You’ll explore the types, functions, and real-world applications of AI agents across industries; you’ll learn to build and implement AI agents, evaluate their performance, and address limitations. You’ll also analyze the ethical and societal implications of AI, developing guidelines for responsible innovation and deployment.

Learn to prompt AI across text, image, and data, and combine those modalities into a single working output. Practice cross-modal prompting in personal accounts, build a multimodal workflow, and evaluate outputs for quality across modalities to produce richer, integrated deliverables than text-only prompting allows.

Learn to connect prompts and AI tools into reliable multi-step workflows, coordinate multi-turn agent orchestration, and place quality checkpoints between stages. Build a prompt chain across multiple tools, run an orchestration sequence, and validate outputs at each stage to turn one-off prompting into repeatable, quality-controlled automation.

Learn hands-on introduction to the world of AI agents, perfect for aspiring AI architects and innovators. Emphasizing practical application and fast-track learning, you’ll immerse yourself in coding while acquiring scalable skills for future projects.

Learn to supervise autonomous AI agents responsibly by determining necessary intervention points, selecting oversight strategies, and delegating tasks safely. Build a risk-scored intervention matrix, define a supervision protocol, and execute a structured delegation with an oversight report to maintain human accountability during automated operations.

In this project, you configure and test a working multi-agent workflow in draft/paused mode and pair it with a supervision protocol that governs how it runs. You will configure the workflow, prepare it with success criteria, and document the intervention points, boundaries, and escalation triggers that keep a human accountable. This matters because employers value people who can both set up automation and run it responsibly.