From prompts to production HR platform.
Monday: 3 engagement prompts. Tuesday: automation code. Wednesday: team workflows. Thursday: complete technical architecture. Agents, ML pipelines, HRIS integration, real-time analytics, and compliance for 10,000+ employees.
Key Assumptions
System Requirements
Functional
- Ingest employee data from HRIS, surveys, Slack, and email
- Real-time sentiment analysis on feedback and communications
- Automated pulse surveys with intelligent follow-up questions
- Manager dashboards with team health scores and risk alerts
- Predictive attrition modeling with early warning system
- Compliance reporting for HIPAA, SOC2, and data privacy regulations
- Multi-tenant support with data isolation and custom branding
Non-Functional (SLOs)
💰 Cost Targets: {"per_employee_per_month_usd":2.5,"llm_cost_per_analysis_usd":0.003,"total_infra_per_1k_employees_usd":250}
Agent Layer
planner
L4Decomposes tasks, selects tools, routes workflows
🔧 guardrail_agent, executor_agent, hris_connector, survey_tool
⚡ Recovery: Retry with backoff (3x), Fallback to manual review queue, Alert on-call engineer if critical
executor
L3Orchestrates primary workflow, coordinates sub-agents
🔧 sentiment_agent, survey_agent, evaluator_agent, hris_api, slack_api
⚡ Recovery: Checkpoint state at each step, Resume from last checkpoint on failure, Partial results if sub-agent fails, Human escalation for unrecoverable errors
evaluator
L2Validates output quality, detects hallucinations, ensures accuracy
🔧 llm_evaluator, rule_based_validator, ground_truth_db
⚡ Recovery: Default to conservative (fail if uncertain), Human review queue for borderline cases, Log all evaluation decisions for audit
guardrail
L1PII detection, policy enforcement, safety filters
🔧 pii_detection_service, policy_engine, content_filter
⚡ Recovery: Fail-safe: block on detection failure, Alert security team immediately, Log all decisions for compliance audit
survey
L3Generates intelligent follow-up questions based on context
🔧 llm_api, question_template_db, evaluator_agent
⚡ Recovery: Fallback to template questions if generation fails, Human review for sensitive topics, A/B test new questions before full rollout
sentiment
L2Multi-dimensional emotion analysis with context
🔧 sentiment_model, emotion_classifier, context_embedder
⚡ Recovery: Ensemble voting if primary model uncertain, Human review for high-urgency low-confidence cases, Fallback to rule-based sentiment if ML fails
ML Layer
Feature Store
Update: Hourly for real-time features, daily for batch features
- • employee_tenure_days
- • avg_sentiment_30d
- • survey_response_rate
- • manager_1on1_frequency
- • promotion_recency_days
- • peer_feedback_count
- • slack_activity_score
- • pto_usage_rate
- • team_size
- • department_attrition_rate
Model Registry
Strategy: Semantic versioning (major.minor.patch), immutable artifacts
- • sentiment_classifier
- • attrition_predictor
- • question_generator
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ Employee privacy concerns - fear of surveillance
High✓ Mitigation: Transparent opt-in consent, anonymization for analytics, clear data usage policies, regular privacy audits, employee council for oversight
⚠️ Bias in sentiment analysis (gender, age, department)
Medium✓ Mitigation: Bias testing in evaluation framework, stratified datasets, fairness metrics (demographic parity), regular bias audits, human review for high-stakes decisions
⚠️ LLM hallucinations leading to incorrect alerts
Medium✓ Mitigation: Multi-layer hallucination detection, confidence thresholds, human review queue, cross-reference with HRIS ground truth, alert calibration
⚠️ HRIS integration failures causing data staleness
Medium✓ Mitigation: Retry logic with exponential backoff, queue for delayed sync, monitoring and alerting, fallback to cached data, SLA with HRIS vendor
⚠️ Cost overruns from LLM API usage
High✓ Mitigation: Rate limiting, caching, prompt optimization, model distillation, cost monitoring and alerts, budget guardrails per tenant
⚠️ Compliance violations (HIPAA, GDPR, CCPA)
Low✓ Mitigation: PII detection and redaction, data residency controls, audit logging, regular compliance audits, legal review of data practices, employee consent management
⚠️ Model drift causing accuracy degradation
High✓ Mitigation: Automated drift detection, retraining pipelines, shadow mode testing, A/B testing, human feedback loop, quarterly model updates
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale (3-6 months)
Phase 3: Enterprise (6-12 months)
Complete Systems Architecture
9-layer architecture from presentation to security
Presentation
5 components
API Gateway
5 components
Agent Layer
6 components
ML Layer
6 components
Integration
5 components
Data
6 components
External
5 components
Observability
6 components
Security
6 components
Request Flow - Employee Feedback Analysis
Automated data flow every hour
End-to-End Data Flow
Employee feedback → Manager alert in <1 second
Key Integrations
HRIS (Workday/BambooHR/ADP)
Slack/Microsoft Teams
Survey Tools (Qualtrics/SurveyMonkey)
Identity Provider (Okta/Auth0)
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI/Anthropic outage) | Switch to backup LLM provider (multi-LLM strategy) | Degraded accuracy, continued operation | 99.5% |
| Sentiment model low confidence (<0.7) | Route to human review queue | Higher latency for uncertain cases | 99.9% |
| HRIS API timeout/rate limit | Retry with exponential backoff (3x), then queue for later | Delayed sync, eventual consistency | 99.0% |
| PII detection service fails | Block all processing (fail-safe mode) | System unavailable until recovery | 100% (safety first) |
| Database unavailable | Read from replica, write to queue | Read-only mode, writes delayed | 99.9% |
| Feature store latency spike | Use cached features (stale up to 1hr) | Slightly outdated context | 99.5% |
| Agent orchestrator crash | Kubernetes auto-restart, resume from checkpoint | In-flight requests retry | 99.9% |
┌──────────────┐
│ API Gateway │
└──────┬───────┘
│
┌──────▼───────┐
│ Planner │ ← Decomposes tasks, routes workflows
└──────┬───────┘
│
┌───┴────┬─────────┬──────────┬──────────┐
│ │ │ │ │
┌──▼──┐ ┌─▼───┐ ┌──▼────┐ ┌──▼────┐ ┌─▼────┐
│Guard│ │Exec │ │Eval │ │Survey │ │Sentim│
│rail │ │utor │ │uator │ │Agent │ │ent │
└──┬──┘ └──┬──┘ └───┬───┘ └───┬───┘ └──┬───┘
│ │ │ │ │
└────────┴─────────┴──────────┴─────────┘
│
┌──────▼────────┐
│ Feature Store │
│ Model Registry│
└───────────────┘🔄Agent Collaboration Flow
🎭Agent Types
Reactive Agent
LowGuardrail Agent - Responds to input, returns safe/unsafe
Reflexive Agent
MediumSentiment Agent - Uses ML model + context from feature store
Deliberative Agent
HighSurvey Agent - Plans questions based on gaps, considers past responses
Orchestrator Agent
HighestPlanner & Executor - Makes routing decisions, handles loops, manages state
📈Levels of Autonomy
RAG vs Fine-Tuning
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Model Monitoring & Drift Detection
Technology Stack
2026 Randeep Bhatia. All Rights Reserved.
No part of this content may be reproduced, distributed, or transmitted in any form without prior written permission.