Runtime Governance Evidence Anchors for AI Agents: One Explicit Correction Request

TLDR

I am testing a run-level diagnostic for separating model-thought failures from runtime-governance failures.
The current v1 packet uses eight required fields and four pass/fail dimensions.
We have one named correction signal and need a second independent correction to validate or falsify the schema.
This post asks for one concrete correction: a missing field, a wrong label rule, or a better minimum threshold.

Why publish this as a correction request

Many incident reviews jump from visible failure to model blame. In practice, runtime-boundary failures often produce the same symptom pattern as reasoning failures. If a tool call is denied, stale context is injected, or writeback contaminates later runs, the transcript can look irrational even when the model step was plausible.

The operational goal is to constrain causal language to evidence quality.

Public diagnostic v1:
https://telegra.ph/Runtime-Governance-Evidence-Anchor-Diagnostic-v1-05-20

Current minimum packet schema (v1)

A packet is triage-eligible only if all fields exist or are explicitly marked missing.

Field	Required	Why it exists	Typical failure when absent
run_id	Yes	Binds events to one execution	Mixed events create false narratives
step_timestamps	Yes	Preserves order	Causality collapses into speculation
retrieved_context	Yes	Reconstructs what the model saw	Stale-context failures become model-blame
skill_version	Yes	Pins procedure revision	Unversioned logic breaks reproducibility
tool_calls	Yes	Captures requested actions	Requested vs executed cannot be compared
permission_outcomes	Yes	Captures allow or deny decisions	Boundary denials look like model disobedience
runtime_outcome	Yes	Captures machine-readable terminal state	Final state becomes narrative-only
state_writeback	Yes	Captures mutation payload and destination	Contamination risk stays hidden

Current label rules

Four dimensions:

Timeline Integrity
Context Provenance
Boundary Evidence
Mutation Audit

Decision labels:

decision-grade: all four pass
provisional: Timeline + Context + Boundary pass, Mutation fails
unknown: Boundary fails
insufficient: Timeline or Context fails

Existing correction evidence

One named practitioner correction already shifted my confidence toward explicit runtime evidence anchors and away from model-language shortcuts.

I now need a second independent correction from a different practitioner. Independent means one of:

a missing mandatory field that changes label outcomes,
a label rule that causes repeatable false positives or false negatives,
a stricter minimum that improves reviewer agreement.

One explicit practitioner question

If you had to remove one field from the current v1 packet without degrading incident attribution quality, which field would you remove first, and what concrete replacement evidence would you require to preserve decision quality?

Please answer with one concrete tradeoff, not a general principle.