What is the Leading AI Agents and Automation course about?
Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing the routing, chasing and re-keying between systems that nobody owns. Each order is checked and updated against the latest insights before delivery. That is why access takes up to.
What does the Leading AI Agents and Automation cover on the situation this is built for?
Critical work happens between systems. Tasks get stuck. Data gets reentered. People chase updates that should be automatic. Now AI agents are silently doing this work, crossing apps, accessing data, making decisions, without clear ownership, audit, or control. The very function you lead is being automated around you.
What do you take away from the Leading AI Agents and Automation course?
Map all active AI agents touching your workflows Define ownership and accountability across agent-driven tasks Identify hidden risks in agent access and decision paths Secure approval chains for autonomous actions Build a governance model for agent-driven work.
How does this map to your situation?
Unmanaged AI agents performing critical tasks Lack of ownership for agent-driven outcomes Hidden access and decision paths in daily workflows Growing risk from unsecured agent communication.
What's included with your purchase?
12 modules with 12 chapters each (144 chapters) Downloadable templates and worked examples for every module Hand-built implementation playbook delivered alongside course access 30-day money-back guarantee.
What does the Leading AI Agents and Automation cover on delivery and format?
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access. Time investment: Approximately 3 hours per module, designed for senior leaders to complete at their own pace over 6 to 8 weeks.
How does this compare to the alternatives?
Unlike technical automation courses, this program focuses exclusively on leadership, governance, and operational control of AI agents, not on building or coding them. It provides actionable frameworks for decision-making, risk assessment, and oversight, not tool-specific tutorials.
What does the Leading AI Agents and Automation cover on frequently asked?
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
Closely related courses: Leading AI Agents and Automation in Enterprise Operations, Leading AI Agents and Automation for Senior Leaders.
More answers: what you get with every course, refund policy, all help answers.
Page updated 2026-10-18.
The Executive Diagnostic and Governance Toolkit
Leading AI Agents and Automation at Work
Score your own function red, amber or green, find out which part is weakest, and walk into the next budget round able to defend what you want to fix. Built for leaders reviewing the routing, chasing and re-keying between systems that nobody owns.
Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.
| 1 |
You stop guessing where you stand. You finish with a score, not an opinion: every part of your function rated red, amber or green, with the weakest ranked first. Evidence: a Quick Scan for the shape of it, then seven domain assessments of 30 scored questions each, 210 in all, rolled into one scorecard, plus a maturity radar and a current-versus-target gap analysis. |
| 2 |
You can defend the decision. You walk into the budget round with the gap named, the owner named and done defined, instead of a case built on instinct. Evidence: project charter, scope statement, RACI, requirements traceability and work breakdown structure, pre-filled in your domain's language. |
| 3 |
The work actually moves. The month after the decision is already built, so nothing stalls waiting for someone to design a form. Evidence: more than 60 project templates across all five PMBOK process groups, plus runbooks, SOPs, a KPI framework, audit checklists and a risk matrix. 55 to 65 files in total. |
| 4 |
You use it the day it lands. No blank templates to interpret. Every workbook opens with what it is, who uses it, when, how, a 1 to 5 scoring guide, what good looks like, and a worked example you delete and type over. |
The situation this is built for
Critical work happens between systems. Tasks get stuck. Data gets reentered. People chase updates that should be automatic. Now AI agents are silently doing this work—crossing apps, accessing data, making decisions—without clear ownership, audit, or control. The very function you lead is being automated around you.
Who this is for
Senior leaders responsible for operational integrity, workflow governance, and cross-system coordination in mid to large enterprises.
Who this is not for
Individual contributors looking to build AI agents, technical developers, or teams focused solely on deploying automation tools.
What you walk away with
- Map all active AI agents touching your workflows
- Define ownership and accountability across agent-driven tasks
- Identify hidden risks in agent access and decision paths
- Secure approval chains for autonomous actions
- Build a governance model for agent-driven work
How this maps to your situation
- Unmanaged AI agents performing critical tasks
- Lack of ownership for agent-driven outcomes
- Hidden access and decision paths in daily workflows
- Growing risk from unsecured agent communication
Before vs. after
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 3 hours per module, designed for senior leaders to complete at their own pace over 6 to 8 weeks.
How this compares to the alternatives
Unlike technical automation courses, this program focuses exclusively on leadership, governance, and operational control of AI agents—not on building or coding them. It provides actionable frameworks for decision-making, risk assessment, and oversight, not tool-specific tutorials.
Also included: the full course, for when you want the reasoning behind a finding (12 modules, 144 chapters)
Depth reference. The diagnostic and the templates stand on their own; this is what to read when you want the reasoning behind a finding.
