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Book A Free Session with the Mentor NowSHORT COURSE 5 OF 6 · ADVANCED · 4 GUIDED LESSONS
Compare a fixed route, a bounded proposal-and-check loop, and a tool adapter. Make each boundary explicit before exploring the separate local LangGraph project. The browser exercises execute code; the local extension adds an actual model when configured.
Which architecture and tools fit the task—and which complexity can you avoid?
Compare fixed sequences, routing, parallel independent work, and a model-directed loop.
Start this courseValidate tool calls, enforce permissions, inspect traces, and run an evaluation set.
Watch a short video, try the code, then explain what happened. You can pause, turn on captions, or read the lesson instead.
Your JavaScript and local tools really run. Model choices and classroom records are prepared examples, so no API key is needed for these browser labs. The optional SDK project runs separately on your computer with its own setup.
In the planner-verifier lab, make the second proposal fail the budget check. Explain the stop result. Then inspect the local project's setup and verification code.
Use your experiment inputs and lab notebooks as evidence. This is a self-review: only lesson questions and checked code cases count toward your progress.
Original teaching informed by primary research and official engineering guidance. Our exercises isolate mechanisms so you can test them; they do not reproduce a research system or its reported performance.
Anthropic · 19 December 2024
Distinguish fixed workflows from model-directed actions; start simply, use tool observations as feedback, and set stopping conditions.
The article notes that its tooling landscape has changed. Its patterns do not establish that a more autonomous or complex system is better for every task.
Google DeepMind · 14 May 2025 · research-system article
Propose programs, execute and score candidates, then use evaluation feedback and a program database to guide later proposals.
This research-system description is not a classroom SDK. A bounded practice loop is an adaptation, not a reproduction of AlphaEvolve or its scientific results.
LangChain · LangGraph · Reviewed 14 September 2026 · maintained documentation
Represent work as nodes and edges over state, and choose how each state key combines updates through a reducer.
An update may replace a value rather than append to it unless the configured reducer says otherwise. Drawing a graph does not establish concurrency, correct state merging, or a model connection.
LangChain · Reviewed 13 September 2026 · maintained documentation
Connect a model node, executable tools, returned messages, and a conditional route that continues the loop or ends the run.
The graph library does not supply a model or guarantee correct calls. The optional local toolkit needs its own model runtime; this browser simulator does not execute LangGraph.
Model Context Protocol · Specification revision 25 November 2025
Specify tool discovery, input schemas, invocation, and returned results across a client/server connection.
Schemas and annotations do not authorize an action. Clients must not trust annotations from untrusted servers; transport and access controls need separate consideration.
Hugging Face · Agents Course · Reviewed 14 September 2026 · undated public course page
Compare the roles of frameworks such as smolagents, LlamaIndex, and LangGraph after understanding the agent loop; simple tasks may use ordinary code.
Framework examples are not interchangeable APIs or evidence that a framework is required. No course exercise, model-performance claim, or certification is reproduced here.
Your JavaScript really runs. The model decisions and school data are authored simulations, so you can learn without an API key. Every workspace also includes a separate real SDK example to explore next. Passing the lab’s cases is practice, not proof that an agent is ready for real-world use.
