How I'm Building an Enterprise Observability Platform in 30 Days Using Claude Code
The Impossible Timeline Challenge
What if I told you I'm building an enterprise-grade, AI-native observability platform from scratch in 30 days? A project that would traditionally require a team of 10+ developers working for 12+ months. Sounds impossible, right?
Today marks Day 1 of this ambitious journey, and I'm documenting every step to show how modern AI development tools—specifically Claude Code—combined with documentation-driven development can compress traditional development timelines by 10x or more.
The Vision: AI-Native Observability, Not Bolt-On AI
Most observability platforms today bolt AI features onto existing architectures. I'm taking a fundamentally different approach: building an AI-native platform where machine learning is integrated at the core, not as an afterthought.
Key Features:
- Real-time anomaly detection using autoencoders trained on your telemetry data
- LLM-generated dashboards that adapt to your role and usage patterns
- Self-healing configuration management that fixes issues before they impact your applications
- Multi-model AI orchestration (GPT, Claude, local Llama) for cost-optimized intelligence
- No Grafana required - the platform generates React components dynamically
The goal? An observability platform that doesn't just show you what happened—it predicts what will happen and fixes problems automatically.
The Documentation-Driven Development Secret Weapon
Here's the key insight that makes this timeline possible: Start with documentation, not code.
Traditional development flows:
- Write code
- Test code
- Document code (maybe)
- Maintain divergent docs and code
My approach with Claude Code:
- Write detailed specifications in Dendron notes
- Generate code from specifications using Claude Code
- Keep docs and code in sync bidirectionally
- Evolve architecture through documentation updates
This isn't just faster—it's fundamentally more maintainable.
Day 1 Setup: The Foundation for Speed
VSCode + Dendron: The Documentation Engine
I started by setting up a Dendron workspace in VSCode. Dendron isn't just note-taking; it's a knowledge management system that creates a living, interconnected documentation vault.
notes/
├── daily/ # Daily development journals
├── packages/ # Package specifications
│ ├── storage/ # Clickhouse + S3 integration
│ ├── ai-analyzer/ # Anomaly detection engine
│ ├── llm-manager/ # Multi-model orchestration
│ ├── ui-generator/ # React component generation
│ └── config-manager/ # Self-healing configs
├── design/ # Architecture decisions
│ └── adr/ # Architecture Decision Records
└── templates/ # Note templates
Every package starts as a detailed specification before a single line of code is written. This creates a blueprint that Claude Code can follow with precision.
The CLAUDE.md Strategy
I created a comprehensive CLAUDE.md file that serves as a guide for future Claude Code sessions. This file includes:
- Development workflow (documentation-first approach)
- Architecture patterns (Effect-TS for complex async operations)
- OpenTelemetry integration patterns
- Code quality standards (TypeScript strict mode, 80% test coverage)
- Build system (Bazel with OTel demo integration)
This ensures every Claude Code session starts with full context about the project's architecture and conventions.
Effect-TS: Handling Complex Async Operations
One crucial architectural decision: using Effect-TS for the data processing layer. Observability platforms involve complex async operations, error handling, and data transformations. Effect-TS provides:
- Structured error handling with tagged union types
- Streaming data processing with backpressure management
- Resource management with automatic cleanup
- Dependency injection for clean service composition
- Scheduled operations for batch processing
This choice multiplies the effectiveness of AI code generation by providing a solid foundation for complex operations.
