Strategic Planning System Architecture: OKR Tracking & Analytics

From prompts to production strategic planning system.

Monday: 3 core prompts for OKR tracking. Tuesday: automated dashboard code. Wednesday: team workflows across strategy roles. Thursday: complete technical architecture with multi-agent orchestration, ML insights, and enterprise-grade scaling.

Key Assumptions

•Organization tracks 50-500 OKRs across 5-20 departments
•Daily progress updates from 10-10,000 active users
•Integration with existing tools (Notion, Asana, Jira, Slack)
•SOC2 Type II compliance required for enterprise customers
•Multi-tenant SaaS with data isolation and optional private deployment

System Requirements

Functional

OKR creation, tracking, and hierarchical rollup (company → department → team → individual)
Automated progress calculation from integrated data sources (Jira tickets, CRM deals, analytics)
ML-driven risk detection (at-risk OKRs, blocked dependencies, resource constraints)
Real-time dashboards with drill-down capabilities
Automated reporting (weekly summaries, quarterly reviews, annual planning)
Natural language query interface for executives (e.g., 'Show me at-risk Q3 OKRs')
Collaboration features (comments, @mentions, approvals, version history)

Non-Functional (SLOs)

latency p95 ms500

freshness min15

availability percent99.9

data accuracy percent99.5

💰 Cost Targets: {"per_user_per_month_usd":15,"ml_inference_per_1k_queries_usd":2,"storage_per_gb_per_month_usd":0.5}

Agent Layer

planner

Decompose user requests into subtasks and select appropriate agents/tools

🔧 Query parser, Permission checker, Agent registry lookup

⚡ Recovery: If parsing fails → fallback to keyword search, If no suitable agent → route to human support queue, Retry with simplified plan if initial plan fails

executor

Execute the primary workflow (OKR updates, progress calculation, data sync)

🔧 Database writer, Integration APIs (Notion, Jira), Progress calculator, Rollup aggregator

⚡ Recovery: Transaction rollback on partial failure, Retry with exponential backoff (3x max), Queue for manual review if all retries fail

evaluator

Validate outputs for quality, accuracy, and completeness

🔧 Schema validator, Anomaly detector (ML model), Consistency checker

⚡ Recovery: If validation fails → block output and alert, If anomaly detected → flag for human review, Log all validation failures for retraining

guardrail

Enforce policies, redact PII, apply safety filters

🔧 PII detector (AWS Comprehend/Presidio), Policy engine, Content filter

⚡ Recovery: If PII detected → block output and redact, If policy violation → reject request and log, Escalate high-risk content to security team

query

Parse natural language queries and generate SQL/filters

🔧 NL-to-SQL model (fine-tuned GPT-4), Permission filter injector, Query optimizer

⚡ Recovery: If SQL generation fails → fallback to keyword search, If query timeout → simplify and retry, Cache common queries for fast path

insight

Generate ML-driven insights, risk alerts, and recommendations

🔧 Risk prediction model, Insight generator (LLM), Recommendation engine

⚡ Recovery: If model inference fails → use rule-based fallback, If LLM timeout → return cached insights, Flag low-confidence insights for human review

ML Layer

Feature Store

Update: Hourly for real-time features, daily for aggregates

• okr_progress_velocity (change per week)
• historical_completion_rate (by owner)
• dependency_count
• team_utilization_percent
• external_blocker_count
• comment_sentiment_score

Model Registry

Strategy: Semantic versioning with A/B testing for new models

• risk_predictor
• progress_forecaster
• insight_generator

Observability

Metrics

📊 api_latency_p50_ms
📊 api_latency_p95_ms
📊 api_latency_p99_ms
📊 agent_execution_time_ms
📊 ml_inference_latency_ms
📊 okr_update_rate_per_min
📊 integration_sync_success_rate
📊 cache_hit_rate
📊 db_query_time_ms
📊 error_rate_percent

Dashboards

📈 ops_dashboard
📈 ml_dashboard
📈 business_metrics_dashboard
📈 security_audit_dashboard

Traces

✅ Enabled

Deployment Variants

🚀 Startup

Infrastructure:

• Vercel (frontend + API routes)
• Supabase (PostgreSQL + auth)
• Upstash Redis (cache)
• OpenAI API (direct)
• GitHub Actions (CI/CD)

→ Deploy in <1 day

→ No DevOps required

→ Pay-as-you-go pricing

→ Single-tenant by default

→ Cost: $200-800/month for 100 users

🏢 Enterprise

Infrastructure:

• AWS EKS or GKE (Kubernetes)
• Aurora PostgreSQL (multi-region)
• ElastiCache Redis (cluster mode)
• SageMaker (model serving)
• Private VPC with VPN/Direct Connect
• BYO KMS for encryption keys
• SSO via SAML 2.0
• Dedicated support + SLA

→ Multi-tenant with VPC isolation

→ Data residency (EU, US, APAC)

→ SOC2 Type II certified

→ Custom compliance (HIPAA, FedRAMP available)

→ Cost: $10K-50K/month for 10K+ users

📈 Migration: Start with startup stack. Migrate to Kubernetes when >1,000 users. Add multi-region when >5,000 users. Private cloud for enterprise contracts.

