Workflow boundary
What repeatable workflow are we reviewing, and what is outside scope?
Readiness assessmentAI workflow controls
The controls to define before an AI workflow acts
The demo can draft, route, summarize, and update. The launch question is different: what can the workflow see, what can it do, who approves, what gets logged, which failures become tests, and how does the team roll back a mistake?
This guide is an implementation-planning resource, not legal advice, compliance certification, or a guarantee that an AI workflow is safe to launch.
Best fit
Use this when a workflow is close to touching customers, CRM records, support tickets, public content, finance/admin work, regulated records, or internal approvals.
Useful dividing line
Control starts when the workflow can affect someone
A workflow does not need heavy governance because it uses AI. It needs controls when its output can change a customer interaction, a business record, a public page, a payment path, an access decision, or a regulated file.
That is the useful dividing line. A private scratchpad summarizer needs different controls than a support-triage workflow that drafts customer replies and prepares CRM notes. For the second case, combine a responsible AI review with a clear data boundary and the security review worksheet.
Central artifact
The controls checklist
Do not start with a fifty-page AI policy. Start with one named workflow and fill the controls that decide whether it can earn more permission.
Data boundary
What systems, records, fields, documents, credentials, vendors, and model exposures are allowed or excluded?
Security review worksheetModel facts
Which model/provider facts can go stale for this workload: region, quota, lifecycle, retention, SDK behavior, review cadence, and fallback?
Model Facts RegisterAnalytics source of truth
Which metric, dataset, owner, freshness rule, query path, and review trigger should an analytics agent trust?
Analytics Source-of-Truth RegisterAgent access matrix
Which agents may discover, call, stream from, authenticate with, rate-limit, log, and disable one another?
Agent Access MatrixApproval policy
What may the workflow read, draft, change, send, escalate, log, and roll back?
Approval policy worksheetDependency exceptions
Which packages, SDKs, connectors, versions, or agent touchpoints are allowed, blocked, temporarily excepted, or under review?
Dependency exception registerShadow week
What happens when the workflow proposes work against real inputs while humans still do the job?
Shadow-week field guideReview queue
How do proposed actions become reviewable decisions with source evidence, policy rules, risk reasons, and reviewer options?
Approval queue guideThreshold tuning
Which mistake is more expensive: risky items slipping through or safe items stuck in review?
Precision and recall guideReceipts
Can the team reconstruct what the workflow saw, proposed, who reviewed it, what happened, and how to correct it?
Agent receipt templateEval / bug cemetery
Do misses become durable regression cases instead of Slack anecdotes?
Agent test suitesObservability
Which queue buckets, receipt fields, reviewer overrides, and incidents will be watched after permission expands?
Queue and receipt evidenceRollback / escalation
Who owns correction, record restore, customer notice, security/legal escalation, and permission reduction?
Rollback checklistControl map
How the controls fit together
Use the policy first, rehearse with real inputs, put proposed actions into a queue, leave receipts, turn misses into eval cases, and watch the workflow after launch. Autonomy expands only when evidence supports the next permission level.
Write the policy before choosing the agent
Keep the control visible in the product surface and in the reviewer handoff, not hidden inside a prompt or launch meeting.
Let the workflow prove itself while people still do the job
Keep the control visible in the product surface and in the reviewer handoff, not hidden inside a prompt or launch meeting.
The queue should show a decision, not just a draft
Keep the control visible in the product surface and in the reviewer handoff, not hidden inside a prompt or launch meeting.
Every proposed or executed action needs a receipt
Keep the control visible in the product surface and in the reviewer handoff, not hidden inside a prompt or launch meeting.
Misses should become tests, not stories
Keep the control visible in the product surface and in the reviewer handoff, not hidden inside a prompt or launch meeting.
Rollback is part of the permission decision
Keep the control visible in the product surface and in the reviewer handoff, not hidden inside a prompt or launch meeting.
Copyable checklist
Paste this into a launch planning doc
This plain-text block is intentionally boring. It is the minimum checklist a team can reuse before a workflow gets source access, write permission, customer visibility, or reduced review.
