Data Science Academy

Agentic AI Engineering with Claude Code

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Claude AI · Online Course

Agentic AI Engineering with Claude Code Reviews & Product Details

The Agentic AI Engineering with Claude Code course by the Data Science Academy is meticulously designed for software engineers, AI developers, and tech innovators who want to move beyond simple prompt engineering into the realm of fully autonomous agentic workflows. As the industry shifts from static chat interfaces to proactive, goal-driven AI systems, understanding how to engineer agentic behavior is a critical modern skill. Throughout this comprehensive training, students will master the core architecture of autonomous systems, focusing on how to implement tool calling to connect Claude with external APIs, relational databases, and enterprise systems. The curriculum dives deep into building robust state management, integrating both short-term and long-term memory architectures so agents retain crucial context across complex tasks. Additionally, learners will discover how to orchestrate multi-agent systems using advanced supervisor-worker patterns and strategic task delegation. By exploring practical planning techniques, optimization strategies, and deployment best practices, participants will gain the hands-on expertise required to build resilient, cost-efficient, and highly scalable AI agents capable of solving real-world challenges independently.

Best For

Software engineers looking to transition from static LLM integration to autonomous agentic workflows AI developers interested in leveraging Claude Code for complex, multi-step problem solving Systems architects aiming to build scalable multi-agent systems and supervisor-worker patterns Technologists who need to implement persistent memory and database connectivity for AI applications

Not For

Complete beginners with no prior programming or basic AI development experience Non-technical stakeholders looking for a conceptual overview without hands-on coding Professionals seeking general prompt engineering advice rather than agentic architectural design Developers focused exclusively on fine-tuning models rather than building application-layer agent systems

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About the Creator

Muhammad Usman Mallick, representing the Data Science Academy, is an expert educator and technical practitioner specializing in cutting-edge artificial intelligence, generative models, and advanced automated workflows. With a deep commitment to demystifying complex technologies, Muhammad guides students through the practical aspects of modern AI development. His training programs focus heavily on immediate application, taking learners beyond theoretical machine learning concepts into hands-on implementation of technologies like Claude Code, Model Context Protocol (MCP) systems, OpenAI, and Anthropic APIs. Muhammad's teaching philosophy revolves around project-based execution. He designs his curriculum to ensure that engineers, developers, and tech enthusiasts can build concrete tools, including AI agents, intelligent systems, and autonomous operating workflows. By prioritizing clean code architecture, prompt engineering, and agentic AI integration, Data Science Academy courses bridge the gap between academic AI foundations and real-world software engineering. Under his mentorship, thousands of learners globally have mastered how to automate workflows, build smart systems, and orchestrate complex LLM operations to drive tangible business value.

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