Risks & Mitigations

⚠️ LLM hallucinations in executive insights

Medium

✓ Mitigation: 4-layer hallucination detection. Human review for high-stakes insights. Confidence scores shown to users.

⚠️ Integration API rate limits (Notion, Jira)

High

✓ Mitigation: Implement backoff/retry. Cache aggressively. Batch requests. Offer webhook alternatives.

⚠️ Multi-tenant data leakage

Low

✓ Mitigation: Row-level security in DB. VPC isolation for enterprise. Automated security testing. Annual penetration tests.

⚠️ ML model drift (accuracy degradation)

Medium

✓ Mitigation: Weekly drift monitoring. Automated retraining pipeline. A/B testing before rollout. Fallback to rule-based system.

⚠️ Scaling costs (LLM API usage)

High

✓ Mitigation: Aggressive caching. Batch requests. Use smaller models where possible. Cost alerts at $X/day.

⚠️ Single LLM provider dependency

Medium

✓ Mitigation: Multi-provider setup (OpenAI + Anthropic). Automatic failover. Standardized prompt interface.

⚠️ Complex agent orchestration failures

Medium

✓ Mitigation: Extensive testing. Simplified fallback paths. Circuit breakers. Comprehensive logging for debugging.

Evolution Roadmap

Phase 1: MVP (0-3 months)

Months 0-3

→ Launch core OKR tracking
→ Basic progress calculation
→ Notion/Jira integration
→ Simple dashboard

Phase 2: Intelligence (3-6 months)

Months 3-6

→ Add ML-driven risk prediction
→ Multi-agent orchestration
→ Automated insights
→ Advanced analytics

Phase 3: Enterprise (6-12 months)

Months 6-12

→ Multi-tenant with isolation
→ Enterprise compliance (SOC2)
→ Advanced integrations
→ Global scaling

Complete Systems Architecture

9-layer architecture from presentation to security

Presentation

Web Dashboard (React/Next.js)

Mobile App (React Native)

Slack Bot

Email Digest

API Gateway

Load Balancer (ALB/NGINX)

Rate Limiter (Redis)

Auth Middleware (OIDC/SAML)

API Router

Agent Layer

Planner Agent

Executor Agent

Evaluator Agent

Guardrail Agent

Query Agent

Insight Agent

ML Layer

Feature Store (Feast/Tecton)

Model Registry (MLflow)

Inference Service

Evaluation Pipeline

Integration

Notion Sync

Asana/Jira Adapter

Analytics Connector

Slack Webhook

Data

PostgreSQL (transactional)

Redis (cache)

S3 (archive)

Vector DB (embeddings)

External

LLM APIs (OpenAI/Anthropic)

Notion API

Jira API

Analytics APIs

Observability

Metrics (Prometheus)

Logs (CloudWatch/Datadog)

Traces (Jaeger)

Dashboards (Grafana)

Security

IAM/RBAC

KMS (secrets)

Audit Logger

WAF

Request Flow - Executive Query

Strategic Planning System - Hub Orchestration

8 Components

HTTP

REST

gRPC

Event

Stream

WebSocket

Strategic Planning System - Feedback & Refinement Loops

8 Components

HTTP

REST

gRPC

Event

Stream

WebSocket

End-to-End Data Flow

From OKR update to executive dashboard

User0ms

Updates key result progress → New value + comment

API Gateway10ms

Authenticates & rate limits → Validated request

Guardrail Agent50ms

Checks for PII in comment → Redacted comment

Executor Agent30ms

Writes to database → Updated record

Executor Agent80ms

Triggers rollup calculation → Parent OKR progress

ML Layer20ms

Updates feature store → New velocity metric

ML Layer40ms

Runs risk prediction → Risk score (0.72)

