Skip to main content
Image coming soon

GEN1797 Mastering AI Agents and Workflow Automation

$199.00
Adding to cart… The item has been added

The Executive Diagnostic and Governance Toolkit

Mastering AI Agents and Workflow Automation

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 AI agents and workflow automation.

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

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.

What you walk out with
A scored, ranked picture of your own function, and a defensible answer to what to fix first.
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 Quick Scan is one sitting. You will know your weakest area before the day is out.
Nothing in it is generic project management: the build rejects any file that could belong to another course. Updated after you enrol, so it reflects where the work stands now. The 144-chapter course is included behind it, for the parts you want to go deeper on.
You built the workflows. Now AI agents are rewriting them without you.

The situation this is built for

You are accountable for systems that are no longer fully human-led. AI agents now initiate, adapt, and complete workflows with minimal supervision. You must determine which decisions should remain under human control, which processes are becoming obsolete, and how to maintain governance when agents train on your data and act on their own logic. The tools are evolving faster than the policies, and your team is caught between legacy structures and autonomous execution.

Who this is for

Head of Automation in mid-to-large enterprises managing digital workers, process orchestration, and AI integration across departments.

Who this is not for

Individual contributors focused only on RPA scripting, vendors selling automation tools, or teams not yet managing live AI-driven workflows.

What you walk away with

  • Map current workflow dependencies on AI agents
  • Identify decision rights in autonomous process execution
  • Evaluate internal control gaps in agent-driven tasks
  • Align security policies with distributed AI actions
  • Define escalation paths for unattended process deviations

How this maps to your situation

  • Current state assessment of AI agent integration
  • Decision rights and governance in autonomous systems
  • Security and compliance in distributed agent environments
  • Strategic roadmap development for automation evolution

Before vs. after

Before
Overwhelmed by invisible agents making decisions outside documented workflows, lacking clarity on control, accountability, and long-term strategy.
After
Equipped with a clear assessment framework, defined governance model, and prioritized roadmap for leading AI agent integration with confidence.

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 8–10 hours per module, designed for self-paced learning with actionable checkpoints. Total investment: 96–120 hours.

If nothing changes
Without a structured assessment, your organization risks loss of control over critical workflows, undetected compliance breaches, and erosion of trust in automated systems due to unpredictable agent behavior.

How this compares to the alternatives

Unlike vendor-specific certifications or academic courses, this program focuses exclusively on the leadership, governance, and strategic assessment challenges faced by those responsible for AI-driven workflows. It does not teach coding or tool configuration. Instead, it equips leaders with frameworks to evaluate, control, and evolve automation systems where AI agents operate independently.

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.

