Agentic AI systems go beyond traditional generative AI by allowing software agents to plan, make decisions, use tools, interact with external systems, and complete multi-step tasks with greater autonomy. This course provides a structured introduction to the design, development, application, testing, and optimization of effective agentic systems.
The course begins with the foundations of agentic AI and explains how agentic systems differ from standard prompt-based generative AI applications. Learners will explore the characteristics of autonomous agents, including goal orientation, reasoning, planning, memory, decision-making, tool use, and interaction with changing environments.
The course then examines the main building blocks of agentic systems. Learners will understand how language models, instructions, memory components, tools, APIs, data sources, workflows, and feedback mechanisms work together to create a functional agent. Attention is also given to the relationships between individual agents, users, and external systems.
Learners will explore how to design effective agentic workflows for complex tasks. This includes breaking objectives into smaller actions, defining decision points, organizing task sequences, handling dependencies, and creating reliable paths for agents to follow. The course explains how structured workflows can improve consistency, efficiency, and control while still allowing agents to operate autonomously.
The development section focuses on creating autonomous agents that can interpret goals, select appropriate actions, use available tools, evaluate results, and adjust their behavior. Learners will examine practical considerations for defining agent responsibilities, setting boundaries, managing context, and reducing unnecessary or incorrect actions.
The course introduces common tools and frameworks used to build agentic AI solutions. Learners will understand how these technologies support model integration, tool calling, orchestration, memory, workflow management, and communication between agents. The focus is on selecting tools based on project requirements rather than relying on a single framework.
Practical applications of agentic systems are explored across business operations, customer support, research, data processing, workflow automation, content management, and decision support. Learners will examine how autonomous agents can improve productivity while recognizing the situations in which human review and approval remain necessary.
The final sections cover testing, monitoring, and optimization. Learners will discover how to evaluate agent accuracy, reliability, task completion, tool usage, response quality, and operational performance. The course also addresses common problems such as hallucinations, repeated actions, poor planning, tool failures, excessive autonomy, and unexpected outputs.
By the end of the course, learners will understand the full lifecycle of an agentic AI system from selecting the appropriate use case and designing its architecture to developing workflows, integrating tools, testing performance, monitoring behavior, and continuously improving the system.
