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v1.9.0 — The Intelligence Release: Local Copilot, AI Attributes, AI Escalation Rules

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@kimpearce888 kimpearce888 released this 28 Sep 03:46
· 11 commits to main since this release
v1.9.0
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v1.9.0 — The Intelligence Release

Local Copilot · General AI Attribute Layer · AI Escalation Rules

The third milestone of the 48-phase roadmap (plan phases 15–17). Three systems, one discipline: the AI is never the authority — it reads, explains and recommends; deterministic code decides, stores and writes.

🤝 Local Copilot (plan phase 15)

An interactive, read-only assistant inside every conversation's context pane. Ask it what a rep actually asks:

What is this customer asking? What happened in their previous tickets? Have we seen this issue before? What solved the previous cases? What documentation applies? What should I check before replying? Why is this ticket currently considered urgent? Show evidence for that answer.

  • Runs fully locally via LM Studio — no cloud LLM, ever. If AI is disabled or LM Studio is unreachable, the Copilot says so (honest 503s and visible error states) instead of pretending to work.
  • The model never sees SQL. It can only call the allowlisted read-only tool registry — now 13 tools, including 7 new ones: conversation context, customer history, similar conversations, issue clusters, AI analyses, SupportOS metadata, and the AI attribute snapshot.
  • Every tool result is server-validated, bounded and redacted before it reaches the prompt.
  • The tool loop is hard-bounded (max rounds + max tool calls): a tool-looping model gets stopped and must answer from the evidence it already has — honestly.
  • Citations are machine-generated. The source list is built by the server from the tool executions it actually performed — the model cannot fabricate a source that survives. Answers must cite evidence with inline markers.
  • Read-only by construction: there is no write path anywhere in the Copilot. Sessions and messages persist locally; every turn is audited (ai_involvement=true) and recorded in ai_runs with model, prompt version and latency.
  • Deterministic starter questions personalize from local facts (prior tickets, known-issue links, analysis state).

🏷️ General AI Attribute Layer (plan phase 16)

Every conversation gets first-class local attributes — in a closed 14-key catalog:

intent · product · feature · issue · urgency · frustration cues · technical familiarity · customer goal · question count · risk · known issue · issue cluster · response style · escalation signal

  • Two layers. Deterministic slots (urgency, question count, risk, known-issue links, escalation signal…) are computed from observable local facts with zero AI — they always exist. AI slots (intent, product, feature, issue, customer goal, response style) are extracted by LM Studio and stored only with enum-closed values and evidence excerpts whose thread references were actually in the prompt — otherwise they are dropped.
  • Versioned. Every recompute supersedes prior rows (superseded_at) and preserves full history per attribute.
  • Honest unknowns. A key with no current row is unknown — never fabricated, never a guess wearing a confidence badge. Attributes never overwrite Help Scout source data; they live in their own table with their own schema version.
  • Searchable, filterable, reportable.
    • A live inbox filter (aiAttribute / aiAttrOp / aiAttrValue) — compiled through the same viewEngine code path as saved Inbox Views, so a live filter and a saved view can never disagree about what urgency: high means.
    • Saved Inbox Views and Outreach segments can carry ai_attribute conditions.
    • The AI Center → Attributes tab shows a coverage report (known vs unknown per attribute, honest percentages) with a searchable conversation drill-down.
    • A per-ticket snapshot card in the AI sidebar: confidence, source (deterministic/AI), expandable evidence excerpts, honest-unknown list, one-click recompute.

⚡ AI Escalation Rules (plan phase 17)

Automation conditions grow two AI-aware fields:

  • ai_attribute — requires a catalog key (urgency, risk, question_count, …); operator semantics follow the attribute's value type; ordered enums compare by vocabulary position.
  • ai_verification — failed / passed / none, from the latest AI draft verification.

The safety invariant is unchanged: conditions only decide whether a rule matches — every action flows through the existing read / non-destructive / higher-risk approval tiers. "High urgency → review queue" is safe because the attribute decides nothing about the customer; the queue is internal, and the approval tier is unchanged. A missing attribute reads as unknown: it matches equals unknown and never a concrete value.

Verification

  • 462/462 tests green (+54 over v1.8.0): 17 unit (catalog + repository contract + viewEngine compilation), 22 integration (deterministic layer with AI off, tool registry surface, Copilot tool loop with a deterministic fake model, escalation-rule matching), 15 e2e over real HTTP (attribute surface, live filter parity, saved views with attribute conditions, the full Copilot chat loop, the escalation-rule lifecycle, 4xx hardening).
  • Black-box audit section K (383 checks total): injection-shaped attribute keys/values, LIKE-wildcard probes, live-filter parity asserted against the snapshot, hostile copilot bodies, closed-vocabulary automation conditions — final 0 HIGH / 0 MEDIUM. The audit found and fixed real bugs before release: copilot citations that failed to deep-link conversations, and an unknown-session 503 that should have been a 404.
  • Human-like browser pass: the Copilot tab (starter questions, honest offline error, session persistence), the attribute snapshot card (recompute, honest unknowns), the live filter through the real UI (URL-backed, honest notes, parity with the drill-down), the AI Center tabs, the automation rule form with the closed catalog selector — and all 17 pages walked with zero console errors.

Upgrading

Migration 013 (m3_copilot_attributes) applies automatically on first run — it creates ai_attributes, copilot_sessions and copilot_messages. No backfill is forced: a bounded worker job (≤500) computes attributes for previously analyzed tickets, everything else computes lazily on its next analysis or on demand. Pre-1.9.0 history honestly reads as unknown.

Full changelog: CHANGELOG.md

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