feat(ssa): Agent channel UX optimization (Solution B) + Plan-and-Execute architecture design

SSA Agent channel improvements (12 code files, +931/-203 lines):
- Solution B: left/right separation of concerns (gaze guiding + state mutex + time-travel)
- JWT token refresh mechanism (ensureFreshToken) to fix HTTP 401 during pipeline
- Code truncation fix: LLM maxTokens 4000->8000 + CSS max-height 60vh
- Retry streaming code generation with generateCodeStream()
- R Docker structured errors: 20+ pattern matching + format_agent_error + line extraction
- Prompt iron rules: strict output format in CoderAgent System Prompt
- parseCode robustness: XML/Markdown/inference 3-tier matching + length validation
- consoleOutput type defense: handle both array and scalar from R Docker unboxedJSON
- Agent progress bar sync: derive phase from agentExecution.status
- Export report / view code buttons restored for Agent mode
- ExecutingProgress component: real-time timer + dynamic tips + step pulse animation

Architecture design (3 review reports):
- Plan-and-Execute step-by-step execution architecture approved
- Code accumulation strategy (R Docker stays stateless)
- 5 engineering guardrails: XML tags, AST pre-check, defensive prompts, high-fidelity schema, error classification circuit breaker

Docs: update SSA module status v4.1, system status v6.7, deployment changelist
Made-with: Cursor

backend/src/modules/ssa/routes/chat.routes.ts

@@ -139,6 +139,7 @@ export default async function chatRoutes(app: FastifyInstance) {
 
         const result = await chatHandlerService.handleAgentMode(
           sessionId, conversationId, content.trim(), writer, placeholderMsgId,
+          metadata,
         );
 
         logger.info('[SSA:Chat] Agent mode request completed', {