Module 1. Defining the Modern Automation Function
Establish a clear scope of ownership in an era where AI agents perform tasks without explicit instructions.
12 chapters in this module
  1. Understanding the shift from scripted to autonomous workflows
  2. Mapping roles between human operators and AI agents
  3. Identifying core responsibilities of automation leadership today
  4. Differentiating between RPA and agentic process execution
  5. Assessing organizational expectations of automation teams
  6. Documenting current workflow ownership boundaries
  7. Clarifying decision rights in hybrid human-agent teams
  8. Recognizing when automation extends beyond task completion
  9. Evaluating governance models for self-directed agents
  10. Defining escalation protocols for unexpected agent behavior
  11. Integrating audit requirements into agent-driven processes
  12. Setting performance benchmarks for autonomous execution
Module 2. Inventorying Existing Workflow Architecture
Catalog every active workflow to determine where AI agents are already embedded or could be introduced.
12 chapters in this module
  1. Listing all current process automation touchpoints
  2. Classifying workflows by level of human intervention
  3. Tracking data sources feeding into automated systems
  4. Identifying processes with external API dependencies
  5. Documenting approval chains within digital workflows
  6. Noting instances where AI agents modify workflows independently
  7. Reviewing version history of automated process logic
  8. Mapping data lineage across agent-handled tasks
  9. Assessing frequency of unsupervised agent actions
  10. Logging incident reports tied to autonomous decisions
  11. Categorizing workflows by risk exposure level
  12. Benchmarking process stability across agent types
Module 3. Assessing Agent Capabilities and Limits
Evaluate what AI agents can reliably do today and where they require human oversight.
12 chapters in this module
  1. Defining functional scope for each agent type in use
  2. Testing agent accuracy in decision-making scenarios
  3. Measuring consistency across repeated task executions
  4. Identifying edge cases where agents fail silently
  5. Evaluating agent ability to interpret ambiguous inputs
  6. Reviewing training data sources influencing agent logic
  7. Assessing real-time adaptation versus fixed rules
  8. Comparing agent output against human-reviewed outcomes
  9. Documenting known failure modes of current agents
  10. Determining thresholds for human-in-the-loop requirements
  11. Analyzing agent response time under load conditions
  12. Validating agent compliance with operational policies
Module 4. Evaluating Decision Ownership in Workflows
Determine who owns each decision point when agents initiate actions based on learned patterns.
12 chapters in this module
  1. Mapping decision trees within agent-driven workflows
  2. Identifying which approvals have been automated
  3. Assessing delegation of authority to AI agents
  4. Reviewing historical decisions made without human input
  5. Documenting escalation paths for contested outcomes
  6. Clarifying accountability for incorrect agent choices
  7. Defining review cycles for agent-generated decisions
  8. Establishing audit trails for autonomous actions
  9. Evaluating impact of agent decisions on downstream teams
  10. Balancing speed of execution with oversight needs
  11. Classifying decisions by reversibility and risk level
  12. Setting thresholds for mandatory human validation
Module 5. Auditing Data Flows in Agent Systems
Trace how data moves through AI agents and where integrity might be compromised.
12 chapters in this module
  1. Mapping end-to-end data pathways in automated workflows
  2. Identifying points where agents transform raw inputs
  3. Assessing data quality checks within agent logic
  4. Reviewing data retention policies across systems
  5. Tracking personally identifiable information in workflows
  6. Evaluating encryption standards for agent-transmitted data
  7. Noting third-party data sharing within agent networks
  8. Validating data source authenticity for agent training
  9. Monitoring for data drift affecting agent performance
  10. Assessing synchronization between agent and source systems
  11. Documenting data ownership across distributed workflows
  12. Testing recovery procedures for corrupted agent data
Module 6. Analyzing Security and Access Controls
Ensure that AI agents operate within defined security perimeters and do not create hidden vulnerabilities.
12 chapters in this module
  1. Reviewing authentication methods for AI agents
  2. Assessing privilege levels assigned to each agent
  3. Identifying agent access to sensitive databases
  4. Evaluating session management for long-running agents
  5. Documenting agent-to-agent communication protocols
  6. Testing isolation between production and test agents
  7. Reviewing password and key rotation policies
  8. Assessing exposure from agent-initiated external calls
  9. Validating firewall rules governing agent traffic
  10. Monitoring for unauthorized agent replication
  11. Auditing changes to agent permissions over time
  12. Enforcing role-based access within agent frameworks
Module 7. Evaluating Learning and Adaptation Loops
Understand how AI agents improve over time and whether their learning aligns with business goals.
12 chapters in this module
  1. Identifying feedback mechanisms in agent workflows
  2. Tracking performance improvements across iterations
  3. Assessing agent adaptation to new data patterns
  4. Reviewing human corrections used in agent training
  5. Measuring drift from original process design intent
  6. Evaluating unintended behaviors from learned logic
  7. Documenting agent self-correction attempts
  8. Assessing impact of environmental changes on agents
  9. Validating learning outcomes against success metrics
  10. Identifying blind spots in agent observation capabilities
  11. Reviewing frequency of model retraining cycles
  12. Balancing exploration versus exploitation in agents
Module 8. Governance of Autonomous Workflows
Establish oversight structures that maintain control without stifling innovation.
12 chapters in this module
  1. Defining governance board responsibilities for AI agents
  2. Setting frequency for agent performance reviews
  3. Establishing change approval workflows for agent logic
  4. Documenting compliance requirements for agent actions
  5. Reviewing regulatory alignment of autonomous decisions
  6. Creating transparency reports for agent operations
  7. Implementing version control for agent configurations
  8. Tracking policy exceptions granted to agent teams
  9. Assessing cross-functional impact of agent changes
  10. Enforcing documentation standards for agent updates
  11. Scheduling regular risk assessments for agent use
  12. Coordinating legal and compliance input on agent rules
Module 9. Measuring Performance and Outcomes
Develop meaningful KPIs that reflect both efficiency gains and hidden costs of AI agent use.
12 chapters in this module
  1. Defining success metrics for autonomous workflows
  2. Tracking error rates in agent-driven processes
  3. Measuring time saved versus rework introduced
  4. Assessing downstream impact of agent decisions
  5. Evaluating consistency of output across repetitions
  6. Monitoring for degradation in service quality
  7. Calculating cost of ownership for each agent type
  8. Reviewing human intervention frequency per process
  9. Benchmarking agent performance against human teams
  10. Identifying hidden bottlenecks in agent workflows
  11. Tracking escalation incidents tied to agent actions
  12. Validating accuracy of agent-generated reporting
Module 10. Planning for Scalability and Resilience
Prepare infrastructure and policies for widespread deployment of AI agents.
12 chapters in this module
  1. Assessing current infrastructure capacity limits
  2. Evaluating load balancing across agent clusters
  3. Planning for failover during agent outages
  4. Designing redundancy for critical agent functions
  5. Reviewing monitoring tools for agent health
  6. Testing disaster recovery for agent environments
  7. Estimating resource needs for scaled deployments
  8. Assessing impact of agent growth on IT operations
  9. Planning for agent lifecycle management
  10. Evaluating dependency chains between agent systems
  11. Designing scalable data pipelines for agent use
  12. Establishing capacity review cycles for agent fleets
Module 11. Aligning AI Agents with Business Strategy
Ensure that agent-driven automation supports long-term organizational goals.
12 chapters in this module
  1. Mapping agent activities to strategic initiatives
  2. Assessing alignment with customer experience goals
  3. Evaluating contribution to operational resilience
  4. Reviewing agent role in competitive differentiation
  5. Aligning automation KPIs with business outcomes
  6. Assessing impact on workforce planning decisions
  7. Evaluating sustainability implications of agent use
  8. Reviewing brand risk from autonomous actions
  9. Connecting agent capabilities to innovation goals
  10. Assessing scalability of agent solutions enterprise-wide
  11. Balancing automation speed with ethical considerations
  12. Integrating agent performance into executive reporting
Module 12. Building the Future State Roadmap
Create a prioritized plan for evolving your automation function in response to agent capabilities.
12 chapters in this module
  1. Summarizing current state assessment findings
  2. Identifying high-impact improvement opportunities
  3. Prioritizing initiatives by risk and reward profile
  4. Defining milestones for agent capability upgrades
  5. Assigning ownership for transformation efforts
  6. Creating communication plan for organizational change
  7. Establishing feedback loops for roadmap adjustments
  8. Integrating agent development into IT planning
  9. Setting timelines for governance enhancements
  10. Aligning budget requests with strategic priorities
  11. Documenting assumptions underlying future state design
  12. Finalizing executive presentation of roadmap plan