The Package Architecture: Six Core Services
Today I designed six core packages that form the foundation of the AI-native platform:
1. Storage Package
- Clickhouse for real-time analytics
- S3/MinIO for raw data storage
- OTLP ingestion directly from OpenTelemetry Collector
- AI-optimized queries for machine learning workflows
2. AI Analyzer Package
- Autoencoder engines for anomaly detection
- Real-time processing with Effect Streams
- Batch training with scheduled model updates
- Pattern recognition across traces, metrics, and logs
3. LLM Manager Package
- Multi-model support (GPT, Claude, local Llama)
- Intelligent routing based on task type and cost
- Conversation management with context preservation
- Fallback strategies for high availability
4. UI Generator Package
- React component generation from LLM prompts
- Role-based templates (DevOps, SRE, Developer)
- Apache ECharts integration for advanced visualizations
- Real-time personalization based on user behavior
5. Config Manager Package
- AI-powered drift detection for configuration changes
- Automated remediation with safety validation
- Multi-layer safety checks (syntax, semantic, security, impact)
- Rollback capabilities for failed changes
6. Deployment Package
- Bazel build system for reproducible builds
- Single-command deployment across Docker/K8s/OpenShift/Rancher
- OTel demo integration for immediate value
- Health monitoring with readiness probes
Why This Approach Works: The Claude Code Advantage
Claude Code isn't just a coding assistant—it's a development multiplier when combined with documentation-driven development:
Precision Through Specification
Instead of vague prompts like "build an observability platform," I provide detailed specifications with:
- TypeScript interfaces with Effect-TS patterns
- Error handling strategies with tagged union types
- Performance requirements and benchmarks
- Integration patterns with specific libraries
Bidirectional Sync
The magic happens in the feedback loop:
- Generate code from detailed specifications
- Analyze generated code to update documentation
- Evolve specifications based on implementation learnings
- Regenerate improved code from updated specs
This creates a virtuous cycle where both code and documentation improve together.
Context Preservation
The CLAUDE.md file ensures every AI session has full project context. Claude Code understands:
- Architectural decisions and the reasoning behind them
- Code patterns and conventions to follow
- Integration requirements with existing systems
- Quality standards and testing approaches
The 30-Day Roadmap
Week 1: Foundation (Days 1-7)
- Complete package specifications ✅ (Day 1 complete!)
- Generate core infrastructure code
- Set up Bazel build system with OTel demo
- Implement basic Clickhouse storage layer
Week 2: AI Integration (Days 8-14)
- Implement autoencoder anomaly detection
- Build LLM manager with multi-model support
- Create real-time processing pipelines
- Add batch training capabilities
Week 3: Dynamic UI (Days 15-21)
- Build React component generation system
- Implement role-based templates
- Add personalization engine
- Create Apache ECharts integrations
Week 4: Self-Healing (Days 22-30)
- Implement configuration management
- Add automated remediation
- Build safety validation systems
- Complete end-to-end testing
Day 1 Results: The Foundation is Set
In a single day, I've:
- ✅ Designed complete package architecture with six core services
- ✅ Created detailed specifications with Effect-TS integration
- ✅ Established development workflow with documentation-driven approach
- ✅ Set up project structure with Dendron knowledge management
- ✅ Documented architectural decisions for future sessions
Traditional development would have taken weeks just to reach architecture consensus with a team. Documentation-driven development with Claude Code compressed this to hours.
The Broader Implications
This experiment isn't just about building an observability platform—it's about demonstrating a new paradigm for software development:
For Individual Developers
- 10x productivity gains through AI-assisted development
- Reduced cognitive load by focusing on architecture over implementation
- Better documentation through documentation-driven workflows
- Faster iteration cycles with bidirectional sync
For the Industry
- Democratized complex software development for smaller teams
- Higher quality codebases through specification-driven generation
- Reduced technical debt through maintained documentation
- Accelerated innovation cycles
What's Next?
Tomorrow (Day 2), I'll start generating actual code from these specifications. I'll show exactly how Claude Code transforms detailed documentation into production-ready TypeScript with Effect-TS patterns.
Follow along as I document this 30-day journey. Whether this succeeds spectacularly or fails instructively, you'll see every step of pushing the boundaries of AI-assisted development.
Want to try this approach yourself?
- Set up Dendron for documentation management
- Install Claude Code for AI-assisted development
- Start with detailed specifications before writing any code
- Use Effect-TS for complex async operations
- Create comprehensive CLAUDE.md files for context preservation
Following the journey:
- GitHub repo: otel-ai
- Daily updates: [Follow this series]
- Architecture decisions: [Documented in ADRs]
The future of software development is here. It's collaborative, AI-native, and documentation-driven. Let's build it together.
Day 1 complete. 29 days to go. The foundation is set—now let's build something extraordinary.
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