An agent graph describes an AI workflow as a network of steps and connections, defining which step comes next and when a loop or approval is needed.
Example
Step 1 reads an email, step 2 decides “invoice or question”, and the flow then continues on two different paths.
For pros
Nodes are model calls, tools or human approvals; edges are the transitions, often with shared state. Frameworks such as LangGraph and many no-code builders work this way. Graphs are more predictable than free agent loops and easier to test.
