Skip to main content
Image coming soon

GEN9194 Mastering AI Agents and Workflow Automation

$200.00
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're responsible for automation that must scale with intelligence — but the tools, teams, and expectations keep shifting.

The situation this is built for

AI agents are no longer theoretical. They are embedded in data pipelines, customer interactions, and internal workflows. As Head of Automation, you are expected to lead this function with precision, yet there is no standard way to assess maturity, no shared language for risk, and no playbook for scaling agent-based workflows responsibly. The work you own is expanding faster than the frameworks to govern it.

Who this is for

Head of Automation in mid-to-large organizations, responsible for designing, deploying, and governing AI-driven workflows and agent systems at scale.

Who this is not for

Individual contributors not responsible for cross-functional automation strategy, vendors selling automation tools, or teams focused solely on RPA or script-based workflows without AI integration.

What you walk away with

  • A complete diagnostic of your current AI agent maturity across 12 dimensions
  • Clear decision criteria for when to build, extend, or retire agent workflows
  • A mapped view of data provenance, agent handoffs, and failure recovery paths
  • Executive-ready assessment artifacts for governance and investment decisions
  • A 12-month roadmap for scaling automation with accountability and auditability

How this maps to your situation

  • Assessment
  • Diagnosis
  • Decision-making
  • Roadmap

Before vs. after

Before
You manage automation through fragmented systems, unclear ownership, and reactive decision-making.
After
You lead with a complete, validated assessment of AI agent maturity and a clear path to scale with governance.

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 to be consumed incrementally alongside regular work. Total estimated time: 36 hours over 12 weeks.

If nothing changes
Without a structured assessment, your automation function will continue to grow in complexity without control, leading to undetected failures, compliance exposure, and wasted investment in uncoordinated agent projects.

How this compares to the alternatives

Unlike vendor-led training or generic AI courses, this program focuses exclusively on the assessment and governance of AI agents from the perspective of the leader responsible for outcomes. No tool-specific content, no sales pitch, no abstraction — just field-tested evaluation frameworks used by practitioners in similar roles.

