When a run does not work
Separate environment problems, unavailable tools and unverified outcomes so the next step addresses the actual issue.
The command cannot start
Confirm that you are in the repository root and using its configured Python environment. Module or dependency errors occur before the agent can do useful project work. Preserve the error message and compare it with the setup instructions for your branch.
Do not repeatedly restart a failing command without changing the condition that caused the failure.
The model service is unavailable
Check the configured endpoint and whether the selected model service is running. A running interface does not prove inference is available. Do not interpret a website example as a response from the local runtime.
If you change the endpoint, review the data path as well as connectivity. Sending project material to a remote service is not just a performance setting.
A tool or connector is missing
Confirm which tools are actually registered for this entry point and whether a connector is configured and reachable. Tool availability can differ across environments. An unavailable tool should remain an explicit limitation in the run.
Use an available, authorized alternative when appropriate. Do not fabricate the result of an operation that did not run.
The outcome is still uncertain
Read the executed checks and their outputs. A successful process may have checked less than the task requires. Narrow the remaining question and run a relevant check before accepting a completion claim.
If you ask for help, share the entry point, a concise reproduction and the relevant error. Remove credentials and confidential project material before sending it.