The Advanced Tool Development and Integration course builds on foundational agent skills by focusing on how to create, customize, and integrate tools into intelligent agents. Learners begin by designing custom functions and APIs that extend agent capabilities beyond built-in options, using best practices for clarity, reliability, and safety.

The course then covers connecting agents to third-party services through secure authentication (including OAuth), enabling them to interact with external platforms such as CRMs, databases, and SaaS applications. Emphasis is placed on tool ecosystems, including Modular Component Protocol (MCP) standards for scalable interoperability. Learners also explore persistent memory and state management techniques, allowing agents to maintain continuity across sessions and tasks. Through guided coding activities, dialogues, and a capstone project, participants will design and deploy a Custom Analytics Agent that integrates multiple data sources, performs real-time analysis, and delivers actionable insights. By course end, learners will be able to engineer tools that empower agents with advanced functionality and seamless integrations.

The Multi-Agent Systems and Orchestration course teaches learners how to design and coordinate AI agents that work together as collaborative systems. Starting with the OpenAI Agents SDK, participants explore how to structure planner–executor architectures, enabling agents to break down complex tasks into coordinated subtasks.

The course emphasizes orchestration strategies such as multi-agent collaboration, workflow delegation, and state sharing across agents, supported by design principles for efficiency and reliability. Learners also examine observability and monitoring techniques to track agent decisions, as well as fault tolerance strategies to handle errors gracefully in production settings. Advanced modules introduce hybrid human–agent workflows, parallel execution patterns, and enterprise-level orchestration for scalability. Through hands-on labs and guided projects, learners will build an Automated Research Team, demonstrating how multiple agents can gather information, analyze data, and synthesize findings. By course completion, participants will have the skills to design multi-agent systems that deliver scalable, reliable, and coordinated AI-driven solutions.

The Production Deployment and Advanced Patterns course equips learners with the skills to transform AI agents into professional-grade applications ready for real-world use. Starting with user interface development, learners build interactive agent interfaces using Streamlit for rapid prototyping and Flask APIs for scalable integration across platforms. They then explore advanced UI patterns such as streaming responses, interactive visualizations, responsive design, and effective expectation management to create seamless agent experiences.

The course progresses to packaging and distributing agents through the GPT Store, emphasizing testing, optimization, deployment, and monitoring strategies for reliability and growth. Learners also master enterprise-grade integration, including role-based access control, audit logging, compliance documentation, and cost-performance management at scale. Finally, the course introduces DSPy, shifting from manual prompt engineering to programmatic, self-optimizing agent development with embedded ethical safeguards and continuous improvement pipelines. By the end, learners will have the skills to build secure, scalable, and future-proof AI systems.