- How AI agents are replacing manual task routing
- Tracing invisible handoffs between automated systems
- Recognizing when AI makes decisions without approval
- Mapping where agents interact with human workflows
- Identifying shadow automation in critical processes
- Assessing frequency of agent-to-agent communication
- Documenting unapproved data access by AI agents
- Evaluating speed versus control in agent actions
- Understanding autonomy levels in current deployments
- Reviewing examples of agent-initiated escalations
- Measuring reliance on agents for routine tasks
- Capturing where oversight is currently absent
- Describing the lifecycle of a routed work item
- Identifying steps in a multi-system approval process
- Listing common data fields rekeyed across platforms
- Tracking time lost in system-to-system transitions
- Naming roles involved in cross-platform coordination
- Defining what constitutes a completed handoff
- Charting dependencies between disconnected tools
- Documenting exceptions in automated workflows
- Classifying types of inter-system communication
- Measuring error rates in manual re-entry tasks
- Observing how context is lost between apps
- Noting where human intervention breaks automation
- Creating a registry of known AI agents
- Identifying agents embedded in business applications
- Discovering AI bots operating in messaging platforms
- Logging agents with access to customer data
- Tracking agents that initiate file transfers
- Recording agents capable of sending notifications
- Mapping agents that trigger API calls
- Documenting agents with write permissions
- Noting agents running on personal devices
- Categorizing agents by functional purpose
- Assessing agent persistence across sessions
- Verifying agent identity and authentication methods
- Listing all applications accessible to AI agents
- Reviewing authentication methods for agent logins
- Auditing API keys assigned to automated tools
- Identifying agents with admin-level privileges
- Mapping data categories accessed by each agent
- Checking for access to sensitive personal data
- Evaluating permission inheritance in agent accounts
- Assessing session duration for agent logins
- Reviewing token expiration policies for bots
- Identifying shared credentials used by agents
- Checking for role-based access controls
- Documenting exceptions to standard permission rules
- Identifying decisions currently made by agents
- Classifying types of autonomous judgments
- Mapping approval chains for agent actions
- Reviewing escalation protocols for agent errors
- Defining thresholds for human override
- Documenting cases where agents skip approvals
- Assessing confidence levels in agent recommendations
- Tracking agent-initiated task assignments
- Evaluating agent use of conditional logic
- Reviewing audit trails for agent decisions
- Identifying agent access to financial data
- Noting instances where agents alter records
- Identifying single points of failure in agent networks
- Assessing impact of agent downtime on operations
- Reviewing data leakage risks from agent logs
- Evaluating compliance exposure from agent actions
- Mapping agent access to regulated information
- Assessing risk of unauthorized data replication
- Reviewing agent behavior during system outages
- Identifying agents without monitoring in place
- Assessing potential for agent impersonation
- Evaluating risks from peer-to-peer agent communication
- Reviewing agent use of unsecured networks
- Documenting lack of encryption in agent traffic
- Naming owners for each active AI agent
- Defining responsibilities for agent maintenance
- Assigning oversight for agent decision outcomes
- Creating escalation paths for agent failures
- Setting expectations for agent performance
- Documenting handover procedures for agent ownership
- Establishing review cycles for agent behavior
- Identifying backup owners for critical agents
- Clarifying vendor versus internal responsibility
- Defining consequences for unmanaged agents
- Linking agent outcomes to performance metrics
- Requiring formal sign-off on agent deployment
- Defining principles for responsible agent use
- Creating standards for agent identity management
- Establishing agent registration requirements
- Designing approval workflows for new agents
- Setting rules for agent data access levels
- Building templates for agent use cases
- Creating audit requirements for agent activity
- Defining retention policies for agent logs
- Setting thresholds for automated actions
- Designing review boards for agent deployment
- Establishing version control for agent logic
- Documenting decommissioning procedures for agents
- Mapping all peer-to-peer agent connections
- Identifying unencrypted messaging channels
- Reviewing agent use of public networks
- Assessing risks from direct device linking
- Evaluating file transfer security protocols
- Checking for unauthorized agent meshing
- Reviewing end-to-end encryption in agent calls
- Assessing authentication in agent-to-agent handshakes
- Identifying agents using personal messaging apps
- Evaluating session hijacking risks
- Reviewing message retention policies
- Documenting third-party routing of agent traffic
- Defining moments requiring human approval
- Designing dashboards for agent monitoring
- Setting thresholds for automatic alerts
- Creating workflows for agent exception handling
- Establishing routines for agent behavior review
- Designing feedback loops from users to agents
- Building escalation paths for agent errors
- Implementing periodic agent behavior audits
- Creating logs for agent decision justification
- Training staff to supervise agent activity
- Designing override mechanisms for agents
- Establishing incident response for agent failures
- Defining success criteria for agent tasks
- Tracking accuracy of agent data entry
- Measuring time saved by agent automation
- Assessing error rates in agent decisions
- Evaluating agent response time to triggers
- Reviewing user satisfaction with agent output
- Tracking rework caused by agent mistakes
- Measuring agent uptime and availability
- Assessing consistency in agent behavior
- Comparing agent performance across teams
- Documenting variance in agent outcomes
- Linking agent KPIs to business results
- Assessing current capacity for agent growth
- Designing onboarding for new AI agents
- Creating training programs for agent supervisors
- Building centralized monitoring capabilities
- Planning for cross-departmental agent integration
- Establishing agent development standards
- Designing interoperability between agent types
- Forecasting infrastructure needs for agents
- Creating playbooks for agent incident response
- Setting benchmarks for agent maturity
- Aligning agent strategy with business goals
- Developing long-term agent governance roadmap
Frequently asked
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
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