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Agent design and scopingagent architecture
System prompt writing and agent stateprompt engineering
Tool definition and tool callingfunction calling
Agent permission control and safe hand-offsAI safety controls
Agent evaluation and output checkingLLM evaluation
Agent loop implementation, tracing and debuggingagent orchestration
Questions
Before you commit
An agent is a loop: it observes, reasons, branches, invokes a tool, reads the result and decides again until a stated condition ends the run. A chatbot answers a turn and stops. ORBIT names the five things any agent does (Observe, Reason, Branch, Invoke, Terminate), and SPINE names its five components (State, Perception, Identity, Navigation, Execution).
No. You build the agent loop in plain Python so every part stays visible: the loop, the tool registry, structured output and the traces. Frameworks come up once, as context. You should already be comfortable calling an LLM API and writing Python.
ReAct alternates reasoning and acting: the model decides the next step, calls a tool, reads the result and decides again. Chapter 5 has you wire that loop from scratch, with a step budget, a stated reason for stopping and a record of every step, so you can see why a run ended where it did.
Chapter 4 treats every tool result as an observation the agent has to parse: a result envelope, an error envelope the model can read, and timeout and retry policies that never repeat a side effect. Chapter 5 adds traces, so when a run goes wrong you can find the step where it drifted and replay it.
Pathfinder, a supply-chain search agent. It takes a sourcing question, searches a mock supplier API, compares candidates on lead time and risk, and drafts a recommendation that a sourcing analyst approves before anything happens. You sketch it in chapter 1, blueprint it in chapter 2, and build and debug it in chapter 5.
More often than most teams expect. Lesson 1.3 gives you a test for whether a task needs an agent at all. A fixed workflow or a single model call is often cheaper, faster and easier to debug, and the course shows how to tell the difference before you build.
With boundaries set before the first run: a permission surface that lists what the agent may touch, constraints enforced in code rather than only in the prompt, and a person approving any consequential action. Every run also leaves a trace you can read afterward.
Engineers who have called an LLM API and written Python and now want to build agents they can reason about. It is relevant to roles such as AI engineer, LLM application developer and AI solutions engineer.
Tutorials usually show an agent that works once. Here each chapter pairs one framework (ORBIT, SPINE, DEPTH, GRIP, FORGE) with the failures it prevents, and you practice it on the same project. Vendor facts, such as API names and model versions, are checked against current documentation.
Python 3.12 or newer with uv, Pydantic for structured output, a small FastAPI app that serves the mock supplier data, and the tool-calling interfaces of the OpenAI Responses API and the Anthropic Messages API. The tools chapter also covers the Model Context Protocol (MCP).
No. You should be able to write a Python function, read JSON and use a terminal. Having tried a chat model is enough; the course explains tool calling, structured output and the agent loop from the start.
No. You write the loop, the tool registry and the trace yourself in plain Python, using a model API directly, so you can see what each part does. Pydantic is used to define argument and answer shapes.
Pathfinder is the agent you build across the course. It searches a mock supplier catalog, answers with supplier ids it can ground in tool results, and has one side-effecting call that waits for your approval at the terminal.
Most of the build and testing runs offline with a scripted model that replays fixed replies. The final chapter runs against a real model, which needs an API key; the runs are short.