agent-observability-spec

Agent Observability Spec Skill

You can't fix what you didn't record. For LLM systems the unit of observability is the trace — everything the model saw and did — because behaviour, not uptime, is what fails. This skill specifies what to capture, what to compute from it, and when to page someone.

Not quite this? Use agent-spec when you are specifying what the agent should do before it is built.

What This Skill Produces

• A trace schema: per-request spans and the fields each must carry
• Metric definitions across health, quality, cost, and behaviour — each with a threshold and owner
• A sampling and retention policy that keeps cost sane and debugging possible
• A privacy note: what logged content contains, who can see it, and how long it lives

Required Inputs

Ask for (if not already provided):

• The system's shape — single LLM call, RAG pipeline, or multi-step tool-using agent
• Traffic volume and cost sensitivity — full tracing at 10M req/day is a budget decision
• What "misbehaving" means here — the two or three failure modes that matter most (wrong facts? wrong actions? cost? refusals?)
• Existing observability stack (Datadog, Langfuse, OTel, homegrown) — spec into it, not around it

Trace Schema

Every request produces one trace; every model call, retrieval, guardrail check, and tool execution is a span. Minimum fields:

Span	Must capture
Request root	request id, user/session (pseudonymous), feature + prompt version, model id, total tokens, total cost, latency, terminal status
Model call	full input context (or content-addressed ref), output, finish reason, tokens in/out, cached-token share, temperature
Retrieval	query, top-k ids + scores, which chunks entered the context
Tool call	tool name, arguments, result (or ref), duration, error
Guardrail	check name, verdict, and what it did (blocked / rewrote / flagged)
User signal	edits, regenerates, thumbs, abandonment — joined to the trace id

The test of the schema: an engineer can replay any incident from its trace alone (see agent-incident-postmortem).

Metrics and Alerts

Define four families; every metric gets a threshold, a window, and an owner.

• Health — error rate, p50/p95 latency, timeout rate, provider 429/5xx rate. Page on these.
• Cost — cost per request (p50, p99), tokens per request, cache hit rate, daily spend vs. budget (pair with llm-cost-latency-budget). Alert on p99 and daily-budget burn — cost incidents are caused by the tail, not the mean.
• Quality proxies — format/schema violation rate, refusal rate, groundedness-check failure rate, judge score on a sampled slice, regenerate/edit rate. Alert on drift vs. a rolling baseline: absolute thresholds go stale, deltas don't.
• Behaviour (agents) — steps per task, tool-error rate, loop detection (same tool + same args N times), unauthorised-action attempts caught by guardrails. Page on the last one.

Sampling & Retention

• Metadata for 100% of requests (ids, versions, tokens, cost, status) — this is cheap and non-negotiable.
• Full content traces: 100% for errors, guardrail hits, and negative user signals; [1-10]% random sample for the rest, adjusted to volume.
• Retention: full content [30-90] days, metadata [12+] months for trend baselines; incident traces pinned indefinitely.
• Privacy: logged context contains user data — state where it lives, who has access, how deletion requests reach it, and that traces are scrubbed or access-gated before wide sharing.

Output Format

Observability Spec: [feature/agent]

System shape: [calls/pipeline/agent] · Volume: [req/day] · Stack: [tooling]

Trace schema: [the span table, tailored]

Metrics:

Metric	Family	Threshold / baseline	Window	Alert → owner

Sampling & retention: [the policy]

Privacy: [content classification, access, deletion path]

Dashboards: [the 2-3 views: live health, quality drift, cost]

First incident drill: pick yesterday's worst trace and confirm it can be replayed end-to-end from the stored data.

Quality Checks

• Any incident is replayable from its trace alone — the schema was tested against that bar
• Every metric has a number, a window, and a named owner — no orphan dashboards
• Quality alerts are drift-based against a rolling baseline, not absolute guesses
• Sampling keeps 100% of error/guardrail/negative-signal traces
• The privacy note exists and names retention and access — logged prompts are user data

Anti-Patterns

• Do not log only inputs and outputs — without retrieval and tool spans, root cause analysis is guesswork
• Do not alert on mean cost or mean latency — the tail is where both incidents live
• Do not run judge-based quality scoring on 100% of traffic — sample; spend the budget on better baselines
• Do not treat observability as launch-week scaffolding — drift metrics only work with months of baseline
• Do not ship an agent that can take actions without logging the guardrail verdicts alongside the actions

Example Trigger Phrases

• "What to log for an LLM app?"
• "Design agent tracing."
• "Define quality and cost monitors."