Accounts that removed or downsold AI Assist in H1 2026. One row per account per event. Columns: account CRN, account name, Salesforce country, finance account type, churn reason. Exclude Dext demos and Reseller accounts. Include account_name as a column.
Per-step detail — each LLM call, each Omni request, each rejected draft —
is traced to stdout only and is not stored, so it cannot be shown here.
Query attempts counts run_query executions, not model turns.
Error calling tool 'ask': Wait returned empty summary with no result: {"job_id":"bbaf8cd5-8c68-4473-bfe4-e86e1160017e","status":"COMPLETE","client_result_id":"6c8ce895-a92d-44cf-8a8a-7005255bf5c3","summary":{"cache_type":"MISS","display_sql":"SELECT \"t1\".\"omni_dbt__eds_calendar_day.date_day[date]\" AS \"omni_dbt__eds_calendar_day.date_day[date]__raw\",\n \"t1\".\"omni_dbt__eds_all_accounts.account_crn\",\n \"t1\".\"omni_dbt__eds_all_accounts.salesforce_country\",\n \"t1\".\"omni_dbt__eds_all_accounts.finance_account_type\",\n \"t1\".\"omni_dbt__eds_all_accounts.churn_reason\",\n \"t1\".\"omni_dbt__eds_bookings_addon_changes.ai_assist_bookings_mrr_movement\",\n \"t1\".\"omni_dbt__eds_bookings_addon_changes.ai_assist_client_limit_change_amount\",\n COALESCE(SUM(\"t1\".\"$f8\"), 0) AS \"omni_dbt__eds_bookings_addon_changes.total_ai_assist_bookings_mrr_change_amount\",\n TO_CHAR(\"t1\".\"omni_dbt__eds_calendar_day.date_day[date]\", 'YYYY-MM-DD') AS \"omni_dbt__eds_calendar_day.date_day[date]\"\nFROM (SELECT \"omni_dbt__eds_all_accounts\".\"ACCOUNT_CRN\" A... [truncated, 25251 more chars]
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Cost is the LLM completion spend LiteLLM priced for each call, summed per
request.
Chandler is running on the Codex CLI, which draws ChatGPT plan quota
rather than per-token API billing, so this figure is not money paid —
it is what the same traffic would have cost on the API, priced from the
token counts Codex reports. Read it as the size of the bill avoided.
Two known limits: embedding spend is not recorded, so
retrieval and matching cost is missing, and because the figure is one sum
per request it cannot be split by model within a request — a
request's classifier and agent calls can use different models while
llm_model holds only one name.
Only authenticated calls are logged, USAGE_LOG_ENABLED can
switch logging off, and log writes are fail-soft — this is not a complete
record of traffic. Questions are grouped into sessions: a session is
exact when the caller echoed its id back to us and otherwise inferred from
a 30-minute gap in that user’s activity, so a grouping is only as
good as the source shown on the session itself. A call with no
attributable user gets no session at all; those questions are listed
separately rather than dropped.