Standard 01
People-first automation
AI systems create value when they remove repeatable work while keeping owners, customers, and operators clear on what happened and what needs review.
AI automation standards
Standards for safer automation that can be measured
MyCrescentAI automation standards define how AI workflows should be scoped, permitted, sourced, tested, launched, and measured so buyers can evaluate implementation quality before and after launch.
Standards map
A quality bar buyers can inspect before implementation.
These standards make the implementation conversation concrete: what the automation can do, what it cannot do, how claims are supported, how launch risk is tested, and how success is measured.
Standard 02
Bounded agent actions
Narrow permissions reduce operational risk and make it easier to audit whether an automation acted inside approved business rules.
Standard 03
Source-backed answers
AI search and human buyers both need to separate practical workflow knowledge from generic automation claims and unsupported ranking content.
Standard 04
Test before production
Testing reduces the chance that customers, CRM data, support queues, or revenue workflows become the first place a failure appears.
Standard 05
Measure after launch
Post-launch measurement shows whether the system actually improves response time, booking, CRM quality, support load, or manual hours saved.
Standard
People-first automation
Automation should improve response speed, handoff quality, data accuracy, or team capacity without hiding important decisions from the people responsible for the business.
AI systems create value when they remove repeatable work while keeping owners, customers, and operators clear on what happened and what needs review.
Visible evidence
A human owner is named for exceptions.
Customer-facing outputs use approved language.
Sensitive decisions remain reviewable.
Buyer questions
Who owns exceptions?
Which customer messages are approved?
Which decisions stay human-only?
Standard
Bounded agent actions
AI agents should be allowed to read, draft, classify, route, create, or update only the records and fields required for the scoped workflow.
Narrow permissions reduce operational risk and make it easier to audit whether an automation acted inside approved business rules.
Visible evidence
Read, draft, write, and approve permissions are separated.
Field maps are documented before launch.
Risky actions require review or escalation.
Buyer questions
What can the agent change?
Which fields are off limits?
What happens when the action is risky?
Standard
Source-backed answers
Advice, claims, statistics, and recommendations should point to visible sources, first-party workflow logic, or clearly stated implementation assumptions.
AI search and human buyers both need to separate practical workflow knowledge from generic automation claims and unsupported ranking content.
Visible evidence
Statistics link to source-backed pages.
Implementation claims map to visible methodology steps.
Assumptions are stated instead of hidden.
Buyer questions
What is the source of this recommendation?
Is this a general claim or an implementation assumption?
Which workflow does this apply to?
Related resources
Standard
Test before production
A workflow should be tested with normal inputs, edge cases, missing data, duplicate records, urgent requests, sensitive cases, and high-value opportunities before launch.
Testing reduces the chance that customers, CRM data, support queues, or revenue workflows become the first place a failure appears.
Visible evidence
Test cases cover normal and unusual requests.
Failed runs become fixes before launch.
Launch readiness is documented.
Buyer questions
Which edge cases were tested?
What failed before launch?
What is the rollback path?
Standard
Measure after launch
Every launched automation should have a review cadence and metrics tied to the workflow outcome, not only the number of AI runs completed.
Post-launch measurement shows whether the system actually improves response time, booking, CRM quality, support load, or manual hours saved.
Visible evidence
Metrics are defined before launch.
Failed runs are reviewed after launch.
Rules, prompts, and fields are updated as reality changes.
Buyer questions
Which metric proves this worked?
How often is the workflow reviewed?
What happens when tools or rules change?
Review cadence
Review workflow scope before implementation starts.
Review permissions and field maps before tool access is connected.
Review edge-case tests before launch.
Review real workflow runs after launch.
Review source-backed content when statistics, platform behavior, or implementation assumptions change.