Explain when LLM agents outperform single-call LLMs and the capability, cost, and latency tradeoffs
Describe a LangChain agent as an LLM-driven reason-act-observe loop with model, tools, prompt, and runtime
Identify core building blocks including tool specs, memory/state, and callbacks for tracing
Trace the agent loop from user goal through tool execution and observation until a final answer
Compare common patterns: tool-calling, ReAct, router/specialist, and multi-agent roles
Apply a design checklist for tool descriptions, limits, human-in-the-loop, and logging
Mitigate pitfalls such as infinite loops, bad tool args, injected observations, and cost blowups
Who this course is for
Developers, ML engineers, and technical product builders who already know basic LLM prompting and want a practical introduction to LangChain-style agents. No prior agent framework experience required; familiarity with APIs and simple function schemas is helpful.
