Private eval review
Monthly review of acceptance, override, false recommendation, exception, traceability, learning quality, and mission-impact or operating-impact metrics.
Feedback Loop Operations
AI workflows need a learning cadence, not just launch day.
Once an AI workflow is live, the work shifts from building to compounding: monitoring, evaluating, tuning, capturing feedback, reviewing exceptions, and communicating results.
Discuss your workflowEvidence from the lab
After launch, the work becomes operating the loop.
Verdify Lab is not a one-time demo. It publishes plans, deviations, known limits, lessons, and scorecards because live AI systems need review, tuning, and incident learning. Feedback Loop Operations applies that cadence to client workflows.
Explore Workflow ExamplesWhat transfers to Feedback Loop Operations
Review private evals, exceptions, overrides, incidents, feedback, outcomes, and known limits on a recurring cadence.
Change prompts, retrieval, routing, approvals, or action limits only with evidence and ownership.
Expand authority only when the learning record supports it; otherwise tune, hold, or stop.
Operating cadence
Feedback loops keep the workflow improving after launch.
The retainer turns a live AI workflow into an operated learning system with review, tuning, incident handling, feedback capture, and evidence-backed expansion.
AI prepares. Humans judge. Outcomes teach the system.
Feedback Loop Operations is the cadence around that principle: private evals, exceptions, approvals, incidents, known limits, and control changes are reviewed over time.
Exception analysis
Triage recurring failures, missing evidence, edge cases, risky recommendations, and reviewer objections.
Eval refresh
Update test cases, rubrics, baselines, known limits, and thresholds as workflow conditions change.
Workflow tuning
Improve prompts, retrieval, routing, approval steps, logging, and handoff logic without expanding authority prematurely.
Stakeholder reporting
Translate operational metrics into an executive narrative: what improved, what failed, what is blocked, and what changes next.
Small expansions
Add narrow workflow steps, sources, users, or approval paths only when the private eval record supports expansion.
Incident review
Document bad recommendations, approval failures, system defects, and corrective actions with clear ownership.
Documentation updates
Keep the Control Matrix, risk register, runbook, private eval record, and known-limits backlog current.
Retainer rhythm
A live workflow needs a learning loop.
The exact cadence depends on risk and volume, but the operating model is intentionally concrete.
Weekly or biweekly
Exception review, urgent defects, prompt or routing changes, and owner decisions for blocked cases.
Monthly
Private eval review, trend analysis, known-limits update, stakeholder report, and expansion recommendation.
Quarterly
Control review, roadmap refresh, procurement or provider changes, and decision on whether to add workflows.
Telemetry baseline
What the learning loop needs to review over time.
A live workflow needs enough event detail to explain what happened, who authorized it, what outcome followed, and what should improve next.
workflow/request ID
versioned workflow configuration
retrieved sources and revisions
current control rules and approval requirements
confidence or exception score
human reviewer
final approver
override events
timestamps
traceable final output and approval record
Monthly output
What the team receives.
Feedback Loop Operations should leave an evidence trail that is useful to operators, executives, and reviewers.
Eval memo
Plain-English summary of metrics, trend changes, caveats, and recommended action.
Exception register
Current defects, edge cases, incidents, owners, severity, mitigation, and status.
Control change log
Any approved change to what AI may read, draft, recommend, execute, or never touch.
Test-case update
New and retired evaluation cases based on live workflow behavior.
Feedback summary
Corrections, review decisions, outcome labels, and traces that should improve future performance.
Known-limits backlog
Unresolved limits that block authority expansion or confidence claims.
Next-step decision
Expand, tune, hold, or stop, with evidence and owner responsibilities.
Feedback Loop Operations fits when the workflow is live enough to learn.
Good fit when
A controlled workflow is live or about to launch.
There are recurring exceptions, overrides, or stakeholder questions.
The team needs a monthly private eval and operating cadence.
Expansion decisions should be evidence-backed.
Not a fit when
There is no live or near-live AI workflow.
No one owns incident review or exception triage.
The team wants set-and-forget AI.
Logs, review decisions, and workflow data are unavailable.
FAQ
Common buyer questions.
What happens after an AI workflow goes live?
The work shifts to operating the learning loop: private eval review, exception analysis, feedback capture, workflow tuning, incident review, documentation updates, and stakeholder reporting.
Is this only for systems Verdify built?
No. Verdify can help operate or improve existing AI workflows if the team can provide enough access to logs, source systems, workflow owners, review data, and outcomes.
When should a workflow expand?
Expansion should happen only when the private evals, exception review, feedback record, and known-limits backlog support larger approved action.