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NEXT STEPS AND IMPLEMENTATION ROADMAP

Documentation for NEXT_STEPS_AND_IMPLEMENTATION_ROADMAP from the Pipeline ex repository.

Next Steps and Implementation Roadmap - Mind Dump

Immediate Actions (Week 1-2)

Critical Path Items

  • Schema Validation Crisis: We have NO formal schema validation! This is a ticking time bomb. Need to implement Exdantic-based validation ASAP before someone ships a broken pipeline to production
  • JSON-First Architecture: The YAML/JSON conversion is foundational - without it, LLM integration is a pipe dream. Build the conversion layer NOW
  • State Management Overhaul: Current map-based state is a mess. Need structured state with Exdantic schemas yesterday
  • Version the YAML Format: We’re on v2 but have no migration path from v1. What happens when we need v3?

Quick Wins

  • Add JSON Schema generation to all existing pipelines
  • Create a simple pipeline validator CLI tool
  • Document the ACTUAL v2 features that are implemented (not the wishlist)
  • Set up basic monitoring with Prometheus/Grafana
  • Create a pipeline template generator

Technical Debt Bomb (Week 3-4)

The Scary Stuff Nobody Wants to Touch

  • Executor.ex is a Monster: 1000+ lines of spaghetti. Needs complete refactor into smaller, testable modules
  • No Integration Tests: We’re flying blind. Need end-to-end pipeline tests NOW
  • Provider Lock-in: Everything assumes Claude/Gemini. Need provider abstraction layer
  • Memory Leaks: Large pipelines eat RAM for breakfast. Need streaming/chunking strategy
  • Error Handling: Currently “let it crash” - need proper error boundaries and recovery

Architecture Bombs

  • Circular Dependencies: The component system allows cycles. This WILL bite us
  • No Resource Limits: Pipelines can consume infinite resources. Need quotas/limits
  • Security Holes: No input sanitization, no rate limiting, no audit logs
  • Performance: No caching, no optimization, everything is synchronous

The LangGraph Parity Sprint (Week 5-8)

Must-Have Features

  1. Programmatic API:

    graph = Pipeline.Graph.new()
|> Pipeline.Graph.add_node("analyzer", &analyze/2)
|> Pipeline.Graph.add_edge("analyzer", "reporter")
|> Pipeline.Graph.compile()

  2. State Channels: Like LangGraph’s state updates

  3. Streaming Execution: Return state after each node

  4. Checkpointing: Save/resume pipeline execution

  5. Time Travel Debugging: Replay pipeline execution

Nice-to-Have Features

  • Visual pipeline builder (Phoenix LiveView?)
  • Pipeline marketplace
  • Automatic optimization
  • Cost prediction before execution

The Real Implementation Order

Phase 1: Foundation (Weeks 1-4)

  1. State Management System

    • Exdantic schemas for all state
    • State versioning and migration
    • Immutable state updates
    • State persistence interface

  2. Validation Framework

    • JSON Schema for pipeline YAML
    • Runtime validation with Exdantic
    • Compile-time validation
    • Error reporting with line numbers

  3. JSON/YAML Conversion

    • Bidirectional converter
    • Preserve comments and formatting
    • Schema-aware conversion
    • Streaming support for large files

  4. Core Refactoring

    • Break up Executor.ex
    • Extract Pipeline.Graph module
    • Create Pipeline.Runtime
    • Implement proper supervision tree

Phase 2: LangGraph Features (Weeks 5-8)

  1. Graph API

    • Node/Edge abstractions
    • Graph compilation
    • Execution engine
    • State management

  2. Provider Abstraction

    • Behaviour for AI providers
    • Provider registry
    • Dynamic provider loading
    • Fallback strategies

  3. Streaming & Observability

    • State streaming
    • Execution tracing
    • Performance metrics
    • Cost tracking

Phase 3: Advanced Features (Weeks 9-12)

  1. Component Library

    • Implement all specified components
    • Component testing framework
    • Component registry
    • Version management

  2. Pipeline Composition

    • Inheritance system
    • Dynamic assembly
    • Template engine
    • Composition validation

  3. Monitoring & Analytics

    • OpenTelemetry integration
    • Custom dashboards
    • Alerting system
    • Cost optimization

The Uncomfortable Truths

What Will Break

  • Backward Compatibility: Moving to JSON-first WILL break existing pipelines
  • Performance: Adding validation and monitoring WILL slow things down initially
  • Complexity: The system is becoming too complex for one person to understand
  • Dependencies: We’re adding too many external dependencies

