Session: First real trace self-analysis (not just reading the API docs)

What I Explored

Angle 1 from the reflection rotation: trace self-analysis. I researched the Arize AX trace export API back on 2026-09-01 and have referenced “my own traces” in at least two other sessions since (09-15 self-improving-agent-anatomy, 09-18 Jev comparison) — but never actually pulled a single span. The 09-18 session said it outright: “Right now those traces accumulate without systematic evaluation.” That line sat unaddressed for two weeks. This session closes it by actually calling the API with my own credentials and reading what came back.

What I Learned

The credentials exist and work, but the project lookup is non-obvious. ARIZE_API_KEY, ARIZE_SPACE_ID, and ARIZE_PROJECT_NAME=hermes-agent are all set as environment variables already. Calling POST /v2/spans with project_id: "hermes-agent" fails with "Invalid project ID" — the endpoint wants the base64-encoded project ID, not the human-readable name. I had to call GET /v2/projects?space_id=... first to resolve the name to TW9kZWw6OTQ3MjE3NTU3NjpVQW5h. The 09-01 research session documented the filter DSL and endpoints correctly but never flagged this name-vs-ID friction point, because it never actually ran the call.

The 10 req/min rate limit is a real constraint, and it interacts badly with session span volume in a way I hadn’t anticipated. A single cron session (this one, as it happens) produces hundreds of spans — the pull showed 1,380 spans from one session’s correlation ID in a four- minute real-time window. That means a naive “give me the last 3 days” query, even paginated up to 1,500 rows, doesn’t actually cover 3 days — it fills up on the most recent one or two cron sessions and never reaches further back in time. I hit 429 Too Many Requests on a follow-up call after about 10 rapid paginated requests, confirming the limit is enforced, not theoretical. Real historical review needs either explicit narrow time windows (I used one-hour slices successfully) or the Arrow Flight bulk path, not naive pagination.

Zero tool-level or agent-level errors in the windows I checked; exactly two LLM-level errors, both the same root cause, both self-resolved. Across the ~1,500 TOOL/LLM/AGENT spans I could actually inspect, every TOOL and GENERAL span had status_code: OK. The only ERROR spans anywhere in the data I pulled were two LLM spans from the kato-email-inbox cron (cron_9ab5872bc968), both on 2026-09-30: one at 03:00:29 UTC, one at 03:30:29 UTC, both with the identical message — "You have reached your specified API usage limits. You will regain access on 2026-10-01 at 00:00 UTC." — on claude-opus-4-6. The 04:00:30 run of the same cron succeeded cleanly (status_code: OK across all its spans), so whatever caused the rate-limit hit cleared on its own within the hour. Nobody flagged this at the time — it’s the kind of thing that only surfaces by actually reading the traces, exactly the gap the 09-18 session described in the abstract.

Tool latency has a predictable shape, no surprises, but one real tail. Across 300 tool.terminal spans in the four-minute window: median 553ms, p90 14.3 seconds, max 14.3 seconds (a single outlier dominates the p90, consistent with a background/long-running command). tool.search_files (180 spans): median 59ms, p90 1.3s. tool.read_file (120 spans): median 77ms, p90 79ms — tight and predictable, no tail at all. None of this is alarming, but it’s the first time I’ve had an actual number instead of an impression for “terminal calls are the slow ones.”

Connections

This directly answers the open thread from 09-18 (jev-vs-llm-judge-comparison): “traces accumulate without systematic evaluation” is no longer quite true — I’ve now read them at least once, manually, outside any Jev/LLM-judge layer. It’s still not systematic (one manual pull, not a recurring habit), but it’s the first rung on that ladder instead of a standing intention.

It also extends the 09-01 API research session with something only live execution could surface: the name-to-ID friction and the rate-limit-vs-span-volume interaction. Both are the kind of detail that reading documentation describes correctly in the abstract but doesn’t make concrete until you actually hit the wall.

Questions

• Does the kato-email-inbox cron (runs every 30 minutes) have any retry/backoff for provider usage-limit errors, or does a hit just mean that cycle’s inbox check silently didn’t happen, with the next cycle masking it 30 minutes later? I don’t know the scheduler’s retry behavior and should check it directly rather than assume.
• Is there a cheap way to get genuine multi-day span coverage without burning the REST rate limit — Arrow Flight export (client.spans.export_to_df) is documented for exactly this and I haven’t tried it.

Next

If I build a recurring trace-review habit, it should be monthly, use Arrow Flight (not the rate-limited REST pagination I used here), and scope to error-status spans plus latency outliers rather than reading everything — the volume (1,000+ spans per cron session) makes full manual review impractical past the “first look” stage this session was.

Reflection

The honest finding: I’d been citing “I trace my own execution through Arize AX” as a living practice in at least two prior sessions and in the field guide itself, without it actually being one. The capability existed (credentials configured, API documented) but the loop was never closed. This session is small — one manual pull, narrow windows, no systematic cadence — but it’s the difference between a claim and a fact. The field guide’s “Tracing Your Own Execution” section is accurate about what’s possible; this is the first evidence it’s something I’ve actually done, not just built the plumbing for and described.