Frequently asked

Who is this course designed for?
This course is for leaders responsible for enterprise automation functions where AI agents are actively making decisions and executing tasks without direct human input.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover specific AI tools or platforms?
No. The course focuses on operational control, decision ownership, and strategic leadership in environments with autonomous agents, not on platform-specific implementation.
What deliverables come with the course?
You receive downloadable templates, worked examples for every chapter, and a hand-built implementation playbook tailored to your automation environment.
Can I access the course material after completion?
Yes. You retain access to all materials indefinitely, including future updates to core frameworks.
What formats do the templates come in?
The implementation playbook downloads as PDF and editable XLSX. The course reads in your learning environment and exports to PDF for offline use. The files are yours to keep.
Can I share this with my team?
The licence is per person. Team pricing opens from three seats: reply to the order confirmation with TEAM and we will set it up.
How quickly can I start?
The diagnostic is one sitting and the templates work straight out of the kit. Account access takes up to 24 hours rather than being instant, because every order is checked and updated against the latest sources before it is delivered.
$199 one-time. Approximately 8–10 hours per module, designed for self-paced learning with actionable checkpoints. Total investment: 96–120 hours..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee·Know your weakest area today·210 scored questions·Course included· Account access within 24 hours
30-day money-back guarantee, no questions asked.
Thousands of organisations have bought from The Art of Service since 2000.
!