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 Scope of AI Agent Ownership
Establish what falls under your responsibility and where boundaries with other teams create friction or risk.
12 chapters in this module
  1. Identifying all active AI agent deployments in your organization
  2. Mapping ownership of agent design versus agent operation
  3. Documenting which teams control agent training data sources
  4. Assessing integration points between agents and core systems
  5. Tracking agent versioning and deployment lifecycle stages
  6. Classifying agent types by autonomy level and function
  7. Evaluating escalation paths when agents fail or stall
  8. Reviewing audit trails for agent decision records
  9. Determining which agents interact with external parties
  10. Analyzing agent concurrency and load handling capacity
  11. Inventorying data sources feeding agent decision loops
  12. Establishing baseline metrics for agent uptime and reliability
Module 2. Assessing Data Provenance in Agent Workflows
Trace how data flows into, through, and out of agent systems to ensure integrity and compliance.
12 chapters in this module
  1. Mapping data lineage from source to agent ingestion
  2. Identifying transformation steps before data enters agents
  3. Verifying schema consistency across data handoff points
  4. Detecting stale or outdated data in agent memory stores
  5. Assessing real-time versus batch data update frequency
  6. Evaluating data access permissions for agent components
  7. Checking for data drift in agent training versus production
  8. Reviewing data retention policies for agent logs
  9. Auditing data deletion and anonymization in agent flows
  10. Measuring data throughput limits in agent pipelines
  11. Validating data accuracy at agent decision checkpoints
  12. Documenting fallback sources when primary data is unavailable
Module 3. Evaluating Agent Autonomy Thresholds
Determine how much decision-making authority agents have and where human oversight is required.
12 chapters in this module
  1. Defining low-risk actions agents can perform independently
  2. Establishing escalation criteria for high-impact decisions
  3. Reviewing agent confidence scoring mechanisms in place
  4. Assessing when agents should request human validation
  5. Mapping approval chains for agent-initiated transactions
  6. Evaluating agent behavior under uncertain input conditions
  7. Testing agent fallback strategies during ambiguous states
  8. Documenting override procedures for agent actions
  9. Analyzing historical incidents where agents exceeded authority
  10. Setting thresholds for agent learning from user interactions
  11. Reviewing compliance requirements for autonomous actions
  12. Benchmarking agent decision speed against manual processes
Module 4. Measuring Workflow Orchestration Complexity
Understand how agents coordinate with other systems and what that means for reliability and maintenance.
12 chapters in this module
  1. Mapping all systems involved in multi-agent workflows
  2. Identifying synchronization points between agent tasks
  3. Assessing retry logic when agent steps fail
  4. Reviewing timeout settings across agent handoffs
  5. Documenting dependency chains in agent-driven processes
  6. Evaluating load balancing across parallel agent executions
  7. Analyzing agent queuing behavior under high volume
  8. Measuring end-to-end latency in agent workflows
  9. Tracking orphaned or abandoned agent tasks
  10. Reviewing state persistence between agent interactions
  11. Assessing idempotency of agent-triggered operations
  12. Identifying single points of failure in orchestration
Module 5. Auditing Agent Learning and Adaptation
Examine how agents improve over time and whether learning introduces risk.
12 chapters in this module
  1. Tracking how agents update their internal models
  2. Reviewing feedback loops from user interactions
  3. Assessing frequency of agent retraining cycles
  4. Evaluating data sources used for agent learning
  5. Monitoring for bias amplification in agent outputs
  6. Documenting version control for agent knowledge bases
  7. Reviewing change logs after agent updates
  8. Testing agent consistency before and after updates
  9. Establishing rollback procedures for agent regressions
  10. Measuring drift between agent behavior and original intent
  11. Auditing external inputs influencing agent learning
  12. Defining boundaries for unsupervised agent adaptation
Module 6. Assessing Failure Recovery and Resilience
Evaluate how well agent systems handle errors, outages, and unexpected inputs.
12 chapters in this module
  1. Documenting known failure modes in current agents
  2. Reviewing alerting mechanisms for agent downtime
  3. Testing agent recovery from network interruptions
  4. Evaluating data consistency after agent restarts
  5. Assessing fallback workflows when agents are unavailable
  6. Measuring mean time to detect and resolve agent issues
  7. Reviewing logs for silent agent failures
  8. Analyzing retry strategies for agent-to-agent calls
  9. Testing agent behavior under resource constraints
  10. Evaluating human intervention time for stuck workflows
  11. Mapping root cause analysis process for agent errors
  12. Establishing service level objectives for agent uptime
Module 7. Evaluating Human-Agent Collaboration Models
Understand how people and agents work together and where friction occurs.
12 chapters in this module
  1. Identifying tasks where agents assist human workers
  2. Mapping handoff points from agents to humans
  3. Assessing clarity of agent communication with users
  4. Reviewing training materials for agent interaction
  5. Measuring user trust in agent recommendations
  6. Analyzing frequency of agent override by humans
