Build AI Agents That Deliver Results

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.

• Separate useful agent opportunities from hype; avoid automating the wrong work
• Move from a business problem to a build-ready agent workflow in five steps
• Prototype a simple no-code or low-code agent, or complete an equivalent platform-neutral build specification
• Identify license, connector, API, data, identity and deployment requirements before committing to a platform
• Reduce unintended consequences through least-privilege access, human approval, test scenarios, monitoring and stop conditions
• Estimate value, define meaningful KPIs, gain stakeholder support and make a scale, revise or stop decision

• 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

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