Level: Beginner | Format: 1-day Instructor-led
Course Overview
This course is designed to transition professionals from basic AI usage to Agentic Workflow Engineering. Instead of using AI as a simple chatbot for writing and brainstorming, you will learn to build autonomous agents that can plan, use tools, and execute complex business processes. By the end of this program, you will move from “talking to AI” to “managing a digital workforce.”
Key Course Takeaways
- Delegation Mastery: You will learn to stop “chatting” and start “assigning,” freeing up hours of your work week.
- Operational Awareness: You’ll understand the “why” behind AI failures, making you the person who can fix them rather than just complain about them.
- Strategic Advantage: You’ll be among the first in your organization to move beyond basic ChatGPT usage into building custom automated workflows.
Professional Skills Gained
By completing this course, you will gain the specialized ability to transition from a passive AI user to a high-level AI Orchestrator. You will master Outcome-Based Engineering, learning how to define objective goals that allow an AI to navigate ambiguity and execute complex workflows without constant human micro-management. This shift in expertise allows you to conduct Feasibility Analyses on any business process, accurately identifying which high-impact tasks are ready for automation and which require human nuance, effectively making you the bridge between technical AI capabilities and practical business operations.
Furthermore, you will develop a robust technical foundation in Persona Architecture and No-Code Integration, enabling you to build custom digital assistants that “speak” to your existing software stack, such as Salesforce, Slack, or Google Drive. Beyond simple automation, you will acquire critical skills in Risk Mitigation and Context Management, ensuring that the agents you deploy operate within strict ethical and corporate guardrails. By the end of the course, you will possess the “Agentic Mindset” required to lead digital transformation initiatives, turning AI from a simple writing tool into a reliable, proactive partner that scales your professional impact.
Module 1: The Agentic Mindset
Description: This module establishes the foundational difference between “Generative AI” (writing poems and emails) and “Agentic AI” (completing workflows). We explore the concept of Autonomy and how to transition from giving micro-instructions to defining high-level goals.
- Key Topics: The transition from Chatbot to Agent; The OODA Loop (Observe, Orient, Decide, Act) in AI; Identifying “Agent-ready” tasks.
- Real-World Skills: * Outcome-Based Engineering: Ability to define clear, measurable goals for AI that don’t require manual step-by-step guidance.
- Feasibility Analysis: Identifying which business processes are ripe for agentic automation versus which should remain manual.
Module 2: Anatomy of an Agent (The Four Pillars)
Description: Every agent, no matter how complex, relies on four core components. This module breaks down how these pillars interact to create “intelligence” and professional-grade reliability.
- Pillar 1: Profile: Defining the agent’s role (e.g., “You are a Senior Tax Auditor”).
- Pillar 2: Planning: How agents break a big goal into smaller, logical tasks.
- Pillar 3: Memory: The difference between short-term “chat” context and long-term “vector” knowledge.
- Pillar 4: Tools: Connecting the agent to the outside world (Web Search, Email, Excel).
- Real-World Skills: * Persona Architecture: Crafting robust system instructions that keep an agent focused on its specific domain.
- Context Management: Managing “token memory” to ensure the AI doesn’t lose track of the mission during long tasks.
Module 3: Tool Integration & “Action” Loops
Description: An agent without tools is just a dreamer. This module focuses on how to give AI “hands.” We demonstrate how agents interact with APIs and software without the user writing a single line of code.
- Key Topics: Introduction to Function Calling; Connecting to Zapier/Make; Setting up “Search” and “Calculator” tools.
- Real-World Skills: * No-Code Integration: Connecting AI agents to everyday business software (Slack, Google Drive, Salesforce).
- Troubleshooting Tool Failures: Learning how to read an agent’s “thought trace” to see why a tool didn’t return the expected result.
Module 4: The “Human-in-the-Loop” (HITL) Framework
Description: Total autonomy is often dangerous in a professional setting. This module teaches the essential safety protocols for ensuring agents don’t “hallucinate” an expensive mistake.
- Key Topics: Designing Checkpoints; The “Ask-Before-Doing” prompt pattern; Setting boundaries and guardrails.
- Real-World Skills: * Risk Mitigation: Designing workflows where the agent performs 90% of the work but pauses for human approval before high-impact actions.
- Error Correction: Learning to provide feedback during a task so the agent can pivot and finish correctly.
Module 5: Collaborative Workshop: Building Your Assistant
Description: A hands-on capstone where participants build a functional agent using a visual builder (like OpenAI’s GPTs or a similar low-code interface).
- The Project: Build a “Market Intelligence Agent” that can research a competitor, summarize their recent news, and draft a response strategy.
- Real-World Skills: * Rapid Prototyping: Moving from a business problem to a functional AI agent prototype in under 30 minutes.
- Iterative Testing: Using a “Trial and Error” approach to refine agent behavior based on real-world outputs.
Demonstrations
- The Goal-Oriented Assistant
- Description: An agent takes the goal “Plan a trip to Tokyo” and creates a structured task list instead of just writing a paragraph.
- Benefit: Project Scoping. Reduces the mental load of project planning by identifying dependencies automatically.
- Web-Browsing Researcher
- Description: An agent that researches 5 different competitors and creates a structured comparison table.
- Benefit: Market Intelligence. Saves hours of manual tab-switching and data entry for competitive analysis.
- PDF Intelligence Agent
- Description: Uploading a 50-page manual and having the agent “act” as the support tech for that product.
- Benefit: Knowledge Management. Instant access to specific information within massive corporate documentation silos.
- The Self-Correcting Math Agent
- Description: An agent that gets a math problem wrong, catches its own error through reflection, and fixes it.
- Benefit: Reliability. Increases trust in AI outputs by implementing “self-critique” steps to catch hallucinations.
- The Multi-Channel Communicator
- Description: An agent that takes a single memo and drafts a LinkedIn post, a Slack update, and an Email.
- Benefit: Omni-channel Marketing. Ensures consistent messaging across platforms with zero manual reformatting.
Demonstrations (continued)
- Calendar Manager
- Description: An agent that “looks” at a calendar tool to find open slots and propose meeting times.
- Benefit: Executive Support. Automates the “scheduling dance,” freeing up administrative time for higher-value tasks.
- The Prompt Refiner
- Description: An agent designed specifically to help you write better, more technical prompts for other agents.
- Benefit: Quality Assurance. Dramatically improves the performance and accuracy of every other AI tool in your stack.
- The Compliance Guard
- Description: A demo of an agent refusing to perform a task because it violates a preset company policy or safety rule.
- Benefit: Governance. Prevents accidental data leaks or policy violations by embedding rules into the AI’s logic.
- Data Extraction Agent
- Description: Converting a photo of a receipt or invoice into a structured, digital expense report.
- Benefit: Operational Efficiency. Eliminates manual data entry for accounting and logistics workflows.
- The “Wait for Approval” Loop
- Description: An agent that prepares an email but sends a notification to the user for a “Yes/No” before hitting send.
- Benefit: Trust & Control. Allows for automation of high-stakes tasks while maintaining 100% human accountability.