  7. Evaluating human understanding of agent limitations
  8. Documenting escalation paths from agent to human
  9. Reviewing feedback mechanisms from humans to agents
  10. Assessing role-based access to agent controls
  11. Measuring time saved versus time lost in collaboration
  12. Benchmarking agent support quality across teams
Module 8. Assessing Scalability of Agent Infrastructure
Determine whether current systems can support growth in agent count and workload.
12 chapters in this module
  1. Measuring current agent deployment density per environment
  2. Reviewing infrastructure costs per agent instance
  3. Assessing auto-scaling capabilities for agent workloads
  4. Evaluating database performance under agent load
  5. Testing agent startup time during peak demand
  6. Reviewing concurrency limits in agent execution engines
  7. Analyzing memory usage patterns across agent types
  8. Measuring network bandwidth consumption by agents
  9. Assessing storage growth from agent logs and outputs
  10. Evaluating container orchestration for agent fleets
  11. Reviewing agent lifecycle management at scale
  12. Benchmarking infrastructure spend against agent output value
Module 9. Governance of Agent Development Lifecycles
Examine how agents are built, tested, and released, and whether controls are sufficient.
12 chapters in this module
  1. Mapping the agent development pipeline from concept to production
  2. Reviewing code review practices for agent logic
  3. Assessing testing coverage for agent decision paths
  4. Evaluating security scanning in agent deployment workflows
  5. Documenting access controls for agent configuration
  6. Reviewing change approval processes for agent updates
  7. Measuring deployment frequency for agent iterations
  8. Analyzing rollback success rates after agent failures
  9. Tracking technical debt in agent codebases
  10. Assessing documentation completeness for agent functions
  11. Reviewing third-party library usage in agent implementations
  12. Establishing audit readiness for agent development
Module 10. Evaluating Compliance and Audit Readiness
Ensure agent workflows meet regulatory and internal policy requirements.
12 chapters in this module
  1. Identifying regulations applicable to agent-driven processes
  2. Reviewing data handling compliance in agent workflows
  3. Assessing recordkeeping requirements for agent actions
  4. Evaluating consent mechanisms for agent interactions
  5. Documenting data residency constraints for agent operations
  6. Reviewing encryption standards in agent communications
  7. Testing agent adherence to access control policies
  8. Auditing agent behavior against compliance baselines
  9. Measuring compliance gap closure timelines
  10. Establishing agent-specific audit reporting templates
  11. Reviewing external auditor feedback on agent systems
  12. Defining compliance ownership for multi-team agent projects
Module 11. Assessing Cross-Team Coordination Models
Understand how different teams collaborate on agent design, deployment, and maintenance.
12 chapters in this module
  1. Mapping stakeholders involved in agent lifecycle decisions
  2. Reviewing meeting rhythms for agent status updates
  3. Assessing clarity of roles in agent ownership models
  4. Evaluating handoff protocols between development and operations
  5. Documenting incident response coordination for agent failures
  6. Reviewing budget allocation processes for agent projects
  7. Analyzing decision latency in cross-team agent approvals
  8. Measuring alignment between data and engineering teams
  9. Assessing communication tools used for agent collaboration
  10. Reviewing shared documentation practices for agent systems
  11. Evaluating conflict resolution mechanisms for agent disputes
  12. Benchmarking cross-team delivery speed for agent features
Module 12. Building the Future State Assessment
Synthesize findings into a strategic roadmap for maturing your automation function.
12 chapters in this module
  1. Consolidating maturity scores across all twelve modules
  2. Identifying high-impact improvement opportunities
  3. Prioritizing initiatives based on risk and return
  4. Defining target state for agent autonomy levels
  5. Establishing metrics for future state success
  6. Aligning roadmap with business growth projections
  7. Documenting resource requirements for transformation
  8. Reviewing organizational readiness for change
  9. Building executive communication plan for automation vision
  10. Creating phased delivery plan with milestone checkpoints
  11. Designing feedback loops for roadmap iteration
  12. Finalizing governance model for future agent evolution

Frequently asked

Who is this course designed for?
It is designed for leaders who own AI agent and workflow automation functions, typically at the director or head of automation level, responsible for scaling intelligent systems across organizations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover specific tools or platforms?
No. The course focuses on assessment frameworks, decision criteria, and operational governance — not on any specific technology stack or vendor solution.
Will I receive templates or practical tools?
Yes. Each module includes downloadable templates and worked examples, plus a hand-built implementation playbook delivered at course access.
Can I use this assessment for executive reporting?
Yes. The course guides you to produce executive-ready artifacts that clearly communicate maturity, risk, and roadmap options.
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 3 hours per module, designed to be consumed incrementally alongside regular work. Total estimated time: 36 hours over 12 weeks..

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.
!