LangGraph: Model State, Approval, and Recovery
Turn an agent workflow into an explicit state graph with clear transitions, retries, and human approval points.
Learning objectives
- Represent an agent workflow as explicit state transitions
- Identify where approval and retry logic belong
- Test recovery without relying on a model response
ToolDix original visual
Frame
Name the outcome and constraints.
Build
Try one bounded workflow.
Review
Keep evidence, revise, and share.
Why a graph
Long-running work needs visible state. Model output is one input to a system, not the system itself. A graph makes branches, retries, and escalation points inspectable before users depend on them.
The project graph
Create five nodes: intake, retrieve, draft, approval, and finish. The state contains the request, retrieved evidence, draft, approval decision, and error count. Only retrieve may access a source. Only approval may permit a future external action.
Failure design
For each node, define a retry policy and a terminal failure. Retrieval can retry once on a transient error; an invalid request should stop immediately; a missing approval should remain pending rather than being guessed by the model.
Practice: replay a trace
Persist one successful and one failed state object as JSON. Re-run the graph from each checkpoint and verify that it does not duplicate tool work or skip approval. This is the minimum exercise that distinguishes a durable workflow from a demo.
Common mistake
Do not put authorization in a prompt. Permission checks must run in application code before an action node executes.
Sources and license context
These references informed the lesson. ToolDix adds its own explanation, workflow, and practice rather than reproducing source material.
- LangChain Academy (Course terms apply)
- Introduction to LangGraph (Course terms apply)
Take it further
Use a primary source to deepen this lesson.
Each recommendation is a direct link to the publisher or author. The study prompt is ToolDix editorial guidance, not copied course content.
Hands-on lab
LLM Course
Complete the pipeline exercise, then write down what information disappears when text becomes tokens.
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Classic reading
Attention Is All You Need
Read the abstract and architecture figure first; annotate what information flows between tokens.
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Course
AI Agents Course
Before using a framework, write down one tool contract and the exact state an agent is allowed to change.
Open original source