feat(ai-orchestrator): improve mesh router classification for inferred/generic queries

libs/ai-orchestrator/src/__tests__/mesh-router.spec.ts

@@ -6,6 +6,7 @@ import {
   createMeshRouterNode,
   DEFAULT_MESH_CONFIG,
   _test_createDefaultMeshClient,
+  MESH_SYSTEM_PROMPT,
   type MeshRouterConfig,
 } from '../orchestrator/mesh-router';
 import { createDefaultAgentState } from '../schema';
@@ -119,6 +120,68 @@ describe('analyzeMeshQuery', () => {
       expect(result).toEqual({ target: null });
     });
   });
+
+  describe('generic query inference (without explicit persona tags)', () => {
+    it('infers po for status/requirements queries', async () => {
+      const client = fakeClient('{"target": "po"}');
+      const messages = [
+        makeMessage('@isolate- What is the status of this issue?'),
+      ];
+      const result = await analyzeMeshQuery(messages, client);
+      expect(result).toEqual({ target: 'po' });
+    });
+
+    it('infers po for design token queries', async () => {
+      const client = fakeClient('{"target": "po"}');
+      const messages = [
+        makeMessage('@isolate- What design tokens are available for colors?'),
+      ];
+      const result = await analyzeMeshQuery(messages, client);
+      expect(result).toEqual({ target: 'po' });
+    });
+
+    it('infers architect for monorepo structure queries', async () => {
+      const client = fakeClient('{"target": "architect"}');
+      const messages = [
+        makeMessage(
+          '@isolate- How are the presets structured in the monorepo?',
+        ),
+      ];
+      const result = await analyzeMeshQuery(messages, client);
+      expect(result).toEqual({ target: 'architect' });
+    });
+
+    it('infers architect for dependency queries', async () => {
+      const client = fakeClient('{"target": "architect"}');
+      const messages = [
+        makeMessage('@isolate- What dependencies do we use for testing?'),
+      ];
+      const result = await analyzeMeshQuery(messages, client);
+      expect(result).toEqual({ target: 'architect' });
+    });
+
+    it('infers a11y for accessibility compliance queries', async () => {
+      const client = fakeClient('{"target": "a11y"}');
+      const messages = [
+        makeMessage(
+          '@isolate- How do we ensure ARIA compliance in components?',
+        ),
+      ];
+      const result = await analyzeMeshQuery(messages, client);
+      expect(result).toEqual({ target: 'a11y' });
+    });
+
+    it('infers a11y for keyboard navigation queries', async () => {
+      const client = fakeClient('{"target": "a11y"}');
+      const messages = [
+        makeMessage(
+          '@isolate- What keyboard navigation patterns are supported?',
+        ),
+      ];
+      const result = await analyzeMeshQuery(messages, client);
+      expect(result).toEqual({ target: 'a11y' });
+    });
+  });
 });
 
 // ── createMeshRouterNode ──────────────────────────────────────────────────────
@@ -339,3 +402,26 @@ describe('_test_createDefaultMeshClient', () => {
     expect(() => _test_createDefaultMeshClient()).toThrow(/OPENAI_API_KEY/);
   });
 });
+
+describe('MESH_SYSTEM_PROMPT', () => {
+  it('includes all six persona domain descriptions with proper bullet format', () => {
+    const domainBullets = [
+      '- po:',
+      '- architect:',
+      '- dev:',
+      '- a11y:',
+      '- qa:',
+      '- docs:',
+    ];
+
+    domainBullets.forEach((bullet) => {
+      expect(MESH_SYSTEM_PROMPT).toContain(bullet);
+    });
+  });
+
+  it('contains required routing instruction keywords', () => {
+    expect(MESH_SYSTEM_PROMPT).toContain('@isolate-');
+    expect(MESH_SYSTEM_PROMPT).toContain('ONLY');
+    expect(MESH_SYSTEM_PROMPT).toContain('JSON');
+  });
+});

libs/ai-orchestrator/src/orchestrator/mesh-router.ts

@@ -47,15 +47,26 @@ export interface MeshQueryResult {
 
 // ── LLM prompt ────────────────────────────────────────────────────────────────
 
-const MESH_SYSTEM_PROMPT = `You are a routing classifier for an AI multi-agent orchestration system.
+export const MESH_SYSTEM_PROMPT = `You are a routing classifier for an AI multi-agent orchestration system.
 
-Analyze the provided message and determine whether it contains a directed query or request aimed at a specific agent persona.
+Analyze the provided message and identify the target specialist persona, or return null if no cross-persona routing is needed.
 
-Valid persona IDs: po, architect, dev, a11y, qa, docs
+Valid persona IDs and their domains:
+- po: Project status, requirements, design tokens, high-level planning
+- architect: Monorepo structure, tooling, dependencies, presets
+- dev: Code implementation, Panda CSS logic, TypeScript types
+- a11y: Accessibility standards, ARIA, keyboard navigation
+- qa: Test coverage, edge cases, bug verification
+- docs: Documentation, Storybook, usage examples
 
 Rules:
-- Return a non-null target ONLY when the message explicitly asks a question of, or directs a task to, a specific persona — for example: "I need @isolate-po to clarify the token choice" or "Can qa verify this edge case?".
-- Return null for standard work outputs, APPROVED/REJECTED decisions, inline explanations, or any message that does not address a specific persona as the recipient of a query.
+- ONLY for messages starting with @isolate- that contain a query (?): infer the best-fit persona based on question content. For example:
+  * "What is the status of this issue?" → po
+  * "How are the presets structured?" → architect
+  * "How do we ensure ARIA compliance?" → a11y
+- For explicit persona tags (e.g., "@isolate-po", "Can qa verify"): return the explicitly named persona.
+- For ALL OTHER messages (no @isolate- prefix and no explicit persona tag): return null, even if they contain questions.
+- Return null for standard work outputs, APPROVED/REJECTED decisions, and inline explanations.
 
 Respond with ONLY valid JSON in this exact format, with no additional text:
 {"target": "persona_id"}