Evaluator Agent15ms

Validates risk score → Confidence: high

Insight Agent200ms

Generates alert if at-risk → Alert message

Integration Layer100ms

Sends Slack notification → Alert posted

Dashboard50ms

Refreshes real-time view → Updated UI

Scaling Patterns

Volume

10-100 users

Pattern

Monolith + Managed Services

Architecture

• Single Next.js app (Vercel)

• Managed PostgreSQL (Supabase/RDS)

• Redis cache (Upstash)

• OpenAI API (direct calls)

Cost

$200/month

300-500ms p95

Volume

100-1,000 users

Pattern

Microservices + Queue

Architecture

• API Gateway + App servers

• Message queue (SQS/RabbitMQ)

• Worker pool (3-5 instances)

• PostgreSQL + read replicas

• Redis cluster

Cost

$800/month

200-400ms p95

Volume

1,000-10,000 users

Pattern

Multi-Agent + ML Pipeline

Architecture

• Load balanced API (ALB)

• Agent orchestration (LangGraph)

• Feature store (Feast)

• Model serving (SageMaker/Vertex)

• Multi-region DB (Aurora Global)

• CDN (CloudFront)

Cost

$3,000/month

100-300ms p95

Volume

10,000+ users (Enterprise)

Pattern

Multi-Tenant + Private Cloud

Architecture

• Kubernetes (EKS/GKE)

• Tenant isolation (VPC per customer)

• Dedicated DB instances

• Private LLM endpoints

• BYO KMS/HSM

• SSO/SAML integration

Cost

$10,000+/month

50-200ms p95

Key Integrations

Notion

Protocol: REST API v1

User connects Notion workspace

Sync OKRs from Notion database

Bi-directional updates (every 15 min)

Conflict resolution (last-write-wins)

Jira

Protocol: REST API v3

Map Jira projects to OKRs

Sync ticket status → KR progress

Webhook for real-time updates

Custom field mapping

Google Analytics / Mixpanel

Protocol: REST API

Define metric queries (e.g., DAU, conversion rate)

Fetch daily → update KR current_value

Automated rollup to parent OKRs

Slack

Protocol: Slack API + Webhooks

Send alerts to configured channels

Slash commands (/okr status)

Interactive buttons (approve/reject)

Thread updates for comments

Security & Compliance

Failure Modes & Recovery

Failure	Fallback	Impact	SLA
LLM API down (OpenAI outage)	Switch to Anthropic Claude (multi-provider setup)	Slight latency increase, no data loss	99.9%
Database connection timeout	Read from replica, queue writes	Read-only mode for <5 minutes	99.95%
Integration API down (Jira/Notion)	Queue sync jobs, retry with backoff	Delayed updates (max 1 hour)	99.5%
ML model inference timeout	Use rule-based risk scoring	Lower accuracy (85% vs 92%)	99.0%
Cache (Redis) unavailable	Direct DB queries (slower)	Latency increases 2-3x	99.9%
Agent orchestration failure	Retry with simplified plan	Degraded functionality, core features work	99.5%
PII detection service down	Block all processing (safety first)	Temporary service unavailable	100% (no PII leaks)

Multi-Agent Collaboration

How specialized agents work together autonomously

┌──────────────┐
│   Planner    │ ← Orchestrates all agents
│    Agent     │
└──────┬───────┘
       │
   ┌───┴────┬──────────┬──────────┬──────────┐
   │        │          │          │          │
┌──▼──┐  ┌─▼──┐  ┌────▼────┐  ┌──▼────┐  ┌──▼────┐
│Exec │  │Eval│  │Guardrail│  │Query  │  │Insight│
│Agent│  │Agent│  │ Agent   │  │Agent  │  │Agent  │
└──┬──┘  └─┬──┘  └────┬────┘  └──┬────┘  └──┬────┘
   │        │          │          │          │
   └────────┴──────────┴──────────┴──────────┘
              │
           ┌──▼─────┐
           │  Data  │
           │ Layer  │
           └────────┘