What We’re Not Admitting

  • The v2 YAML format is overengineered
  • Nobody actually needs 90% of these features
  • We’re building a framework when people want a library
  • The LLM costs will be astronomical at scale
  • We have no idea how to handle async execution properly

Technical Debt We’re Creating

  • Every new feature adds 2x maintenance burden
  • The test suite is already falling behind
  • Documentation is out of sync with reality
  • No performance benchmarks exist
  • Security is an afterthought

The Real Priorities

What Actually Matters

  1. Reliability: Pipelines should NEVER fail silently
  2. Debuggability: When they fail, finding why should be easy
  3. Performance: Should handle 1000s of pipelines/second
  4. Cost Control: Prevent runaway LLM costs
  5. Developer Experience: Should be a joy to use, not a chore

What We Should Drop

  • Complex composition patterns (YAGNI)
  • Multi-provider optimization (pick one and do it well)
  • Visual pipeline builder (nice but not critical)
  • Advanced monitoring features (start simple)

Implementation Gotchas

The Hidden Complexities

  • State Serialization: How do we handle functions in state?
  • Distributed Execution: Current design assumes single-node
  • Pipeline Versioning: How do we run v1 and v2 pipelines together?
  • Resource Cleanup: Who cleans up after failed pipelines?
  • Dependency Hell: Component versions will conflict

The Missing Pieces

  • Testing Strategy: How do we test LLM-based pipelines?
  • Deployment Story: How do pipelines get to production?
  • Multi-tenancy: How do we isolate customer pipelines?
  • Compliance: GDPR, HIPAA, SOX - we handle none of it
  • Disaster Recovery: What happens when the database dies?

The 90-Day Plan

Month 1: Stop the Bleeding

  • Fix critical bugs
  • Add basic validation
  • Implement state management
  • Create integration tests
  • Document what actually exists

Month 2: Build the Foundation

  • Implement Graph API
  • Add provider abstraction
  • Create component library
  • Set up monitoring
  • Launch beta program

Month 3: Polish and Scale

  • Performance optimization
  • Security hardening
  • Documentation blitz
  • Customer onboarding
  • Prepare for GA

Random Thoughts and Brain Dumps

Things That Keep Me Up at Night

  • What if OpenAI changes their API again?
  • How do we handle 100GB files?
  • Pipeline costs could bankrupt users
  • The executor is held together with duct tape
  • We have no rollback strategy

Crazy Ideas Worth Exploring

  • Pipeline compilation to native code
  • Distributed pipeline execution with Ray
  • Pipeline optimization with genetic algorithms
  • Natural language pipeline programming
  • Self-modifying pipelines

Libraries and Tools to Investigate

  • Temporal: For workflow orchestration
  • Apache Beam: For data pipeline patterns
  • Prefect: For pipeline scheduling
  • Dagger: For pipeline visualization
  • Argo: For Kubernetes-native pipelines

Competitive Analysis Needed

  • LangChain/LangGraph (Python)
  • Haystack (Python)
  • Semantic Kernel (C#)
  • AutoGPT patterns
  • CrewAI architecture

Architecture Decisions to Make

  • Monolith vs microservices
  • Sync vs async execution
  • Push vs pull data flow
  • Static vs dynamic typing
  • SQL vs NoSQL for state

Performance Targets

  • 10ms pipeline startup time
  • 1000 concurrent pipelines
  • 99.9% availability SLA
  • <$0.01 per simple pipeline run
  • 1GB/s data throughput

Security Requirements

  • SOC2 compliance
  • End-to-end encryption
  • Role-based access control
  • Audit logging everything
  • PII detection and masking

Developer Experience Goals

  • 5-minute quickstart
  • Intuitive error messages
  • Comprehensive examples
  • IDE integration
  • Hot reloading

Business Model Considerations

  • Open source core, paid enterprise features?
  • Usage-based pricing?
  • Hosted vs self-hosted?
  • Support tiers?
  • Partner ecosystem?

The Brutal Truth

We’re building a Ferrari when most people need a Honda. The specification documents are beautiful but disconnected from reality. The current implementation is 20% of what’s specified. We need to either:

  1. Dramatically simplify and ship something useful
  2. Get serious funding and hire a team
  3. Pick a niche and dominate it
  4. Open source it and let the community help

The current trajectory leads to a half-built monument to overengineering. Time to make hard choices.

Final Thoughts

This system could be revolutionary OR it could be another abandoned GitHub project. The difference is in the execution. We have 90 days to prove this is worth building. After that, either double down or move on.

The specs are written. The vision is clear. Now comes the hard part: making it real.

Stop planning. Start building. Ship something people can use.

The clock is ticking.