Launch AI Agents
Prepare your AI agents for controlled operations with the right guardrails, rules, and approvals.
- Define allowed actions
- Set approvals and escalation paths
- Launch with control
The AI Control Library for Teams Building Agents.
Practical playbooks, use cases, templates, and field insights for moving AI agents from idea to approved, controlled production.
AI Control Library
Step-by-step playbooks for approving, controlling, and deploying AI agents.
Topics
8 resources
Category → Topic → Details · Playbooks selected
Control AI Agents Before They Take Action
Industry Risk & Control Matrix
Define the risk, apply the right control, and decide what AI can do
AI Agent Action
Risk Check
Control Layer
ApproveSafe to proceed within policy
EscalateHuman review required
BlockAction not authorized
Monitor & Audit
IndustryRiskControl
Healthcare
Wrong patient prioritization, unsafe escalation, patient-data exposure.
Approval for patient-impacting actions, policy rules, audit trail.
Manufacturing
Defective production decisions, unsafe line interruption, maintenance errors.
Approval before line-impacting actions, safety thresholds, exception rules.
Hospitality
Booking mistakes, guest-impacting decisions, refund or compensation errors.
Approval for compensation and rate overrides, booking rules, customer action logs.
Financial Services
Credit/payment errors, fraud misses, compliance breaches.
Decision thresholds, dual approval, policy enforcement, evidence logs.
Retail
Refund abuse, incorrect order actions, inventory mistakes, checkout errors.
Approval gates for refunds and customer-impacting actions, policy rules, audit logs.
IndustryRiskControl
IoT
Unauthorized device actions, downtime, unsafe remote commands.
Device policy rules, approval for remote actions, continuous monitoring.
OT
Unsafe machine commands, production disruption, safety incidents.
Hard runtime blocks, safety approval gates, operator confirmation.
Public Sector
Non-compliant service decisions, permit errors, citizen-impacting actions.
Policy enforcement, approval workflows, explainability, case audit trail.
Education
Enrollment mistakes, policy exception misuse, student-data issues.
Approval for exceptions, access controls, policy checks, audit logs.
Energy & Utilities
Outage-response errors, unsafe grid actions, service disruption.
Approval for operational actions, safety rules, runtime monitoring.
01
AI Control Playbook
Choose the playbook that matches your AI initiative, risk level, and control needs.
Control AI Decisions
Define how AI decisions are made, approved, blocked, or escalated to meet your risk and business needs.
- Define decision points
- Configure approval logic
- Reduce risky automation
Evidence & Audit
Capture the evidence required to prove that AI actions were safe, approved, and explainable.
- Standardize evidence collection
- Improve audit readiness
- Support accountability
Train Teams
Help your team understand AI risk, controls, and their role in safe, responsible AI.
- Build role-based readiness
- Reinforce policy execution
- Improve team confidence
02
From Resource to Control Plan
A simple path from selection to controlled AI.
Choose use case
Select the AI activity your team wants to control.
Review the resource
Use a trusted resource to understand risks and controls.
Select approval gates
Define who must approve, when, and under what conditions.
Define runtime rules
Set rules for actions, decisions, and exceptions.
Collect evidence
Define what evidence must be captured before and after action.
Create a Trust Pack
Compile your controls, evidence, and approvals for audit and oversight.
03
Example Runtime Decisions
See how TRUSTELIS handles different AI decisions at runtime.
Approved
ExampleAuto-categorize support tickets
WhyLow risk, policy met, all controls satisfied
NextProceed. Evidence captured automatically.
Needs Approval
ExampleApply discount over policy threshold
WhyMedium risk, requires human review
NextRouted to approver. Awaiting decision.
Blocked
ExamplePay vendor outside approved list
WhyHigh risk, violates policy or rule
NextAction blocked. Policy exception required.
Escalated
ExampleHigh value refund request
WhyException outside review authority
NextEscalated to risk or leadership review.
04
Ready to build your control plan?
Use the resources above to build a safe, approved, and audit-ready AI control plan.
The AI Control Library
for Teams Building Agents.
for Teams Building Agents.
Practical playbooks, use cases, templates, and field insights for moving AI agents from idea to approved, controlled production.
