Agentic AI for Business Training

This course is designed for business leaders, consultants, product and project managers, and other decision makers who are interested in unlocking the power of agentic AI to enhance business processes. It is also a great starting point for technical practitioners who have been tasked with implementing agentic solutions in the organization.

In this course, you will responsibly plan agentic AI initiatives that deliver measurable business value. You will:

• Identify the fundamentals of agentic AI
• Determine whether AI agents are suitable for a given business problem
• Design business-driven agentic solutions at a high level, without going deep into the technical details
• Manage the risks of agentic AI
• Adopt agentic AI into the organization
• Prepare to execute an agentic strategy so implementation can proceed

Lesson 1: Agentic AI Fundamentals

Establish the conceptual foundation you need to speak confidently about agentic AI with any stakeholder — technical or not.

• Identify Agentic AI Concepts — Trace the evolution of business automation from rule-based scripts through machine learning to today's agentic systems. Understand how LLMs power generative AI, how prompting works, and what makes agentic AI distinct: it doesn't just respond to prompts — it autonomously pursues goals, adapts to changing conditions, and takes multi-step action in the real world.
• Identify Core Components of an Agent Architecture — Explore the Brain-Memory-Tools (BMT) framework that underlies every agentic system, the "conscience" layer of guardrails and policies that governs what an agent is allowed to do, and the Sense-Think-Act (STA) loop that drives autonomous behavior. Learn to read and create agentic architecture diagrams that communicate system design to any audience.

Lesson 2: Determining the Suitability of AI Agents

Not every business problem calls for an AI agent. Develop a structured approach to evaluating where agents add genuine value — and where they don't.

• Analyze Business Processes for Automation Potential — Learn the characteristics that make a task agent-suitable: high variability, multi-step reasoning, context-dependent decisions, and cross-system tool use. Apply Business Process Analysis (BPA) to map workflows, identify decision points, and surface exceptions. Understand which tasks should remain human-led — including high-stakes, irreversible, or legally regulated decisions — and how to gather stakeholder input that reveals risks no documentation can capture.
• Assess the Value and Feasibility of Agentic AI — Evaluate the market forces driving the "agentic shift" and weigh efficiency gains against real complexity costs: architectural overhead, non-deterministic behavior, governance requirements, and ongoing monitoring. Assess data availability, tool constraints, talent gaps, and LLM token economics — including how to estimate cloud vs. local model costs and calculate a realistic ROI for an agentic initiative.
• Identify Real-World Use Cases — Survey proven agentic applications across operations, IT and cybersecurity, HR, finance, and customer support. Examples include predictive maintenance coordination, candidate screening and onboarding, invoice processing, and proactive service-disruption notification — providing concrete inspiration for your own organization's opportunities.

Lesson 3: Designing Agentic Solutions at a High Level

Make the architectural decisions that determine whether your agentic solution is effective, maintainable, and appropriately scoped — without needing to write a line of code.

• Select Appropriate Agent Design Patterns — Choose the right modality (text, structured data, vision, system interaction, or multimodal) for your use case and understand the tradeoffs each introduces. Decide between one-shot and iterative design patterns based on task complexity. Evaluate single-agent vs. multi-agent architectures, and determine when fine tuning, Retrieval-Augmented Generation (RAG), action-taking tools, or orchestration layers are the right fit.
• Identify Memory and Context Strategies — Understand that memory in agentic AI is an intentional design choice, not an automatic feature. Compare short-term and long-term memory strategies across dimensions of persistence, governance risk, and business value. Manage context windows effectively, address information decay before it degrades outputs, and find the right balance between recall and cost for your specific use case.

Lesson 4: Managing Agentic AI Risks

Agentic AI is not "set and forget." Understand the full risk landscape — technical and organizational — and learn how to design systems that fail safely.

• Identify the Technical Risks of Agentic AI — Recognize confabulation (hallucination), misinformation, and training data limitations for what they are and how they manifest in production. Understand the dangers of vibe coding, agent overreach, tool misuse, and prompt injection attacks — including how a malicious document retrieved via RAG can redirect an agent's behavior entirely. Learn to design failure modes that degrade gracefully rather than cascade into larger problems.
• Identify the Business Risks of Agentic AI — Address the governance challenges that agentic AI introduces at the organizational level: workforce impact, operational disruption, data quality and control, brand reputation, intellectual property and copyright exposure, and legal and regulatory considerations including the EU AI Act. Examine the five core ethical principles — privacy, accountability, transparency, fairness, and safety — and how agentic systems can violate each.
• Design Safety and Oversight Mechanisms — Conduct a structured risk assessment to prioritize where oversight matters most. Apply Human-in-the-Loop (HITL) patterns — pre-approval, post-action review, uncertainty escalation, and human-as-trainer — with an honest view of each approach's tradeoffs. Define safe autonomy boundaries across action, data access, time, and scope dimensions. Build escalation triggers and auditability measures that keep agents accountable over time.

Lesson 5: Adopting Agentic AI into the Organization

Turn strategic intent into an executable plan — covering resources, governance, change management, and organizational readiness.

• Plan Agent Initiatives — Define the people, data, and infrastructure requirements for an agentic project. Navigate model selection, token-cost estimation, and the build-vs.-buy decision. Establish KPIs and ROI benchmarks that give leadership a clear picture of expected value before development begins.
• Prepare the Organization for Agent Adoption — Build the governance frameworks and change-management processes that allow agentic AI to be introduced without disrupting operations. Address workforce impact honestly, develop policies for responsible deployment, and assess organizational readiness so that adoption proceeds at a pace the business can sustain.

Lesson 6: Preparing to Execute Agentic Strategy

Translate your analysis into a prioritized, communicable action plan that earns stakeholder confidence and sets implementation up for success.

• Prioritize Agent Workflows and Applications — Map short-term quick wins against longer-term strategic opportunities. Identify dependencies, plan phased rollouts, and produce a scored use-case list that guides where development resources should go first.
• Communicate Agent Strategy to Stakeholders — Tailor your message for executives, technical practitioners, and end users. Align on common objectives, set realistic expectations about timelines and outcomes, and build the organizational buy-in needed to move from planning to execution.

• A foundational knowledge of business processes and general business concepts
• A general awareness of AI
• At least a basic understanding of information technology resources and systems, including networks, computers, and other electronic devices used in the enterprise