Agentic Workflows

Introduces the foundational concepts of AI agents and agentic workflows, setting the stage for the course. It covers prerequisites, the course environment, and how to use the necessary API keys.

Explores what defines a modern AI agent, its core components (Persona, Knowledge, Tools, Interaction), and the different types of agents based on their LLM interaction model.

Design and visualize agentic workflows. Learn common agent types as building blocks for creating visual workflow diagrams.

Design and visualize agentic workflows. Learn common agent types as building blocks for creating visual workflow diagrams.

Covers the practical aspects of translating agentic workflow models into Python code. Students learn to structure agent logic, define agent classes, and orchestrate their interactions.

Introduces the Prompt Chaining pattern for breaking down complex tasks into a sequence of smaller, dependent steps. It covers strategies for task decomposition, validation, and context management.

Provides hands-on experience in implementing the Prompt Chaining pattern. Students build a multi-agent chain to solve a problem where information is passed sequentially.

Teaches the Routing pattern, which involves classifying incoming tasks and directing them to the most appropriate specialized agent or processing path.

Students implement a routing system where a router agent uses an LLM to classify a query and then dispatches it to the correct specialist agent, which may involve orchestrating sub-tasks.

Introduces the Parallelization pattern for executing multiple agent tasks concurrently. It covers strategies for task decomposition (sharding, aspect-based) and result aggregation.

Students implement a parallel workflow using Python's threading module, where multiple specialist agents analyze a document concurrently, and a synthesizer agent combines their findings.

Focuses on the Evaluator-Optimizer pattern, an iterative process of generation, critique, and refinement to improve output quality. It emphasizes clear evaluation criteria and actionable feedback.

Students build a two-agent system (a creator and a critic) that works in a loop. The creator generates a solution, and the critic provides feedback until the solution meets all constraints.

Introduces the advanced Orchestrator-Workers pattern, where a central agent dynamically plans, delegates, and synthesizes the work of multiple specialized worker agents.

Students implement a market analysis report generator where an Orchestrator agent creates a plan, assigns tasks to news, competitor, and trend analysis workers, and then synthesizes their findings.

Course review.