Agent Collaboration Flow

Planner Agent

Receives user request → Decomposes into tasks → Routes to appropriate agents

Query Agent

If NL query → Parse to SQL → Fetch data

Executor Agent

If update → Validate → Write to DB → Trigger rollup

Evaluator Agent

Validates output → Checks quality → Flags anomalies

Guardrail Agent

Scans for PII → Redacts if found → Applies policies

Insight Agent

If executive query → Generate insights → Recommend actions

Planner Agent

Aggregates results → Returns to user

Reactive Agent

Executor Agent - Responds to commands, no planning

Autonomy: LowStateless

Reflexive Agent

Evaluator Agent - Uses rules + context

Autonomy: MediumReads context

Deliberative Agent

Query Agent - Plans query strategy, iterates

Autonomy: HighStateful

Orchestrator Agent

Planner Agent - Coordinates all agents, makes routing decisions

Autonomy: HighestFull state management

Levels of Autonomy

Tool

Human calls, agent responds

→ Monday's prompts

Chained Tools

Sequential execution

→ Tuesday's automation

Agent

Makes decisions, can loop

→ Query Agent parsing NL

Multi-Agent

Agents collaborate autonomously

→ This system

Advanced ML/AI Patterns

Production ML engineering beyond basic API calls

RAG vs Fine-Tuning

Hallucination Detection

LLMs hallucinate OKR progress or fake dependencies

Confidence scores from model (<0.8 = flag)

Cross-reference with source data (Jira, Analytics)

Logical consistency (progress can't exceed 100%)

Human review for low-confidence insights

0.5% hallucination rate, 98% caught before user sees

Evaluation Framework

Risk Prediction Accuracy

92.3%target: 90%+

Insight Relevance (user rating)

4.2/5target: 4.0/5

Forecast Error (MAPE)

12.1%target: <15%

Recommendation Acceptance Rate

34%target: >30%

Testing: A/B testing with 10% holdout. Weekly evaluation on new data.

Dataset Curation

Collect: 50K historical OKRs - Export from pilot customers

Clean: 42K usable - Remove duplicates, incomplete records

Label: 10K labeled - ($$25K)

Augment: +5K synthetic - Edge case generation (at-risk scenarios)

→ 15K high-quality examples. Inter-annotator agreement: 0.89 (Cohen's Kappa)

Agentic RAG

Query Agent iteratively retrieves based on reasoning

Executive asks 'Why is Marketing OKR at risk?' → RAG retrieves OKR data → Agent reasons 'need dependency info' → RAG retrieves blockers → Agent reasons 'need team capacity' → RAG retrieves utilization → Insight generated with full context

💡 Not one-shot retrieval. Agent decides what context it needs iteratively.

Model Drift Detection

Technology Stack

Frontend

Next.js 14, React, TypeScript, Tailwind CSS

Backend

Node.js, Python (ML services), FastAPI

Database

PostgreSQL (Aurora), Redis (ElastiCache)

LLMs

GPT-4 (OpenAI), Claude 3.5 Sonnet (Anthropic)

Orchestration

LangGraph, CrewAI (optional)

ML Platform

Feast (feature store), MLflow (registry), SageMaker (inference)

Message Queue

SQS (AWS), RabbitMQ (self-hosted option)

Compute

Serverless (Lambda/Cloud Run) or Kubernetes (EKS/GKE)

Monitoring

Datadog (metrics, logs, traces), Sentry (errors)

Security

Auth0 (SSO), AWS KMS (secrets), Presidio (PII detection)

CI/CD

GitHub Actions, Terraform (IaC)

🏗️

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Strategic Planning System Architecture 🏗️

From prompts to production strategic planning system.

Key Assumptions

System Requirements

Functional

Non-Functional (SLOs)

Agent Layer

planner

executor

evaluator

guardrail

query

insight

ML Layer

Feature Store

Model Registry

Observability

Metrics

Dashboards

Traces

Deployment Variants

🚀 Startup

🏢 Enterprise

Risks & Mitigations

⚠️ LLM hallucinations in executive insights

⚠️ Integration API rate limits (Notion, Jira)

⚠️ Multi-tenant data leakage

⚠️ ML model drift (accuracy degradation)

⚠️ Scaling costs (LLM API usage)

⚠️ Single LLM provider dependency

⚠️ Complex agent orchestration failures

Evolution Roadmap

Phase 1: MVP (0-3 months)

Phase 2: Intelligence (3-6 months)

Phase 3: Enterprise (6-12 months)

Complete Systems Architecture

Request Flow - Executive Query

Strategic Planning System - Hub Orchestration

Strategic Planning System - Feedback & Refinement Loops

End-to-End Data Flow

Scaling Patterns

Key Integrations

Notion

Jira

Google Analytics / Mixpanel

Slack

Security & Compliance

Failure Modes & Recovery

Multi-Agent Collaboration

Agent Collaboration Flow

Reactive Agent

Reflexive Agent

Deliberative Agent

Orchestrator Agent

Levels of Autonomy

Advanced ML/AI Patterns

RAG vs Fine-Tuning

Hallucination Detection

Evaluation Framework

Dataset Curation

Agentic RAG

Model Drift Detection

Technology Stack

Need Architecture Review for Your Strategic Planning System?