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Learn to architect and test reliable LLM-powered autonomous agents that combine tool calling, memory, and planning capabilities for autonomous task performance. Explore the fundamental challenges of agent reliability that have hindered large-scale deployment and productionization, then discover practical solutions using LangGraph to design and build dependable agents supporting diverse self-corrective applications including RAG and code generation. Master comprehensive testing methodologies using LangSmith to evaluate both final agent responses and detailed tool use trajectories throughout the agent's decision-making process. Understand three critical testing loops essential for robust agent development: runtime testing for immediate feedback, pre-production validation to catch issues before deployment, and production monitoring for ongoing performance assessment. Gain hands-on insights into building controllable agents that can reliably execute complex workflows while maintaining transparency and debuggability in their operations.
