### 1) System Architecture Overview | Element | Design Choice | Rationale & Notes | |---------|---------------|-------------------| | Purpose & Goals | • 24 × 7 multilingual support (12 languages) 📈 CSAT ≥ 90%<br>• ≤ 30 s P95 first-response time<br>• 99.5 % monthly service uptime | Aligns with business SLA and brand reputation | | Core Architecture | Hybrid: decentralized task-specialist agents connected through a central Orchestrator (supervisor-agent) plus a fast, stateless Edge Responder for common FAQs/cached answers | Combines resiliency of decentralized micro-services with unified governance & monitoring | | Data & Systems Integration | • Real-time APIs: Order/Shipping, Payments, CRM, Inventory, Loyalty, Fraud<br>• Event bus (Kafka / Pulsar) for state changes<br>• Vector DB (product & policy embeddings)<br>• Auth via OAuth 2.0 / mTLS | Enables low-latency look-ups and strong security | | Key Metrics | Response Time P95, Resolution Rate P95, Language-accuracy BLEU > 0.85, Escalation Rate < 4 %, Orchestrator CPU < 60 % | All metrics reported to Grafana / Datadog dashboards | --- ### 2) Agent Roles & Responsibilities | # | Name / Role | Core Responsibilities | Inputs | Outputs | Tool Permissions | |---|--------------|-----------------------|--------|---------|------------------| | 1 | Edge Responder | Serve cached FAQ / order-status answers in < 3 s | User message; cache | Final answer or “pass” | Read-only cache | | 2 | Language Detector | Identify user language & locale confidence ≥ 99% | Raw user text | ISO-639-1 code + confidence | None | | 3 | Intent Classifier | Map to ≥ 200 intents, detect urgency/fraud | Lang-tagged text | Intent label, urgency score | None | | 4 | Sentiment & Priority Analyzer | Determine sentiment, VIP/Loyalty tier | User text, CRM VIP flag | Sentiment (-1…1), priority | CRM read | | 5 | Policy Checker | Validate compliance (refund limits, KYC) | Candidate response, user data | “ok” / violation + reason | Policy DB read | | 6 | Knowledge Retriever | RAG search over product + policy embeddings | Intent, context | Ranked passages | Vector DB read | | 7 | Response Generator | Compose answer, tone-match language | Draft context package | Draft response | LLM write | | 8 | Translator | Neural MT ↔ English pivot when needed | Draft response + target lang | Final localized text | None | | 9 | Quality Gatekeeper | Run hallucination, toxicity, brand-tone checks | Localized draft | Approved/Reject + issues | Safety model read | |10 | Escalation Agent | Decide human hand-off, generate ticket | Context, Gatekeeper result | Escalation ticket / none | Zendesk write | |11 | Learning Observer | Sample 1 % sessions, gather feedback loops | Logs, CSAT, human corrections | Model fine-tune dataset | Storage write | |12 | Orchestrator (Supervisor) | Route tasks, maintain conversation state, enforce SLAs, retries | All intermediate outputs | Orchestration decisions | Full read/write | Role-Based Access Controls are enforced via mTLS-authenticated service accounts; each agent gets the minimum API scopes shown above. --- ### 3) Workflow Orchestration 1. Event Trigger: Web / app / email / social inquiry lands in message queue. 2. Edge Responder (async, 5 ms SLA) checks cache; if hit → respond & log. 3. Orchestrator launches parallel Language Detector + Intent Classifier. 4. Sentiment Analyzer runs; Orchestrator tags conversation context. 5. In parallel: Knowledge Retriever pulls facts (RAG). CRM / Order API look-ups (via Orchestrator service plug-ins). 6. Orchestrator packages context → Response Generator (LLM). 7. Draft flows through Policy Checker → Translator (if user language ≠ en) → Quality Gatekeeper. 8. Decision Point: If QA ✅ and urgency < threshold → deliver reply. Else → Escalation Agent creates human ticket; user receives interim apology + ETA notice. 