Evidence-Graded Automation QA for AI Agencies

A monthly reliability service for AI-native agencies, but only for client automations that already emit auditable ground truth without custom integrations: document extraction against source records, classification/routing against downstream disposition codes, and acceptance/rework workflows with explicit correction logs. AE converts the agency's highest-risk prompt chains into behavioral contracts with constraints, expected ranges, and promotion/demotion/kill rules, then runs adversarial multi-model tests and grades performance against exported evidence the agency already has in CSVs, review queues, or standard tool exports. The output is not generic prompt advice; it is a weekly evidence-backed reliability report showing which changes improved measured correctness, where failures map to the six-pattern taxonomy, and which workflows should be promoted, rolled back, or constrained further. This deliberately excludes subjective outcome domains and deep client-specific CRM plumbing. The wedge is narrower but much more defensible: agencies can use it on the subset of live automations where embarrassment, rework, and renewal risk are highest and where truth is already observable.

AE has a credible wedge as a recurring QA layer for AI agencies because it focuses on measurable, high-risk automations where agencies already have ground truth, already earn meaningful revenue, and urgently need evidence-backed reliability control.

Engine verdict: GATHER_MORE_SIGNAL (WORTH_SKIMMING). ⚠ 3 load-bearing contradiction(s) found. Credible pain and a real wedge, but no proof agencies will buy recurring external QA rather than ad-hoc audits or internal tooling.