From prompts to production IR platform.
Monday: 3 core prompts for investor tracking, update generation, and engagement scoring. Tuesday: automated agent workflows. Wednesday: team collaboration patterns. Thursday: complete technical architecture with agent orchestration, ML pipelines, GDPR compliance, and scaling from 100 to 100K investors monthly.
Key Assumptions
System Requirements
Functional
- Track investor interactions across email, meetings, documents, and events
- Generate personalized weekly/monthly updates for each investor segment
- Score investor engagement and predict likelihood of follow-on investment
- Automate follow-up suggestions based on investor behavior patterns
- Extract and structure data from unstructured investor communications
- Maintain complete audit trail of all investor interactions
- Support multi-fund/multi-portfolio management with access controls
Non-Functional (SLOs)
💰 Cost Targets: {"per_investor_per_month_usd":0.5,"per_update_generated_usd":0.15,"per_engagement_score_usd":0.05}
Agent Layer
planner
L4Decomposes high-level tasks into executable sub-tasks and orchestrates agent collaboration
🔧 TaskDecomposer, AgentRegistry, ResourceEstimator
⚡ Recovery: Retry with simplified plan, Fall back to manual workflow queue, Alert on-call engineer if critical
executor
L3Executes the primary workflow orchestrated by planner, coordinates between specialized agents
🔧 TrackingAgent, ScoringAgent, UpdateGeneratorAgent, DatabaseClient, CRMAdapter
⚡ Recovery: Checkpoint and resume from last successful step, Request replanning from planner agent, Graceful degradation with partial results
evaluator
L3Validates outputs for quality, completeness, and business logic before delivery
🔧 QualityChecker, CompletenessValidator, BusinessRuleEngine, HistoricalComparator
⚡ Recovery: Request regeneration if quality < threshold, Flag for human review if repeated failures, Log quality issues for model retraining
guardrail
L4Enforces safety, compliance, and policy constraints across all agent outputs
🔧 PIIScanner, ContentModerator, PolicyEngine, AuditLogger
⚡ Recovery: Block delivery if critical violation, Auto-redact and retry if PII detected, Escalate to compliance team if uncertain
tracking
L2Captures and structures investor interactions from multiple channels
🔧 EmailParser, NLPExtractor, SentimentAnalyzer, DatabaseWriter
⚡ Recovery: Queue for manual review if extraction confidence < 70%, Retry with alternative parsing strategy, Log parsing failures for model improvement
update_generator
L2Creates personalized investor updates based on recent activity and portfolio performance
🔧 LLMClient (Claude/GPT), TemplateEngine, MetricsAggregator, PersonalizationEngine
⚡ Recovery: Retry with different prompt if quality low, Use template fallback if LLM unavailable, Queue for human editing if repeated failures
scoring
L2Calculates investor engagement scores and predicts likelihood of follow-on investment
🔧 FeatureStore, MLModel (XGBoost/LightGBM), ScoreAggregator, TrendAnalyzer
⚡ Recovery: Use rule-based fallback if ML model unavailable, Return last known score if computation fails, Alert ML team if prediction confidence low
ML Layer
Feature Store
Update: Hourly for real-time features, daily for aggregated features
- • interaction_frequency_7d
- • interaction_frequency_30d
- • avg_response_time_hours
- • meeting_attendance_rate
- • email_open_rate
- • email_click_rate
- • days_since_last_interaction
- • total_interactions_all_time
- • sentiment_score_avg
- • topic_diversity_score
- • investment_history_count
- • fund_performance_percentile
Model Registry
Strategy: Semantic versioning with automated A/B testing for new versions
- • engagement_scorer
- • investment_predictor
- • sentiment_analyzer
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ LLM hallucinations in investor updates (fake data, incorrect facts)
Medium✓ Mitigation: 4-layer validation pipeline (confidence scoring, fact verification, consistency checks, human review). Golden dataset for regression testing. Hallucinations logged and reviewed weekly for prompt improvements.
⚠️ PII leakage to LLM providers (GDPR violation)
Medium✓ Mitigation: Mandatory PII scanning before all LLM calls. Automated redaction. No bypass allowed. Regular audits. Enterprise customers can use private LLM endpoints (AWS Bedrock, Azure OpenAI) with data residency guarantees.
⚠️ Agent autonomy leading to incorrect decisions (wrong investor routing, bad recommendations)
Medium✓ Mitigation: Evaluator agent validates all outputs. Confidence thresholds for autonomous actions. Human-in-the-loop for low-confidence decisions. Agent decision traces logged for post-hoc analysis.
