Introduction

As organisations deploy agentic AI systems capable of initiating actions, making decisions, and interacting with other systems, accountability does not disappear — it becomes distributed. The most common governance failures in agentic AI environments arise not from technical malfunction, but from unclear ownership, fragmented decision authority, and poorly defined escalation when autonomous actions create adverse outcomes.

The Agentic AI Governance and Control training course addresses this accountability gap directly. It focuses on how organisations govern agentic AI in practice, clarifying decision rights, ownership boundaries, escalation thresholds, and evidence expectations. The emphasis is on defensible governance rather than system design, ensuring that AI-enabled decisions can withstand internal challenge, audit scrutiny, and regulatory review. Participants develop practical judgement and oversight discipline to manage agentic AI responsibly within real organisational constraints.

Key focus areas include:

Key Learning Outcomes

At the end of this Agentic AI Governance and Control training course, participants will be able to:

Training Methodology

This Agentic AI Governance and Control training course uses scenario-led facilitation supported by realistic governance cases, applied exercises, and structured reflection. Participants work through accountability mapping, escalation decisions, and oversight challenges drawn from real agentic AI use cases to ensure practical and defensible application.

Agentic AI Governance and Control

Who Should Attend?

This Agentic AI Governance and Control training course is ideal for:

  • Professionals accountable for AI-enabled processes or outcomes
  • Leaders approving or overseeing autonomous or semi-autonomous AI decisions
  • Governance, risk, compliance, and audit professionals
  • Technology risk and operational oversight managers
  • Senior managers responsible for escalation and accountability
  • Professionals required to justify AI-related decisions and controls

Course Outline

Day 1

  • What agentic AI means from a governance perspective
  • Where technical responsibility ends and governance accountability begins
  • How autonomy changes decision ownership and accountability
  • Common accountability failures in agentic AI deployments and their consequences
  • Building an Agentic AI accountability and escalation map for your role
Day 2

  • Understanding AI related risk for non-technical decision-makers
  • Operational, compliance, conduct and reputational risks from agentic systems
  • Applying risk appetite and tolerance to autonomous decision-making
  • Controls as management and governance responsibilities, not technical safeguards
  • Applying risk awareness to a live agentic AI governance scenario
Day 3

  • Decision-making when authority is partially delegated to AI systems
  • Trade-offs between autonomy, speed, oversight and escalation
  • Managing uncertainty, exceptions and opaque AI behaviour
  • Over-reliance on automated outputs and the myth of neutral systems
  • Decision workshop: justify, evidence and defend AI governance choices
Day 4

  • Escalation judgement for agentic AI behaviour and outcomes
  • Timing escalation correctly to avoid noise or exposure
  • Communicating AI risks clearly without technical complexity
  • Documentation standards for defensible AI governance and oversight
  • Escalation role-play: raising AI governance concerns professionally
Day 5

  • Embedding agentic AI governance into daily oversight routines
  • Oversight discipline for approvals, reviews and performance monitoring
  • Identifying and eliminating governance shortcuts that create exposure
  • Personal governance discipline for AI related decisions
  • Individual action plans and implementation commitments

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FAQs

This training course focuses on establishing accountability, governance, and control for autonomous AI systems operating with limited human intervention. 

No, the training course is designed for governance, risk, compliance, and management professionals rather than AI engineers. 

The training course clarifies ownership, decision authority, escalation thresholds, and documentation expectations for agentic AI decisions. 

Yes, the training course is particularly relevant where AI decisions are subject to governance, assurance, or regulatory scrutiny. 

Yes, by strengthening oversight discipline and escalation judgement, the training course reduces exposure caused by unclear accountability. 

Related Categories

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Corporate Governance
Artificial Intelligence (AI)

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Digital Transformation & Emerging Technology

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