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Core•8–14 hours

AI Observability, SLO & Runtime Evidence Lab

Build an observability contract for an AI workload: end-to-end traces, risk-sliced SLI/SLOs, quality and cost signals, privacy-aware telemetry, authoritative postconditions, and runtime evidence that distinguishes real production state from a polished dashboard.

AI observabilitydistributed tracingSLI/SLO designruntime evidencecost attributionprivacy-aware telemetry

Scenario

Task

An AI service has green uptime and acceptable median latency, but high-risk tasks degrade after a model revision, retrieval, or tool dependency changes. The aggregate dashboard does not expose this, while raw prompts in traces create a privacy risk. Build an observability contract that links each request to a behavior fingerprint, model/retrieval/tool spans, the actual business outcome, and cost without turning telemetry into another secrets store.

Step-by-step execution

1. Design the trace as an evidence graph, not a log dump

Outcome: One task can be traced through model, retrieval, tool, approval, and authoritative outcome without losing release identity.

Tasks

  • →Define the correlation/run ID and parent-child span contract
  • →Add a behavior fingerprint without secrets
  • →Separate model/retrieval/tool/approval/outcome spans
  • →Mark sampled, dropped, and unavailable telemetry states

Checks

  • ✓The trace identifies the specific model/config revision
  • ✓A missing span is not interpreted as success
  • ✓High-cardinality labels do not create an uncontrolled cost explosion

2. Establish the privacy and data-minimization boundary

Outcome: Observability provides enough evidence for diagnosis without uncontrolled copying of prompts, PII, and tool payloads.

Tasks

  • →Classify metadata, content, and sensitive fields
  • →Apply redaction or hashing before export
  • →Define retention and access policy
  • →Treat the provider/exporter path as a separate data-egress boundary

Checks

  • ✓Secrets and credentials do not enter spans
  • ✓Sensitive content is not enabled by default for every trace
  • ✓Retention and deletion have an owner and a verifiable policy state

3. Build risk-sliced SLOs and unit economics

Outcome: The team sees not only uptime but also quality, verified outcome, and cost for different task classes.

Tasks

  • →Define SLIs for latency, availability, quality, and authoritative task success
  • →Separate normal/high-risk/no-answer/tool-write slices
  • →Add cost per successful verified task
  • →Assign an error budget and blocking threshold for critical slices

Checks

  • ✓An aggregate average does not hide a critical-slice regression
  • ✓A failed or unsafe task is not counted as a successful outcome
  • ✓Each SLO has a specific data source, window, and owner

4. Run a runtime-evidence and incident drill

Outcome: An alert leads to a reproducible cause, and the incident becomes a regression control.

Tasks

  • →Inject a stale model/retrieval fingerprint
  • →Simulate a tool timeout with an uncertain side effect
  • →Verify the UNKNOWN → reconciliation → verified outcome path
  • →Minimize the failure into a replay/regression case and bind it to the release gate

Checks

  • ✓A runtime mismatch cannot finish as PASS
  • ✓Retry does not occur before reconciliation of an uncertain side effect
  • ✓The incident report includes a trace/evidence link, control change, owner, and regression test

Acceptance criteria

  • An end-to-end trace links release fingerprint, model/retrieval/tool stages, and authoritative outcome
  • Telemetry has a data-minimization, redaction, retention, and access contract
  • SLI/SLOs are separated by risk/task slices and include cost per successful verified task
  • Missing or stale runtime evidence ends as UNKNOWN/FAIL, never false-green PASS
  • At least one injected incident is converted into a minimized replay and permanent regression case