⚠️ Model performance degradation over time (data drift, concept drift)
High✓ Mitigation: Continuous monitoring of prediction accuracy. Automated drift detection (KL divergence on features, performance metrics). Retraining triggered if performance drops >5%. A/B testing for new models before full rollout.
⚠️ Cost overruns from LLM API usage (especially at scale)
High✓ Mitigation: Multi-model routing (use cheaper models for simple tasks). Caching of common queries. Rate limiting per customer. Cost dashboards with alerts. Budget guardrails at API gateway level.
⚠️ Integration failures with CRM/email systems (API changes, rate limits)
Medium✓ Mitigation: Adapter pattern with versioned APIs. Retry logic with exponential backoff. Rate limit monitoring and adaptive throttling. Fallback to manual sync. Integration health dashboard.
⚠️ Compliance violations (GDPR, SOC2) due to inadequate controls
Low✓ Mitigation: Built-in compliance by design. Automated audit trails. Regular compliance audits (quarterly). DPO oversight. Data residency controls. Right to deletion workflows. SOC 2 Type II certification.
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale & Automate (3-6 months)
Phase 3: Enterprise & Global (6-12 months)
Complete Systems Architecture
9-layer architecture from presentation to security
Presentation
4 components
API Gateway
4 components
Agent Layer
7 components
ML Layer
5 components
Integration
4 components
Data
4 components
External
4 components
Observability
4 components
Security
4 components
Request Flow - Generate Investor Update
Automated data flow every hour
End-to-End Data Flow
From investor interaction to personalized update delivery
Key System Integrations
CRM Integration (Salesforce/HubSpot)
Email Service (SendGrid/Postmark)
Calendar Sync (Google/Outlook)
Document Parser (AWS Textract)
Security & Compliance Architecture
Authentication & Authorization
Data Encryption
Privacy & PII Protection
Audit & Compliance
Secrets Management
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (Claude/GPT) | Automatic failover to backup LLM provider → Use cached responses for common queries → Queue for retry | Degraded performance (slower generation), not broken | 99.5% (multi-LLM redundancy) |
| Update generation quality low (<80/100) | Regenerate with different prompt → Use template fallback → Queue for human review | Quality maintained, slight delay | 99% (quality threshold enforced) |
| Database connection lost | Read from replica → Serve from cache → Return cached data with staleness warning | Read-only mode, eventual consistency | 99.9% (multi-AZ deployment) |
| PII detection service unavailable | Block all LLM processing → Queue requests → Alert compliance team | Processing halted (safety first) | 100% (no PII leakage tolerated) |
| Feature store data stale (>6 hours old) | Use last known good features → Compute features on-demand → Alert ML team | Slightly less accurate scores | 99% (freshness SLO: <1 hour) |
| Email delivery failure (bounces, blocks) | Retry with different sender domain → Use alternative email provider → SMS fallback | Delivery via alternative channel | 98% (deliverability target) |
| CRM sync lag (>1 hour behind) | Increase sync frequency → Manual sync trigger → Alert operations team | Slightly stale data in CRM | 95% (sync lag SLO: <15 min) |
┌──────────────┐
│ Planner Agent│ ← Orchestrates all agents
└──────┬───────┘
│
┌───┴────┬─────────┬──────────┬──────────┐
│ │ │ │ │
┌──▼───┐ ┌─▼───┐ ┌──▼────┐ ┌──▼────┐ ┌──▼────┐
│Track │ │Score│ │Update │ │Eval │ │Guard │
│Agent │ │Agent│ │Gen │ │Agent │ │Agent │
└──────┘ └─────┘ └───────┘ └───────┘ └───────┘
│ │ │ │ │
└───────┴─────────┴──────────┴──────────┘
│
┌──▼──────┐
│Executor │ ← Coordinates workflow
│ Agent │
└─────────┘🔄Agent Collaboration Flow
🎭Agent Types
Reactive Agent
Low (Level 1)Tracking Agent - Responds to input (fetch interactions), returns structured output
Reflexive Agent
Medium (Level 2)Scoring Agent - Uses rules + ML model, adapts to context (investor segment)
Deliberative Agent
High (Level 3)Update Generator - Plans content structure, iteratively refines based on quality checks
Orchestrator Agent
Highest (Level 4)Planner + Executor - Makes routing decisions, handles loops and retries, coordinates all agents
📈Levels of Autonomy
RAG vs Fine-Tuning Decision
Hallucination Detection & Mitigation
Evaluation Framework
Dataset Curation & Quality
Agentic RAG (Multi-Step Reasoning)
Multi-Model Routing
Technology Stack
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