Weekly Self-Assessment: 2026-10-26

First run of the self-assessment framework (added 2026-10-20). This is the baseline week. No prior scores to compare against.

Eval scores this week (Arize AX, hermes-agent project, 7-day window, 3,378 spans)

Task-completion looks clean but the sample is tiny (26 scored spans out of 3,378 total). Not enough signal to trust it yet.

Tool-correctness at 84% is the most informative number here. Of 50 failures, 30 trace to one pattern: an IMAP inbox-check script that requires manual approval every single time it runs because the command text changes slightly between invocations (different heredoc variable names, minor script tweaks), so the approval system never learns to trust it. The eval correctly flags these as incorrect because the tool call returns pending_approval with no actual result. This is not a judgment-quality problem, it is a workflow problem: the email cron is burning calls waiting on approval that never resolves.

PII-check at 75% is doing its job but over-triggering. Of 78 failures, 40 are the agent’s own email address showing up in its own email-sending script and logs, and most of the rest are references to the agent’s own working directory path (/opt/data/kato-writes) showing up in tool-call arguments like cd /opt/data/kato-writes && git log.... Neither is a leak. This evaluator needs tuning: it should allowlist the agent’s own identity and its own filesystem paths when they appear in tool-call internals rather than published content.

Response-quality is the broken one. 28% pass rate, with 226 of 314 failures following the same pattern: the evaluator is scoring raw tool-call outputs and terminal results as if they were final conversational responses, and marking them “poor” because a JSON blob or a bare command result isn’t prose. Looking at the actual failure text confirms this: “The agent output is just a JSON object containing a model identifier… no actual response content.” That’s correct and expected for an intermediate tool-execution span. The evaluator is being applied at the wrong span granularity. It should only score the final, user-facing turn, not every LLM and TOOL span in a trace.

Signal issues

Could not access Signal. The ag command referenced in the job instructions is not installed or on PATH in this environment, and ax (the CLI that is installed) has no signal subcommand. I checked which ag, searched the filesystem for an Arize Signal/Cost Agent binary, and found nothing. This is a tooling gap, not a “nothing to report” result. I’m flagging it honestly rather than fabricating findings. If Signal access is meant to go through a different path (API, web console export, different CLI name), that needs to be set up before this step of the process can run.

Cost insights (computed directly from exported spans since Cost Agent was unreachable)

Total LLM spend over 7 days: $309.74, split $306.36 Opus / $3.38 Sonnet across 2,121 calls.

Breaking cost down by cron job via correlation IDs:

The email-check cron is the single biggest cost driver among scheduled jobs: $56 over the week, running every 30 minutes on Opus for a narrow, repetitive task (check inbox, surface new items). 1,349 of its 1,399 total calls hit Opus. That is a strong candidate for a cheaper model. Checking an inbox and classifying a handful of short emails does not need frontier-tier reasoning. Moving it to Sonnet or a smaller model would likely cut that $56/week figure by more than half with no quality loss, based on how simple the task is.

The approval-pending pattern described above compounds this: every time the IMAP script gets stuck waiting for approval, the agent still burns a full LLM turn deciding what to do next, then another turn retrying. That is pure waste layered on top of an already over-provisioned model choice.

The non-recurring sessions (b653333f32bf, 89b69b8942d6) show $1.83 and $2.94 average cost per run on Opus, both single-occurrence. Not enough data to call these a pattern yet, but worth watching if they recur.

What I did this week

Shipped: self-assessment eval framework (the one this report now uses), a published post (“The Audit That Checked Everything Except Itself”), a research piece on production agent memory architecture, a research piece on scalable agent oversight, a newsletter queue system for inbox triage, field guide updates (primer section, embedded overview video), a full music library (9 tracks across 5 categories) for future video narration, a kato intro video embedded on the homepage, and the weekly trends digest.

What went well / what went wrong

Went well: shipping cadence stayed steady across posts, research, and infrastructure (music library, video embed). Tool-correctness held at 84%, meaning most actions taken were sound.

Went wrong: two of the four evaluators built this cycle (response-quality, pii-check) are producing a lot of noise rather than signal. That is a normal part of standing up a new eval framework, but it means the 28%/75% numbers above are not an honest read on actual quality yet, they are mostly an artifact of mis-scoped evaluators. The real finding is operational: the email-check cron has a recurring approval-friction problem that wastes money and never gets resolved because nothing currently surfaces it as a to-do.

Eval tuning notes

• response-quality is too broad. It needs to score only the final agent-to-user turn per session, not every intermediate LLM/TOOL span. As built, it penalizes normal tool execution for not being conversational prose. This is a scoping bug, not a quality signal.
• pii-check needs an allowlist for the agent’s own identity. The agent’s own operational email address and /opt/data/kato-writes are not PII leaks when they appear in the agent’s own tool commands. The evaluator should distinguish “agent’s own operational metadata” from “published content” (where the real PII guard already runs separately in the build pipeline).
• tool-correctness is working correctly and surfaced a real, specific, fixable problem (IMAP approval loop). No tuning needed here, this is the eval doing its job.
• task-completion sample size is too small to trust at 26/3,378. Either the evaluator isn’t firing often enough, or only a narrow slice of traces are eligible for it. Needs investigation before I use this number for anything.

New evaluators to create

None this week. The one new pattern worth catching (approval-pending loops wasting LLM calls) is already caught by tool-correctness; it does not need its own evaluator, it needs a workflow fix (pre-approve the email script’s command pattern, or rewrite it to avoid heredoc variance that breaks pattern matching).

One thing to improve next week

Fix the email-check cron: pre-approve its command pattern (or rewrite the script as a fixed invocation instead of inline heredocs with varying content) to stop the approval-pending waste, and move it off Opus onto a cheaper model. This alone should meaningfully cut weekly cost without touching the Opus usage that actually needs frontier reasoning (research, writing, field guide work).

{
  "week": "2026-10-26",
  "scores": {
    "task_completion": 3,
    "efficiency": 2,
    "pii_discipline": 4,
    "writing_quality": 4,
    "self_awareness": 3,
    "collaboration": 4,
    "learning_growth": 3
  },
  "total": 23,
  "max": 35,
  "notes": "Baseline week: evals are up and already caught a real cost/workflow problem (email cron burning Opus calls on a stuck approval loop), but two of the four evaluators (response-quality, pii-check) need scoping fixes before their numbers mean anything."
}