Reliable Agentic AI: Production Performance and Monitoring

Moving AI agents from experimental prototypes to dependable production systems requires more than just prompt engineering. You need to ensure your autonomous agents are reliable, cost-effective, and perform consistently under real-world conditions. This text-based course guides you through the foundational concepts of Agentic AI, teaching you how to architect, monitor, and optimize agent workflows for maximum uptime and minimal errors. You will learn how to handle agent drift, manage token budgets, and establish automated evaluation pipelines. What you'll learn: 1. Understand the core architecture of autonomous AI agents and multi-agent systems. 2. Design robust fallback mechanisms to handle agent failures and infinite loops. 3. Implement real-time monitoring and logging for agent decisions and tool usage. 4. Apply automated evaluation frameworks to measure accuracy, latency, and cost. 5. Optimize token consumption and manage rate limits in production environments. 6. Configure retrieval-augmented generation patterns to ground agent responses. The course begins with essential terminology and the fundamentals of agentic behavior before moving into practical strategies for testing, debugging, and continuous optimization. You will progress through structured written explanations and conceptual walkthroughs designed to make your AI systems production-ready. This course is designed for software developers, data practitioners, and technology enthusiasts who want to build stable AI systems. It is highly beginner-friendly, requiring no prior machine learning experience, though basic programming concepts are helpful. Start reading today to build autonomous AI agents that you can confidently deploy to production.