How LLM agents call tools in a loop, how to design tools they use well, and how to keep agents safe and reliable.
An agent is a language model that can take actions — searching, calling APIs, running code — and decide what to do next based on the results. This course explains the pattern behind every agent framework, then focuses on the parts that decide whether an agent works in practice: tool design, guardrails and evaluation.
The code is provider-neutral: every major model API supports tool use in a similar way, so check your provider's documentation for the exact names.
4 lessons · 1 hr 2 min
Workflows versus agents, and when each fits.
Tool definitions, tool calls and results, in a provider-neutral loop.
Names, descriptions, inputs and outputs that make agents reliable.
Limit what agents can do, keep people in the loop, and measure reliability.
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How LLMs work, how to write prompts that get reliable results, and how to check what comes back.
Turn text into vectors, build a semantic search index, and ground an LLM's answers in your own documents.
Build test sets, grade outputs with code, people and model-based graders, and catch regressions before your users do.