AI Agents for Senior Managers Training Course: Use Cases, Risks and Oversight

Programme Summary

AI agents are the subject of this one-week training course for senior managers and heads of function new to agentic AI, ending in an AI agent adoption brief for one business area. Agents now plan steps, use software tools and act with limited supervision, yet many leaders cannot tell a real agent from a chatbot or a scripted automation. The programme builds a working vocabulary, then screens use cases, examines risks and sets oversight rules, applying each tool to a function the participant leads. Mars Training Centre UK delivers this programme on AI agents to mixed-organisation cohorts.

The Business Case

Vendors describe almost every product as an agent, so leaders risk funding tools that add cost without changing outcomes, or approving systems that act without clear limits. Where AI agents are judged by demonstration alone, organisations collect pilots with no owner and no stopping rule. Managers who can screen use cases, name the risks and set oversight before launch make steadier decisions.

• Pilot Selection Quality: Improves as use cases are screened against value, effort and risk.
• Unsupervised Action Exposure: Falls as limits, approvals and escalation routes are agreed first.
• Decision Time on Proposals: Falls as managers use a shared vocabulary to question vendors.

Principles and Evidence Base

• NIST AI Risk Management Framework: Its govern, map, measure and manage functions structure the risk work in Sessions 3 and 4.
• ISO/IEC 42001: The standard for AI management systems frames the policy, roles and records discussed in Session 4.
• Human oversight principles: Levels of autonomy and approval points guide the limits designed in Session 4.

Intended Participants

• Senior managers who are asked to sponsor or approve AI agent proposals.
• Heads of function who decide which tasks may be handed to software that acts.
• Risk, audit and compliance leaders who must question agent behaviour and records.
• Operations leaders whose teams will work alongside agents.
• Finance leaders who test the cost and benefit claims of agent projects.

Typical functions include operations, customer service, finance, procurement and public services.

Learning Outcomes

1. Participants will be able to distinguish AI agents from chatbots, scripts and assistants.
2. Participants will be able to evaluate business tasks for suitability to an agent.
3. Participants will be able to prioritise agent use cases by value, effort and risk.
4. Participants will be able to diagnose failure modes such as wrong actions, data leakage and drift.
5. Participants will be able to construct oversight rules, approval points and escalation routes.
6. Participants will be able to justify an adoption brief to an executive committee.

Programme Structure

Day 1: AI Agents and Agentic AI Foundations

What makes software an agent, and what does agentic AI add to earlier automation?

• Chatbot, Script and Agent Comparison
• Autonomy Level Scale
• Goal, Tool and Memory Model
• Plain-Language Glossary
• Vendor Claim Reading Sheet

Day 2: Agent Anatomy and Business Use Cases

Which tasks in a function suit an agent, and which do not?

• Task Decomposition Map
• Tool Access Inventory
• Use Case Long List
• Suitability Test Questions
• Cross-Sector Example Review

Day 3: Risks, Limits and Failure Modes

How do agents fail, and how is the harm sized?

• Failure Mode Catalogue
• Wrong Action Impact Scale
• Data Exposure Check
• Prompt Manipulation Scenario
• Risk Register Entry

Day 4: Oversight, Guardrails and Governance

Who approves, monitors and stops an agent?

• Approval Point Design
• Permission and Spending Limit Table
• Monitoring and Logging Plan
• Escalation Route Sheet
• Accountability Assignment Grid

Day 5: Applying the Tools to One Business Area

Do the vocabulary, screening, risk and oversight tools support one adoption brief?

• Applying the Session 1 Autonomy Level Scale
• Using the Session 2 Suitability Test Questions
• Testing the Session 3 Risk Register Entry
• Checking the Session 4 Approval Point Design
• Completing the AI Agent Adoption Brief

Methodology

The programme uses a structured exercise method built on anonymised agent scenarios from service, finance and operations settings. Participants work in peer groups on a shared business area, challenge each other's autonomy ratings and receive written facilitator feedback on each exercise set before turning to a function of their own.

• Suggested activity: Rating five vendor demonstrations on the autonomy scale and defending the ratings.
• Suggested activity: Tracing how an agent with payment access could cause a wrong action.
• Suggested activity: Drafting approval rules for an agent that drafts customer replies.

Capstone Output

Each participant completes an AI agent adoption brief for one business area. It holds a screened use case list, an autonomy rating, a risk register extract, an oversight and approval design, and a staged next-step plan. The brief is used at work to brief an executive committee, question vendors with a shared vocabulary and decide which first pilot to sponsor.

Frequently Asked Questions

Is AI agents training suitable for managers without a technical background?

Yes. AI agents training assumes senior responsibility for a function and builds the vocabulary step by step, without coding or advanced mathematics.

Which standards do AI agents rest on in this programme?

AI agents are examined through the NIST AI Risk Management Framework for risk work and ISO/IEC 42001 for management system policy, roles and records.

How is the brief from AI agents training used at work?

You use the AI agent adoption brief to brief an executive committee, question vendors and decide which first pilot to sponsor.

How does AI agents training differ from agent workflow building training?

AI agents training centres on concepts, screening, risks and oversight for managers, while workflow building training centres on designing and testing agents step by step.