From prompts to production partnership intelligence.
Monday showed 3 core prompts for partnership discovery, evaluation, and engagement tracking. Tuesday automated them into a working system. Wednesday mapped team workflows across BD, Strategy, and Ops. Today: the complete technical architecture. Multi-agent orchestration, ML feature pipelines, scaling patterns from startup to enterprise, and compliance layers for strategic alliance data.
Key Assumptions
System Requirements
Functional
- Discover potential partners based on strategic fit criteria
- Evaluate partnership opportunities with scoring models
- Track engagement metrics and relationship health
- Generate partnership briefs and executive summaries
- Alert on partnership risks and opportunities
- Integrate with CRM and data warehouse systems
- Support multi-user collaboration with role-based access
Non-Functional (SLOs)
💰 Cost Targets: {"per_partner_tracked_usd":0.5,"per_evaluation_usd":2,"per_user_per_month_usd":50}
Agent Layer
planner
L4Decomposes user requests into subtasks and orchestrates agent execution
🔧 Task decomposition LLM (GPT-4), Agent capability registry, Cost estimator
⚡ Recovery: Retry with simplified plan, Escalate to human if >3 failures, Log failure patterns for retraining
discovery
L3Finds potential partners matching strategic criteria
🔧 Crunchbase API, LinkedIn Sales Navigator API, Internal partner database, Web scraper, Embedding model (text-embedding-3-large)
⚡ Recovery: Fallback to cached results if API down, Expand search criteria if <10 results, Manual review queue if data quality low
evaluation
L3Scores partnership opportunities across strategic fit, financials, and risk
🔧 Feature store (Feast), Scoring models (XGBoost, LightGBM), LLM for rationale generation (Claude), Risk classifier
⚡ Recovery: Use rule-based fallback if ML model fails, Request human review if confidence <0.6, Log low-confidence cases for retraining
engagement
L2Tracks interactions and recommends next steps
🔧 NLP sentiment analyzer, CRM API (Salesforce), Email parser, LLM for summarization (GPT-4)
⚡ Recovery: Manual entry if parsing fails, Default to last known state, Alert BD team if health score drops >20%
evaluator
L4Validates agent outputs for quality and consistency
🔧 Evaluation dataset, Metrics calculator (accuracy, F1, BLEU), Drift detector, Human-in-the-loop interface
⚡ Recovery: Escalate to human reviewer if quality <0.8, Block deployment if critical errors, Auto-rollback if prod metrics degrade
guardrail
L4Enforces policy, redacts PII, and filters unsafe outputs
🔧 PII detector (Presidio), Policy engine (OPA), Content filter (OpenAI Moderation API), Audit logger
⚡ Recovery: Block output if policy violated, Alert security team if repeated violations, Default to conservative filtering
ML Layer
Feature Store
Update: Daily batch + real-time for engagement metrics
- • partner_revenue_growth_12m (float)
- • market_overlap_score (float)
- • tech_stack_compatibility (float)
- • cultural_fit_score (float)
- • geographic_proximity (float)
- • partnership_history_count (int)
- • engagement_frequency_30d (int)
- • sentiment_score_avg (float)
Model Registry
Strategy: Semantic versioning with A/B testing for major versions
- • partnership_scorer_v3
- • risk_classifier_v2
- • engagement_predictor_v1
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 lead to bad partnership recommendations
Medium✓ Mitigation: Multi-layer validation: confidence thresholds, cross-reference with ground truth, human review for low-confidence. Track hallucination rate in production.
⚠️ Data staleness (partnerships change fast)
High✓ Mitigation: Real-time data ingestion for critical sources (news, funding). Daily batch for others. Freshness SLO: 24h for most data, 1h for alerts.
⚠️ API rate limits (Crunchbase, LinkedIn)
Medium✓ Mitigation: Aggressive caching (24h TTL). Fallback to alternative data sources. Upgrade to higher API tiers. Throttle non-critical requests.
⚠️ PII leakage in partnership data
Low✓ Mitigation: Guardrail agent with Presidio. Block outputs if PII detected. Audit all data access. Encrypt PII at rest. Regular compliance audits.
⚠️ Cost explosion at scale
High✓ Mitigation: Cost guardrails per user/request. Cache aggressively. Use cheaper models for simple tasks. Monitor cost per partnership in real-time. Auto-throttle if budget exceeded.
⚠️ Model drift (scoring accuracy degrades)
Medium✓ Mitigation: Weekly offline evaluation. Real-time drift detection (PSI). Auto-retrain if drift >0.2. A/B test new models before full rollout.
⚠️ Vendor lock-in (AWS, OpenAI)
Medium✓ Mitigation: Multi-cloud strategy (AWS primary, GCP backup). Multi-provider LLMs. Abstract infrastructure with Terraform. Test failover quarterly.
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
4 components
API Gateway
4 components
Agent Layer
6 components
ML Layer
5 components
Integration
4 components
Data
4 components
External
4 components
Observability
4 components
Security
4 components
Request Flow - Partnership Evaluation
Automated data flow every hour
End-to-End Data Flow
From user request to partnership recommendation in 3.5 seconds
Key System Integrations
Salesforce CRM
Crunchbase API
Snowflake Data Warehouse
LinkedIn Sales Navigator
Security & Compliance Architecture
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| LLM API down (OpenAI/Anthropic) | Switch to backup provider (Anthropic ↔ OpenAI) | Slight latency increase (100-200ms) | 99.5% (multi-provider redundancy) |
| Discovery agent returns 0 results | Expand search criteria, query cached results | Stale data (up to 7 days old) | 99.0% |
| Evaluation model confidence <0.6 | Route to human reviewer | Increased latency (human review in 24h) | 95.0% (quality over speed) |
| Database connection timeout | Read from replica, queue writes | Read-only mode, eventual consistency | 99.9% |
| Crunchbase API rate limit exceeded | Use cached data, throttle requests | Delayed updates (up to 24h) | 98.0% |
| PII detection fails (guardrail agent) | Block all outputs, alert security team | System unavailable until manual review | 100% (safety first) |
| Feature store unavailable | Use cached features (up to 24h old) | Slightly stale scores | 99.5% |
┌─────────────┐
│ Planner │ ← Orchestrates all agents
│ Agent │
└──────┬──────┘
│
┌───┴────┬────────┬──────────┬──────────┐
│ │ │ │ │
┌──▼──┐ ┌─▼──┐ ┌──▼───┐ ┌──▼────┐ ┌──▼────┐
│Disco│ │Eval│ │Engage│ │Evalua-│ │Guard- │
│very │ │uate│ │ment │ │tor │ │rail │
└──┬──┘ └─┬──┘ └──┬───┘ └──┬────┘ └──┬────┘
│ │ │ │ │
└────────┴────────┴─────────┴──────────┘
│
┌──▼─────┐
│ User │
│Response│
└────────┘🔄Agent Collaboration Flow
🎭Agent Types
Reactive Agent
LowDiscovery Agent - Responds to query, returns results
Reflexive Agent
MediumEvaluation Agent - Uses context (features, history)
Deliberative Agent
HighPlanner Agent - Plans multi-step workflows
Hybrid Agent
HighEngagement Agent - Reactive (fetch data) + Deliberative (recommend next steps)
📈Levels of Autonomy
RAG vs Fine-Tuning Decision
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Cost Optimization
Technology Stack
2026 Randeep Bhatia. All Rights Reserved.
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