Agentic AI for Business Training

This course is designed for business leaders, managers, and decision-makers evaluating AI agent adoption, rather than developers or technical implementation teams.

Upon successful completion of this course, students will be able to:

• Explain what agentic AI actually is and how it's different from other generative AI tools, so internal conversations start from a shared, accurate baseline
• Identify where AI agents are likely to create real value in their own organization's operations, instead of adopting the technology just because competitors are
• Select an appropriate high-level design approach for a proposed agent, including how much it should remember and how much context it needs to work well
• Evaluate the technical and business risks of a given agentic AI deployment, and design the guardrails and oversight needed to deploy it safely
• Apply a structured framework for planning and prioritizing agentic AI initiatives, including resourcing, cost, and success metrics
• Build a plan for rolling out agentic AI across the organization and communicating that strategy to stakeholders with different concerns

Understanding Agentic AI

What defines an "agent" as distinct from other generative AI tools · The business drivers behind the current shift toward agentic systems · Core components of an agent's architecture

Hands-on exercise: Students watch a short simulation of a fictional operations-support agent handling incoming issue reports and recommending fixes, then map its capabilities onto an architecture diagram to understand how its components fit together.

Evaluating Opportunities

Analyzing business processes for automation potential · Assessing the value and feasibility of a proposed agentic solution · Identifying real-world use cases across different business functions

Hands-on exercise: Students analyze a set of business processes at a fictional training company to recommend candidates for automation, walk through the steps of a proper business process analysis, assess the value and feasibility of agentic AI for a regional retailer expanding into e-commerce, and identify concrete, domain-specific use cases across manufacturing, financial services, IT support, and customer support.

Designing Agentic Solutions at a High Level

Selecting an appropriate agent design pattern for a given use case · Identifying memory and context strategies, and the tradeoffs between recall, cost, and reliability

Hands-on exercise: Students recommend design patterns for several proposed agent use cases within an organization, then work through a healthcare provider's memory and context strategy decisions — what an agent should remember, for how long, and how much context to include in each interaction.

Managing Risk and Responsibility

Identifying the technical risks of agentic AI · Identifying the business risks of agentic AI · Designing safety and oversight mechanisms

Hands-on exercise: Students analyze a set of real incidents from a company's early agentic AI deployments to identify root causes — including a procurement agent that placed excessive orders due to poorly defined thresholds — and design the guardrails and human-oversight checkpoints a healthcare provider should put in place before scaling its own deployment.

Building an Adoption Plan

A structured framework for evaluating and prioritizing agentic AI initiatives · Planning resource requirements and model selection · Identifying cost drivers and success metrics · Preparing the organization for agent adoption

Hands-on exercise: Students plan the resource requirements and model-selection criteria for a logistics company's first agentic AI initiative, define cost drivers and target metrics for its proof of concept, and identify the organizational barriers to adoption the company will need to address before a broader rollout.

Preparing to Execute Agentic Strategy

Prioritizing agent workflows and applications for rollout · Communicating agent strategy to stakeholders with different concerns

Hands-on exercise: Students prioritize a slate of proposed agent workflows for a manufacturing and distribution company's phased rollout, then build a stakeholder communication plan that speaks separately to executives focused on ROI, practitioners worried about workflow disruption, and customers concerned about service quality and trust.

No technical background or prior AI experience is required. This course assumes a general business or management background and an interest in evaluating AI adoption strategically.

Agentic AI is moving quickly, and much of the freely available content on the topic is either overly promotional or overly technical for a business decision-maker's actual needs. The value of this course is largely in the discussion it enables: working through how the concepts apply to your organization's specific operations, with an instructor available to answer questions a generic video course cannot anticipate.

Students also retain up to one year of access to a recording of the session and six months of post-class instructor support, which is particularly useful when bringing this framework back to internal stakeholders and needing to revisit specific points.