Related artifacts
Open the worksheet that matches the next decision
The hub is a control sequence, not a replacement for the worksheets and templates. Need a specific artifact first? Use the AI workflow artifacts library to compare access maps, review packets, receipts, sandbox contracts, source maps, and rollout plans before walking the full checklist.
Approval policy worksheet
Define allowed reads, draft-only actions, approval-required actions, prohibited actions, receipts, escalation, and rollback.
Use the approval policy worksheetWrong-change rollback map
Turn the buyer objection into a concrete table of action boundaries, reviewer evidence, receipts, rollback ownership, correction paths, and stop rules.
Map the rollback pathRollback checklist
Fill the copyable checklist that names the workflow, v1 action ceiling, approval evidence, receipt fields, rollback owner, manual fallback, and permission criteria.
Copy the rollback checklistRollback plan worksheet
Define stop triggers, owners, affected systems, receipt fields, repair actions, notifications, and re-enable criteria.
Plan the rollback pathSecurity review worksheet
Map source systems, excluded records, vendor/model exposure, credentials, retention, and open security questions.
Map the data boundaryAgent Access Matrix
Record allowed callers, required scopes, operations, streaming rules, rate limits, credential sources, log destinations, disable owners, and review triggers before agents call each other.
Copy the access matrixApproval queue guide
Design the review lane around proposed actions, source evidence, risk reasons, policy rules, and reviewer decisions.
Read the approval queue guideAgent receipt template
Log the trigger, sources, proposed action, policy check, reviewer decision, final action, version, and rollback path.
Copy the receipt templateDependency exception register
Record dependency evidence, owner, status, allowed actions, blocked actions, review deadline, rollback path, and receipt requirements before agents or production workflows act.
Copy the dependency registerLearn paths
If the workflow is not named yet, use the Learn center and readiness assessment before writing controls.
Browse AI workflow guidesLaunch review packet note: keep using the approval policy and receipt rollback fields until that packet route exists.
Source posts
Read the supporting BaristaLabs playbooks
These eight posts explain the individual controls behind the checklist.
Write the AI approval policy before you choose the agent
Use this when the team is tempted to compare platforms before writing permission rules.
Run a shadow week before you automate the workflow
Use this to test real inputs before the workflow earns more autonomy.
Build the approval queue before you build the agent
Use this to turn proposed actions into a reviewable product surface.
Precision, recall, and the approval queue
Use this to tune review thresholds around the business cost of mistakes.
Agent receipts: what to log before AI touches customer work
Use this to make each proposed or executed action reconstructable.
AWS Bedrock AgentCore agent test suites
Use this to turn misses into durable regression cases and release gates.
OpenAI third-party evals and agent results
Use this to ask what an eval harness actually measured before trusting a score.
AI code review bots need review lanes
Use this as a developer-workflow example of lanes, permissions, receipts, and rollback.
Launch decision
How to read the control map
Proceed
Proceed with restrictions
Revise
Shadow longer
Stop
FAQ
Questions teams ask before permission expands
Is this a compliance checklist?
No. It is an implementation-planning guide for one workflow. Regulated or sensitive workflows still need the client's legal, privacy, compliance, security, or operational stakeholders.
What should we create first?
If you already have a candidate workflow, write the approval policy and security boundary first. If you have not chosen a workflow, start with the readiness assessment and Learn guide paths.
When can an AI workflow act without review?
Only after the team has defined the action boundary, tested real inputs, watched misses, logged receipts, confirmed rollback, and agreed that the remaining risk is acceptable for that narrow action.
Do all workflows need a shadow week?
Any workflow close to customers, records, money, publishing, access, or regulated work should shadow-run before permission expands. Lower-risk internal drafting may need a lighter review sample, but it still needs boundaries.
What is the difference between a receipt and an audit log?
A receipt is the run-level record a reviewer or operator can read: trigger, source evidence, proposed action, policy check, reviewer decision, final action, destination, version, and rollback path. It may live inside a broader audit-log system.
Need help with one workflow?
Bring the workflow; we will map the controls before buildout
BaristaLabs helps small teams turn one candidate AI workflow into a scoped implementation plan: data boundary, approval policy, reviewer lane, receipt fields, eval cases, rollback path, and launch decision.