Performance reports were structurally disconnected from the v6 ID
space: the UI (and any API caller working from /api/state) addresses
resources by canonical unified ID, while the metrics store is keyed by
each platform's native source ID (the resource's metricsTarget). The
engine queried the store with the unified ID verbatim, so every report
rendered 'Data Points: 0' regardless of how much history existed, and
covers showed raw hash IDs a report reader cannot map to a machine.
- MetricReportRequest gains MetricsResourceID: handlers resolve the
unified ID through the tenant monitor's resource store (new
Monitor.MetricsTargetForResource accessor; the registry computes
targets on demand, they are not persisted on snapshot structs) and
the engine uses it for store queries only. Recovery points and
Patrol findings stay keyed by the unified ID.
- Legacy snapshot models and their alerts are keyed by the metrics
target ID, so enrichment now matches either ID space and resource
names/status resolve again on covers, headers, and fleet rows.
- Fleet summaries mirror the single-report guard: zero data points
across the fleet renders a muted NO DATA card instead of a green
HEALTHY 'All systems operating normally' - false reassurance is the
worst failure mode for a client-facing stability report.
- Em dashes in PDF-bound literals become hyphens; fpdf core fonts are
cp1252 and rendered them as mojibake.
Found by exercising pulse_summarize in a real chat session. The
chat-tool response surfaced:
"observations": [{"Text": "...", "Severity": "info"}]
The AI narrator's system prompt (report_narrator.go) tells the
model to emit lowercase keys:
{"text": "...", "severity": "..."}
The model was being taught one schema and shown a different one
in the tool response for the same shape. NarrativeBullet,
FleetOutlier, Narrative, and FleetNarrative had no JSON tags, so
embedded struct fields serialised with their Go names.
Add struct tags so the wire shape matches the prompt schema. Pure
marshaling change — JSON tags don't affect Go field access, so
PDF rendering (which reads fields directly) is unchanged. Tests
in narrative_json_test.go pin the shape so the inconsistency
can't reappear silently.
Process note: this is a class of bug that only appears when an
LLM actually consumes the output. No unit test caught it; no
review of the code showed it; the model running through the chat
path is what surfaced it. Another argument for "exercise the
artifact" — even the tool surface that looks correct in
isolation has hidden inconsistencies you only see when something
external reads it.
The single-resource AI narrative landed in b2bd9d114 but multi-resource
fleet reports stayed heuristic-only. That left a gap on the exact axis
where AI helps most: a 50-resource fleet PDF is where synthesis is the
difference between useful and unread.
Introduce FleetNarrator as a separate interface from Narrator. The
input shapes are different — single-resource takes one set of metric
stats with a prior window, fleet takes a denormalised cross-resource
view with per-resource summaries plus a fleet aggregate.
HeuristicFleetNarrator owns the deterministic fallback: ranks
resources by severity (critical alerts > unhealthy disks > storage
pressure > memory > CPU > non-critical alerts), picks up to 5
outliers, derives cross-cutting patterns by counting how many of N
resources share a hot signal, and emits fleet-scoped recommendations.
internal/ai.Service implements FleetNarrator through
report_fleet_narrator.go. Distinct use-case label
(report_narrative_fleet) so fleet vs single-resource spend is
separable in the cost ledger and budget gate. The fleet payload is
denormalised through buildReportFleetPayload so prompt cost scales
linearly with fleet size. Same fail-closed invariant — nil provider,
parse failure, or context cancellation falls through to the heuristic.
Single-resource Narrator is intentionally NOT propagated through
engine.GenerateMulti: a 50-resource fleet report performs one AI call
(fleet narrator), not 51. The router resolver returns the AI service
for all three roles (Narrator, FleetNarrator, FindingsProvider).
The fleet PDF renders the FleetNarrative in the fleet summary cover
when present: executive prose, named outliers with severity-coloured
bullets, cross-cutting patterns, recommendations, optional period
comparison, and an AI provenance footer. The deterministic resource
summary table is preserved above so every named outlier is verifiable
against the table immediately below it. Legacy "Highest CPU / Most
alerts" bullets remain as the fallback when no FleetNarrative is
attached.
rcourtman