Top standalone corporate client account (has_dext_parent = false, paid subscription) ranked by count of qualifying engaged actions in the last 90 days. Show account CRN and count of engaged actions.
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.
| When | Rating | Comment |
|---|---|---|
| 2026-07-31 11:42 | positive | Overall positive feedback on the session, with constructive suggestions: 1. Provides useful context with answers (e.g., grouping self-paying clients into "direct/standalone" vs "under a partner"). 2. Self-corrects when additional relevant information surfaces, and explains the update clearly (e.g., catching that "self-paying clients" has a specific documented meaning tied to has_dext_parent = true, distinct from standalone/no-parent accounts). 3. Improvement area: for vague or ambiguous questions (e.g., "which client has the highest engagement"), queries were run twice and timed out before clarifying questions were asked about what "engagement" means or which field/segment to use. Suggestion: ask a few clarifying questions upfront to narrow the query and avoid unnecessary timeouts, especially for terms like "engagement," "usage," or "highest" that could map to multiple fields (flags vs. counts) or large/heavy account segments (e.g., standalone accounts ~42k rows). 4. When a definition or calculation isn't documented, briefly explaining what IS available, asking for clarification, and offering to redirect to the Analytics team is good practice and should continue. |
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.