Scorecard review
Monthly review of acceptance, override, false recommendation, exception, traceability, and business-impact metrics.
AgentOps Retainer
AI workflows need operations, not just launch day.
Once an AI workflow is live, the work shifts from building to operating: monitoring, evaluating, tuning, expanding, reviewing exceptions, and communicating results.
Book a Fit CallEvidence 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. AgentOps applies that cadence to client workflows.
Explore Workflow ExamplesWhat transfers to AgentOps
Review scorecards, exceptions, overrides, incidents, 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 operating record supports it; otherwise tune, hold, or stop.
Operating cadence
AgentOps keeps the workflow accountable after launch.
The retainer turns a live AI workflow into an operated system with review, tuning, incident handling, and evidence-backed expansion.
AI prepares. Humans authorize. Systems of record remain authoritative.
AgentOps is the operating layer around that principle: scorecards, 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.
Evaluation 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 scorecard 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, scorecard, and known-limits backlog current.
Retainer rhythm
A live workflow needs a review 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
Scorecard 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 AgentOps needs to review over time.
A live workflow needs enough event detail to explain what happened, who authorized it, and which artifact left the system.
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.
AgentOps should leave an evidence trail that is useful to operators, executives, and reviewers.
Scorecard 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.
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.
AgentOps fits when the workflow is live enough to operate.
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 scorecard 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 automation.
Logs, review decisions, and workflow data are unavailable.
FAQ
Common buyer questions.
What happens after an AI workflow goes live?
The work shifts to operations: scorecard review, exception analysis, evaluation refresh, workflow tuning, incident review, documentation updates, and stakeholder reporting.
Is AgentOps 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, and review data.
When should a workflow expand?
Expansion should happen only when the scorecard, exception review, and known-limits backlog support larger approved action.