What evidence do you actually need to debug an AI coding session?
Additional-Ladder-24 · reddit · 2026-10-28
The author raises an underrated problem: when an AI coding session produces an unexpected result, a transcript alone often can't reconstruct what happened. Useful evidence might include the full sequence of user/assistant messages, tool calls with inputs, outputs and timestamps, visible reasoning summaries and status changes, code/artifact revisions across the session, retries and regenerated branches, and the mapping between actions and resulting changes.
They are exploring a structured, inspectable, potentially replayable session exporter/telemetry layer for debugging, incident investigation, and agent evaluation, while cautioning that capturing more data doesn't automatically improve audits (and hidden internal reasoning needn't be exposed). Three questions are posed to builders and users: what information is typically missing when investigating agent mistakes; whether existing logs, traces and session exports suffice; and whether a structured replayable session record solves a real problem — with concrete workflow examples especially welcome.
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