9. Learning Observer streams anonymized interaction + outcome for retraining. Parallelism: steps 3-5 all execute concurrently; Orchestrator awaits fastest quorum. Failure paths: Any agent error → retry × 2; on persistent failure Orchestrator invokes degraded path: cached answer → human escalation. Orchestrator health degraded → traffic automatically re-routed to hot-standby via load balancer. --- ### 4) Communication Protocols Message Envelope (JSON) ```json { "conv_id": "uuid", "turn": 4, "agent": "IntentClassifier", "payload": {...}, "trace": ["EdgeResponder", "Orchestrator"] } ``` Prompt Pattern (Agent-to-Agent) ``` [ROLE]: {agent_role} [INPUT]: {canonical_json_payload} [CONTEXT]: {conversation_snippet} [TASK]: {specific_instruction} [FORMAT]: {required_schema} ``` Shared Context Store: Redis-backed TTL = 30 min keyed by `conv_id`. Conflict Resolution: Orchestrator applies majority-vote or precedence list (Policy Checker > Knowledge Retriever > Sentiment) and logs discrepancies. --- ### 5) Error Handling & Resilience | Layer | Mechanism | |-------|-----------| | Transport | gRPC with deadline & automatic back-off retries | | Agent Health | Heartbeat every 5 s; absence > 15 s triggers eviction & container restart | | Circuit Breakers | Trip after 5 consecutive failures; fallback to cache or human | | Monitoring | Prometheus metrics → Alertmanager (pager during P1) | | Escalation Path | Orchestrator → Escalation Agent → Tier-1 human within 2 min; auto-escalate to Tier-2 if idle > 10 min | --- ### 6) Scalability & Performance Kubernetes Horizontal Pod Autoscaler per agent (CPU / RPS targeting) Load Balancing: Envoy service mesh with locality-aware routing (Americas, EMEA, APAC). GPU Pools: Shared A100 cluster; Response Generator deploys 3 replicas per region. Caching: Edge-FAQ Redis cluster (99 % hit for top 300 questions). JWT Auth & user profile cache 15 min TTL. Bottleneck Mitigation: Intent & Language models distilled and quantized → < 20 ms inference. Async I/O for external API calls. Batch translation when >1 msg/sec in same language. --- ### 7) Testing & Quality Assurance | Phase | Scope | Benchmarks | |-------|-------|-----------| | Unit | Each agent mocked I/O; aim ≥ 95 % code coverage | Latency ≤ 10 ms where applicable | | Integration | Full workflow in staging incl. API sandboxes | End-to-end P95 ≤ 25 s | | System / Load | 15k req/min, 20 min soak | No error-rate spike > 0.3 % | | Multilingual QA | BLEU, COMET, human linguist review | BLEU > 0.85 all langs | | A/B & Shadow | New model behind feature flag; shadow 5 % traffic | CSAT delta ≥ +1 pt | Real-time dashboards + nightly red-team adversarial prompts feed Learning Observer for tuning. --- ### 8) Implementation Roadmap | Quarter | Milestones | Dependencies / Risks | |---------|------------|----------------------| | Q1 | • Architectural spec sign-off<br>• PoC: Orchestrator + 3 core agents (English only)<br>• CI/CD pipeline setup | Staffing, GPU budget approval | | Q2 | • Add multilingual pipeline (6 languages)<br>• Edge Responder cache<br>• Beta in 1 region (Americas) | Legal review of data localization | | Q3 | • Scale to 12 languages<br>• Full policy checker & Gatekeeper<br>• Global region deployment with follow-the-sun failover | Vendor MT quality | | Q4 | • Adaptive learning loop live<br>• Achieve SLA 99.5 % for 90 days<br>• Decommission legacy FAQ bot | Holiday traffic spike | | Continuous | Weekly model refresh, monthly chaos drills, quarterly security pen-tests | Governance board engagement | --- Key Take-aways Explicit specialization keeps each agent small, testable, and replaceable. Hybrid orchestration balances global governance with localized resilience. End-to-end observability (metrics, traces, logs) underpins the 99.5 % uptime target. Parallel, cache-first design is essential to hold the 30-second P95 response-time SLA at 10k+ daily inquiries.