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 Duration 21 hours

Course Outline

Foundations of Agentic AI in Operational Efficiency

  • Defining agentic AI and its strategic significance for automation
  • Reviewing key tools and frameworks for developing intelligent agents
  • Enterprise applications: customer support, logistics, and marketing

Uncovering Automation Potential

  • Analyzing existing workflows and identifying bottlenecks
  • Assessing feasibility and return on investment for AI-driven tasks
  • Establishing key performance indicators and integration prerequisites

Architecting Agentic Processes

  • Structuring agents for specific tasks and broader orchestration
  • Crafting prompts and logical structures for automation agents
  • Incorporating decision logic and exception management

Connecting Agents to Corporate Systems

  • Linking AI agents with CRMs, ERPs, and communication platforms
  • Leveraging Zapier, Make, or Power Automate for workflow orchestration
  • Executing API integrations using Python

Real-World Applications

  • Automating customer interactions and analyzing sentiment
  • Forecasting supply chain demands and coordinating with vendors
  • Refining marketing campaigns through AI-generated insights

Oversight, Security, and Monitoring

  • Controlling access rights and managing data sensitivity
  • Configuring dashboards and alert systems for monitoring
  • Reviewing and auditing automated decisions for compliance

Practical Exercise: Developing a Unified AI Workflow

  • Selecting a target process for automation
  • Designing and deploying the AI agent
  • Conducting tests, evaluations, and refinements

Conclusion and Future Directions

Requirements

  • A solid grasp of operational processes and workflow automation principles
  • Proficiency in Python or API-driven system connections
  • Prior experience with productivity suites or automation software

Intended Participants

  • Product managers looking to uncover new avenues for automation
  • Automation specialists tasked with deploying AI-centric workflows
  • Business analysts focused on designing data-driven operational frameworks

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