About This Course
Learn how to design, build and evaluate AI agents that can support real business workflows. This hands-on course helps professionals identify the right opportunities for AI agents, create controlled workflows with human oversight, build or specify a simple agent, test for risks and determine whether an agent is ready to launch and scale.
Overview
The AI spotlight has moved from individual tools and prompting to agents that can interpret a goal, use information, choose permissible actions, and complete sections of a workflow to make work more efficient. The opportunity is substantial, but an agent that can act can also create new questions about authority, data access, licenses, cost, quality, accountability, and unintended consequences.
This one-day course gives business professionals a practical way to move from an idea to a controlled, pilot session during which an agent is built. Participants learn the difference between conventional automation and agentic work, identify valuable use cases, design a workflow with human checkpoints, build or specify a simple agent, test failure scenarios, and prepare a responsible launch plan. The course does not assume that every process needs an agent, and it does not treat deployment as a purely technical decision.
Who Should Attend
- Managers and practitioners who need to identify, sponsor, design, prototype, evaluate, or govern AI agents across multiple functions.
How You Will Benefit
- Define key AI agent concepts and terms
- Differentiate AI agents from AI assistants and conventional automation
- Identify business opportunities appropriate for AI agents
- Design an agentic workflow with clear roles, boundaries, and human oversight
- Build or specify a simple no-code or low-code AI agent
- Test an AI agent for reliability, risk, and unintended consequences
- Develop a governed pilot with meaningful business measures
- Create your AI agent action plan
What You Will Cover
- Define key AI agent concepts and terms
- Differentiate AI agents from AI assistants and conventional automation
- Identify business opportunities appropriate for AI agents
- Design an agentic workflow with clear roles, boundaries, and human oversight
- Build or specify a simple no-code or low-code AI agent
- Test an AI agent for reliability, risk, and unintended consequences
- Develop a governed pilot with meaningful business measures
- Create your AI agent action plan
Outline
Module 1: Select the Right Business Opportunity
- Recognize business processes where an AI agent could improve productivity, service, or operational efficiency
- Differentiate agentic workflows from conventional automation and AI-assisted tasks
- Evaluate potential opportunities based on business value, feasibility, workflow variability, risk, and cost
- Identify licensing, data, technology, and resource requirements that could affect the business case
- Participant output: Opportunity brief, baseline metrics, and license feasibility check
Module 2: Design the Agent’s Job and Boundaries
- Map an agentic workflow using inputs, directions, actions, outputs, exceptions, and escalations
- Define the agent’s role, users, data sources, permissible tools, and completion criteria
- Determine where human review, approval, or intervention is required
- Establish appropriate permissions, action limits, and stop conditions
- Participant output: Agent workflow canvas and authority map
Module 3: Build or Configure a Safe Prototype
- Compare no-code and low-code agent-building options based on business and technology requirements
- Identify the licenses, connectors, permissions, authentication, and usage costs required by the selected platform
- Configure the agent’s instructions, knowledge, examples, tools, actions, and output requirements
- Build a simple prototype or complete an equivalent build-ready agent specification
- Participant output: Working prototype or build-ready specification
Module 4: Test Reliability and Unintended Consequences
- Develop normal, edge, exception, adversarial, and misuse test scenarios
- Recognize common agent risks, including hallucinations, prompt injection, privacy exposure, bias, unauthorized actions, and cost escalation
- Apply controls such as human approval, least-privilege access, rate limits, spending limits, logging, rollback, and a kill switch
- Use test results to make a go, revise, restrict, or no-go recommendation
- Participant output: Test evidence, issue log, and go or no-go recommendation
Module 5: Launch, Measure, Govern, and Scale
- Develop a controlled pilot with defined business ownership, technical support, user training, and incident response
- Select KPIs that measure business value, quality, adoption, cost, exceptions, overrides, and user trust
- Identify the governance practices needed to monitor performance, manage change, and maintain accountability
- Determine whether to scale, revise, restrict, pause, or retire an AI agent
- Participant output: Pilot charter and KPI scorecard
This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.
Upon registration, the instructor will send along additional information about how to log-on and participate in the class.
