Some HELP choosing the best architecture

[darthShana]

HI Team

Im working on a langgraph app to provide bookkeeping/accounting services for simple property rental companies.. My aim has been to create an "agentic" system.. Where the user has various "jobs to be done" like

• record transactions
• create reports
• file tax
• forcast
  And the llm will use the tools available to get these jobs done
  Its been going pretty well.. BUT now i have realised that there are errors that can be made and the app can benefit from a another "agent" which uses other tools to check the work find errors or anomalies, kind of like an auditor

MY question is what do you think is the correct architecture here?
is it
https://langchain-ai.github.io/langgraph/tutorials/multi_agent/multi-agent-collaboration/

or
https://langchain-ai.github.io/langgraph/tutorials/multi_agent/agent_supervisor/

or
https://langchain-ai.github.io/langgraph/tutorials/reflection/reflection/

or something else?
I thought i would ask instead of spending a lot of time investing in all of these..

Many thanks

[hinthornw]

Hi @darthShana - they're not mutually exclusive, but I typically start with reflection/validation/critique (including static code checks if relevant) since it's:

• easy to add without making the graph too much more complex.
• Generally useful for a large category of issues (like hallucinations, style guides, etc.).
  It's especially useful if you're doing async/background work. If the user is waiting for a response you can surface the initial output and then indicate that it's checking its work.

The supervisor pattern I think is easier to implement and extend than the mulit-agent collaboration example. If you find that the intent detection and routing of work needs to be improved, you can use something like the supervisor pattern and optimize the prompt and checks there.