AI Control Library
Step-by-step playbooks for approving, controlling, and deploying AI agents.
Topics
8 resources
Category → Topic → Details · Playbooks selected
Control AI Agents
Before They Take Action
Industry Risk & Control Matrix
Define the risk, apply the right control, and decide what AI can do.
AI Agent ActionThe Agent prepares a business action.
Risk CheckThe action is evaluated against its impact and risk.
Control LayerPolicies, thresholds, approvals, and runtime rules are applied.
ApproveSafe to proceed within policy
EscalateHuman review required
BlockAction not authorized
Monitor & AuditThe decision, action, and evidence are recorded.
Healthcare
Risk
Wrong patient prioritization, unsafe escalation, patient-data exposure.
Control
Approval for patient-impacting actions, policy rules, audit trail.
Manufacturing
Risk
Defective production decisions, unsafe line interruption, maintenance errors.
Control
Approval before line-impacting actions, safety thresholds, exception rules.
Hospitality
Risk
Booking mistakes, guest-impacting decisions, refund or compensation errors.
Control
Approval for compensation and rate overrides, booking rules, customer action logs.
Financial Services
Risk
Credit/payment errors, fraud misses, compliance breaches.
Control
Decision thresholds, dual approval, policy enforcement, evidence logs.
Retail
Risk
Refund abuse, incorrect order actions, inventory mistakes, checkout errors.
Control
Approval gates for refunds and customer-impacting actions, policy rules, audit logs.
IoT
Risk
Unauthorized device actions, downtime, unsafe remote commands.
Control
Device policy rules, approval for remote actions, continuous monitoring.
OT
Risk
Unsafe machine commands, production disruption, safety incidents.
Control
Hard runtime blocks, safety approval gates, operator confirmation.
Public Sector
Risk
Non-compliant service decisions, permit errors, citizen-impacting actions.
Control
Policy enforcement, approval workflows, explainability, case audit trail.
Education
Risk
Enrollment mistakes, policy exception misuse, student-data issues.
Control
Approval for exceptions, access controls, policy checks, audit logs.
Energy & Utilities
Risk
Outage-response errors, unsafe grid actions, service disruption.
Control
Approval for operational actions, safety rules, runtime monitoring.
01
AI Control Playbook
Choose the playbook that matches your AI initiative, risk level, and control needs.
Launch AI Agents
Prepare your AI agents for controlled operations with the right guardrails, rules, and approvals.
- Define allowed actions
- Set approvals and escalation paths
- Launch with control
Control AI Decisions
Define how AI decisions are made, approved, blocked, or escalated to meet your risk and business needs.
- Define decision points
- Configure approval logic
- Reduce risky automation
Evidence & Audit
Capture the evidence required to prove that AI actions were safe, approved, and explainable.
- Standardize evidence collection
- Improve audit readiness
- Support accountability
Train Teams
Help your team understand AI risk, controls, and their role in safe, responsible AI.
- Build role-based readiness
- Reinforce policy execution
- Improve team confidence
02
From Resource to Control Plan
A simple path from selection to controlled AI.
Choose use case
Select the AI activity your team wants to control.
Review the resource
Use a trusted resource to understand risks and controls.
Select approval gates
Define who must approve, when, and under what conditions.
Define runtime rules
Set rules for actions, decisions, and exceptions.
Collect evidence
Define what evidence must be captured before and after action.
Create a Trust Pack
Compile your controls, evidence, and approvals for audit and oversight.
03
Example Runtime Decisions
See how TRUSTELIS handles different AI decisions at runtime.
Approved
ExampleAuto-categorize support tickets
WhyLow risk, policy met, all controls satisfied
NextProceed. Evidence captured automatically.
Needs Approval
ExampleApply discount over policy threshold
WhyMedium risk, requires human review
NextRouted to approver. Awaiting decision.
Blocked
ExamplePay vendor outside approved list
WhyHigh risk, violates policy or rule
NextAction blocked. Policy exception required.
Escalated
ExampleHigh value refund request
WhyException outside review authority
NextEscalated to risk or leadership review.
04
Ready to build your control plan?
Use the resources above to build a safe, approved, and audit-ready AI control plan.