Complete visual structure
See the failing line in relation to loops, branches, subworkflows, variables, and upstream commands.
AI WORKFLOW DEBUGGING
Debug the workflow that failed—not an abstract description of it.
Runavelo can give your selected AI model the visual flow, command parameters, runtime logs, and error stack needed to connect a symptom to a likely cause. The proposed changes remain visible and testable.
EVIDENCE BEFORE ADVICE
A useful diagnosis needs context from the actual run.
A generic chatbot can only reason from the failure description you type. Runavelo can assemble the relevant automation evidence so the assistant can inspect what command ran, how it was configured, what happened immediately before it, and what the runtime reported.
The values that shaped behavior
Inspect target elements, timeouts, match methods, expressions, inputs, outputs, and relevant configuration.
Logs and error stack
Use timestamps, progress messages, the reported exception, and available stack details to test the explanation.
EXAMPLE: ELEMENT NOT FOUND
Distinguish a changed selector from a missing page state.
An “element not found” message does not identify the root cause by itself. The page may still be loading, a dialog may cover the target, the workflow may be on the wrong tab, the captured selector may include a changing attribute, or the element may genuinely no longer exist.
With the workflow and run evidence, AI can compare the failed command with the preceding navigation, current URL, captured element definition, and timing. A recommendation might remove an unstable page-number attribute, add an explicit wait, correct the active webpage variable, or handle the final pagination page as a normal stop condition.
The change should then be reviewed and rerun against representative pages. A fix that succeeds once is not enough; test the first page, intermediate pages, the final page, slow loading, empty results, and a missing optional field.
AI CAN ALSO TEACH
Ask for the manual repair, not only an automatic one.
If you want to understand the product or keep full control, ask the assistant which command to add, where to drag it, which parameter matters, and how the resulting description should read. This turns a failure into a reusable lesson about workflow design.
The same product documentation used during generation can help explain command semantics and valid parameter values. You can compare the suggestion with the visible workflow rather than accepting an opaque code patch.
HUMAN REVIEW STILL MATTERS
AI advice is a hypothesis until the workflow passes tests.
Models can misunderstand incomplete evidence or propose a fix that is too broad. Review changes that touch credentials, external systems, destructive actions, financial data, access controls, or high-volume operations.
Prefer the smallest correction, retain useful logging, and test with safe data. When the evidence does not support a confident diagnosis, collect another run rather than hiding the failure with long retries or ignored errors.
QUESTIONS
AI workflow debugging FAQ
What evidence can AI inspect?
The assistant can use the workflow structure, command parameters, runtime logs, and available error stack to explain a failure and recommend changes.
Does AI change the workflow automatically?
AI can propose or apply supported workflow changes through its tools, but the result remains visible in the editor for review and testing.
Can AI explain how to fix a workflow manually?
Yes. You can ask for a diagnosis, a step-by-step manual repair, or guidance on how to configure the relevant command.
Will every diagnosis be correct?
No automated diagnosis is guaranteed. Treat the recommendation as a reasoned starting point, review the evidence, and test the change before production use.
Keep the workflow and its diagnosis in the same workspace.
Generate a visual flow, refine it manually or with AI, run it, and bring the actual failure evidence back to the assistant when something changes.