Root Causes & Remediation

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When analysis runs

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What each root cause holds

• Evidence: the traces attributed to the cause, each with the passage that shows it. Since analysis samples, most traces are attributed to no cause; filter the finding’s trace list by Root cause to see the ones that are.
• Suggested fix: what to change in your application, plus prompt edits as before/after passages when the fix is a prompt change. A cause with no confident fix says so.
• A status: a cause recent runs no longer find is labeled No longer detected, the signal that a fix worked; it returns by itself if the issue comes back.

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From your coding agent

from dataframer import Dataframer

client = Dataframer()  # uses DATAFRAMER_API_KEY

for failure in client.dataframer.failures.list().results:
    for cause in failure.analysis.causes:
        if cause.status != "active" or cause.remediation is None:
            continue
        print(failure.name, "->", cause.title)
        print(cause.remediation.instructions)
        for edit in cause.remediation.prompt_template_edits:
            print(edit.template_ref, "\n-", edit.before, "\n+", edit.after)

• status, last_analyzed_at and new_traces_since_analysis say whether causes exist and how fresh they are.
• causes[] are the root causes, each with examples[] as evidence and a remediation holding the instructions and the prompt_template_edits[] as before/after passages.
• prompt_templates[] are the prompt templates DataFramer reconstructed from your traces; each edit’s template_ref names the one it applies to, so the agent can find the matching text in your repository.

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Next steps