Per participant
- 15 - 19 Nov, 2026
- English
- Virtual Learning
Agentic AI for Business Transformation - Virtual Learning
Why Attend
Agentic AI represents a significant advancement in artificial intelligence, enabling systems to autonomously plan, reason and execute tasks across complex workflows. Unlike traditional AI applications, agentic systems operate with a higher degree of independence, interacting with tools, data sources and other agents to achieve defined objectives.
This course provides a structured and practical approach to implementing agentic AI within organisations. It examines the architecture, design, deployment and governance of AI agents, enabling participants to evaluate opportunities, develop implementation strategies and manage associated risks in enterprise environments.
This course is also offered in face-to-face courses, click on the course below.
Overview
Course Methodology
This course combines interactive presentations, group discussions, case studies, progressive hands-on laboratories and a capstone project. Participants progressively build and evaluate increasingly capable AI agents while developing implementation strategies tailored to their organisational context.
Course Objectives
By the end of the course, participants will be able to:
- Evaluate the capabilities and limitations of agentic AI within organisational contexts.
- Analyse business processes to identify suitable applications for AI agents.
- Design agentic AI architectures aligned with operational requirements.
- Develop structured workflows for autonomous and multi-agent systems.
- Assess risks, governance requirements and ethical considerations in agent deployment.
- Formulate implementation roadmaps for scaling agentic AI solutions.
Target Audience
Professionals involved in digital transformation, innovation and operational excellence, including business leaders, strategy professionals, IT managers, data and AI specialists, and consultants responsible for evaluating and implementing emerging technologies.
Target Competencies
- Agentic AI Strategy
- AI Agent Architecture Design
- Multi-Agent Workflow Design
- AI Governance and Risk Management
Course Outline
Day 1 – The Evolution and Foundations of Agentic AI
- The Development of Intelligent Systems
- From rule-based automation to generative AI
- Limitations of traditional AI and workflow automation
- The emergence of autonomous and agent-based systems
- Core Principles of Agentic AI
- Distinction between assistants, copilots and agents
- Autonomy, reasoning and decision-making capabilities
- Context engineering and system prompt design
- Components of Agentic AI Architectures
- Large language models and reasoning engines
- Memory structures and contextual awareness
- Tool integration and external system interaction
- Agent Design Patterns
- ReAct and reasoning-action frameworks
- Planning and task decomposition models
- Multi-agent collaboration structures
- Organizational Use Cases
- Knowledge management and research automation
- Customer interaction and service delivery
- Process automation and decision support
- Progressive Hands-on Lab
- Build a simple AI research assistant
- Define objectives and system prompts
- Configure behaviour and operating context
- Test basic reasoning and execution capabilities
Day 2 – Designing Agentic AI Solutions
- Identification of High-Value Opportunities
- Suitability of business processes for agentic AI
- Complexity, variability and decision intensity
- Value creation and ROI considerations
- Structuring Agent Workflows
- Task decomposition and sequencing
- Roles and responsibilities within agent systems
- Feedback loops and adaptive learning cycles
- Agent Frameworks and Platforms
- Overview of leading agent development frameworks
- Orchestration tools and execution environments
- Enterprise AI platforms and integration layers
- Tooling and System Integration
- API connectivity and external tool usage
- Integration with CRM and ERP
- Data access and knowledge retrieval mechanisms
- Memory and Context Management
- Short-term and persistent memory models
- Vector databases and semantic retrieval
- Context optimisation for decision accuracy
- Progressive Hands-on Lab
- Enhance the research assistant
- Integrate tools and enterprise knowledge
- Add memory and contextual awareness
- Improve reasoning
Day 3 – Development and Deployment of AI Agents
- Agent Development Lifecycle
- Goal definition
- Tool configuration and interaction logic
- Execution flow and orchestration
- Multi-Agent Systems
- Role-based agent architectures
- Hierarchical and collaborative agent models
- Coordination and communication between agents
- Integration into Business Environments
- Embedding agents within operational systems
- Workflow automation across departments
- Human-agent interaction models
- Enterprise AI Evaluation & Readiness
- Why AI projects fail
- Common implementation pitfalls
- AI evaluation frameworks
- Production readiness assessment
- Measuring business value and success criteria
- Testing and Validation
- Monitoring agent outputs and decisions
- Handling uncertainty and failure scenarios
- Performance evaluation and refinement
- Optimization and Scalability
- Prompt and workflow optimisation
- Resource utilisation and cost management
- Scalability considerations in enterprise settings
- Progressive Hands-on Lab
- Develop a multi-agent business workflow
- Evaluate business readiness
- Refine solution
Day 4 – Governance, Risk and Responsible AI
- Risk Landscape of Agentic AI
- Decision accuracy and hallucination risks
- Security vulnerabilities and misuse scenarios
- Operational and reputational risks
- Governance Frameworks
- Human-in-the-loop and oversight mechanisms
- Policy development and control structures
- Monitoring, auditing and accountability
- Regulatory and Compliance Considerations
- Data protection and privacy requirements
- Emerging AI regulations and standards
- Industry-specific compliance considerations
- Ethical and Responsible AI
- Transparency and explainability
- Bias and fairness in autonomous systems
- Defining acceptable boundaries for agent behaviour
- Organizational Readiness
- Change management and workforce implications
- Capability development and AI literacy
- Aligning AI initiatives with business strategy
- Progressive Hands-on Lab
- Assess governance requirements
- Identify risks
- Evaluate organisational readiness
Day 5 – Scaling Agentic AI Across the Enterprise
- Enterprise Architectures for Agentic AI
- Centralised vs decentralised agent ecosystems
- Orchestration layers and control frameworks
- Integration with digital transformation initiatives
- Operationalization of AI Agents
- Deployment pipelines and lifecycle management
- Monitoring performance and continuous improvement
- Managing agent portfolios at scale
- Strategic Implementation Planning
- Identifying priority use cases
- Phased implementation approaches
- Stakeholder alignment and governance structures
- The Future of Agentic AI
- Autonomous enterprise models
- Multi-agent ecosystems and digital workforces
- Emerging trends and technological advancements
- Capstone Exercise
- Design, evaluation and implementation roadmap for an enterprise agentic AI solution tailored to participants' organisations.
Schedule & Fees
Face-to-Face Courses
This course is also offered in face-to-face courses, click on the course below.
Course Contact
Contact me if you have any questions.
I speak English & Arabic!
© 2026 Meirc Training & Consulting. All rights reserved.