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.
Performance reports rendered the Executive Summary, Observations, and
Recommendations sections from inline threshold rules in pdf.go. That
narrative looked intelligent but was static templating against alert
counts and metric percentiles, which felt off-brand alongside Patrol
and Pulse Assistant.
Introduce a Narrator interface in pkg/reporting and a FindingsProvider
counterpart that the engine consults at report time. The heuristic
rules are lifted into HeuristicNarrator unchanged so the deterministic
fallback still produces the same observations and recommendations.
The engine now also queries the comparable prior period and threads
its aggregate stats through the narrator so deltas can be expressed.
internal/ai.Service implements both interfaces via report_narrator.go
(single-turn JSON call grounded in the structured ReportData payload,
falling back to the heuristic on any error/timeout) and
report_findings.go (Patrol findings whose lifecycle overlaps the
report window). The reporting handler resolves the per-tenant AI
service when it is configured and supplies it in the request; absent
configuration, reports look identical to the prior heuristic output.
Charts, stats tables, alert lists, storage and disk sections stay
deterministic — sysadmins can verify every AI claim against the data
tables next to it. The PDF renders the AI prose between the health
card and Quick Stats, adds a Period-over-period section after
Recommendations, and prints a provenance footer when the narrative
came from the assistant.
ai-runtime.md and api-contracts.md updates land in a follow-up commit
on this branch; agent-lifecycle / performance-and-scalability /
storage-recovery have no contract delta from this change (router.go
is referenced in their Extension Points but their semantics are
unchanged).
rcourtman