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+Feature: Observability — timing and metrics for performance and UX (ADR-030, #15)
+  As the maintainer running Tapir for pilot users
+  I want timing and Prometheus metrics for the activities that drive performance and UX
+  So that I can see latency, model behaviour, and usage — and feed the Stage-0 eval gate
+
+  # AI metrics are the priority (ADR-030 R3). Each scenario maps to a Go test in
+  # scenarioCoverage once implemented; tagged @pending until the TDD step lands it.
+
+  @pending  # TestObserveSummarizeRecordsModelOutcomeFallback
+  Scenario: Summarization latency is recorded per endpoint
+    Given the summarizer runs a transcript through its endpoint chain
+    When an endpoint returns a parseable summary
+    Then the summarize latency is recorded with the model, outcome "success", and whether it was a fallback
+
+  @pending  # TestObserveSummarizeRecordsFailureOutcomes
+  Scenario: A failing summarizer endpoint records its failure outcome
+    Given the summarizer runs a transcript through its endpoint chain
+    When an endpoint errors or returns unparseable output
+    Then the summarize latency is recorded with outcome "error" or "parse_error" before the chain advances
+
+  @pending  # TestObserveCaptionFetchByOutcome
+  Scenario: Caption fetch latency is recorded by outcome
+    Given a caption fetch is attempted for a video
+    When it resolves to captions, no captions, or a rate limit
+    Then the caption-fetch latency is recorded labelled by that outcome
+
+  @pending  # TestLLMUsageHookRecordsTokens
+  Scenario: LLM token usage is recorded from the completion
+    Given an LLM completion returns a usage block with prompt and completion tokens
+    When the client finishes the call
+    Then the prompt and completion tokens are recorded for that model
+
+  @pending  # TestChatAnswerLatencyRecorded
+  Scenario: Q&A answer latency is recorded
+    Given a user asks a question about a video
+    When the answer is produced from the stored transcript
+    Then the chat answer latency is recorded for the answering model
+
+  @pending  # TestHTTPMiddlewareRecordsByRoutePattern
+  Scenario: HTTP requests are counted by route, method, and status
+    Given the metrics HTTP middleware wraps the app
+    When a request is served against a registered route
+    Then it is counted and timed under the bounded route pattern, not the raw path
+
+  @pending  # TestLoginCounted
+  Scenario: A successful login is counted
+    Given a user completes the OIDC callback and a session is established
+    Then the login counter is incremented
+
+  @pending  # TestMetricsNotOnPublicMux
+  Scenario: The metrics endpoint is not on the public app port
+    Given the service is running
+    When the public app mux is inspected
+    Then it exposes no /metrics route — metrics are served on the dedicated metrics port only