free text can itself contain secrets. Review/redact it before provider upload.
Run
python state-design.pyHow to verify the run
API examples print structured fields such as action and team. Labels and scores may differ from mocks. Removing the key should produce review without exposing raw error text. The calibration script prints development and held-out metrics, not model-quality evidence.