Worked Intelligence Selection

Worked Intelligence Selection

Read Worked Intelligence Selection: invoice practice guidance with evidence limits, practical checks and editable source material.

The engagement does not select “an agent” for the workflow. It assigns the smallest sufficient mechanism to each consequential step.

Decision step Selected mechanism Why Fallback and verifier
Check tenant, identity, revision, and required fields Deterministic software Closed invariants with exact failure states Deny and return to the existing queue; contract tests verify behavior
Retrieve the active policy and cited invoice evidence Governed retrieval The reviewer needs current, attributable sources Mark evidence insufficient; source revisions and permissions are verified
Suggest a resolution and assemble an evidence packet One typed model-assisted proposal, only if target evaluation justifies it Language interpretation may help on variable exception evidence No proposal or manual review; reviewer checks cited evidence
Validate proposal shape and policy constraints Deterministic software A model must not define its own allowed action Reject invalid proposal; schema and invariant tests verify behavior
Approve the correction Human review Policy assigns accountability to a designated reviewer Existing manual queue; approval is bound to identity, policy, expiry, and proposal digest
Commit and deduplicate the approved correction Deterministic transaction service Exact authority, idempotency, and readback are software responsibilities Stop, reconcile, or compensate; ledger readback verifies completion
Decide whether the service continues or expands Human service review over measured evidence Value, adoption, risk, and ownership are organizational decisions Continue shadow, constrain, pause, or stop

Why not a multi-agent system

The workflow has no demonstrated permission, context, ownership, specialization, or latency boundary that requires multiple agents. A coded workflow is easier to inspect and recover. The current teaching runtime therefore uses a deterministic proposal fixture so the action boundary can be tested without presenting model capability as proven.

Before admitting a model-assisted proposal, a target team must evaluate representative normal, exception, missing-evidence, conflicting-evidence, adversarial, and recovery cases. The model route remains replaceable; approval, authorization, commit, and readback remain below it.