AI Operations Control Tower
A review-queue and escalation model connecting evidence states, priorities, owner risk, and operating decisions.
Synthetic workflow prototype
Task: Make missing intake evidence and human-review boundaries explicit before routing a request.
Contribution: Project direction by Kamal; AI-assisted implementation. The editable extension translates review questions into a transparent, bounded intake workflow.
Inputs → processing → output: Synthetic request + required-field policy → deterministic completeness and evidence checks → clarification items, field-state rows and human-review status.
Technology: React · Pure JavaScript evaluator · JSON schema + fixtures · In-memory state
What the reviewer can do: Edit the synthetic request, remove a required field, change required-field policy and inspect the resulting clarification state.
Limits: Author-configured policy, not a practitioner-validated top three. No deployed LLM, autonomous decision, backend ticketing service, client use or measured resolution-time gain.
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What changes when you change an input?
The intake is editable. Clearing a required field creates a clarification item and a needs-review result. Changing the required-field policy changes which missing fields count. Reset restores the exact synthetic fixture. The presence-only JSON export includes current policy and calculated review counts, never entered names, identifiers, scripts or error text.
Computed: Required/supplied counts, missing-field list, completeness state and clarification/review flags.
Published baseline: The original nine review queues, priority matrix and aggregate figures above remain prepublished evidence. This local intake computes a separate result and does not alter those figures.
Required fields are configurable author assumptions, not a practitioner-approved mandatory top three. Errors/scripts are inert text. No deployed LLM, backend, measured resolution improvement or automatic approval is claimed.
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