An Autocoder For Enterprise Systems Building

Not just a coding assistant

What We Are Building
JUXX22 is an LLM autocoder.It uses plain English to build complete, production-ready systems by constraining points of failure and leveraging LLM strengths. It disassembles complex problems into small, individually coded components and recomposes them into deployable solutions.


How It Works

  1. Understands Your Goal - Describe what you want to build in plain English. The system clarifies through conversation the problem and goal, determining the individual capabilities and components required.

  2. Designs the Architecture - The system creates a formal blueprint. This plan outlines independent components (for APIs, databases, or logic) and defines how they connect through strictly typed input/output ports to ensure data integrity, adaptability, and scalability.

  3. Composes the System- Components are assembled into an integrated service graph so the whole system works as one.

  4. Writes the Code - For each component in the blueprint, the system uses AI to write the unique business logic. It then programmatically injects the architectural boilerplate (imports, error handling, class structures). This lets the AI focus on problem-solving while guaranteeing architectural consistency.

  5. Validates, Tests, and Heals Continuously - Multi-tier validation catches issues early.
    - Component Tests: Each component is tested with AI-generated synthetic data.
    - End-to-End: The full system is tested; if validation fails, the system auto-heals and can revert to previous stages until checks pass.

  6. Packaged for Production - Once validated, the system generates a production-ready deployment package: Dockerfiles, Kubernetes manifests, security baselines, health checks, and monitoring pre-configured. Reproducible builds and provenance are included.

  7. Visual Blueprint for Transparency -The system generates documentation and a visual map showing how each part functions and fits together.


How It Works (Demo)


How JUXX22 Handles Any Data Pipeline Challenge
JUXX22 decomposes complex data flows into four modular, testable, type-safe components—avoiding the fragility of monolithic systems and enabling precise, adaptable pipelines.1. The Universal Building Block: The Component
Instead of a handful of rigid parts, we use a single, flexible component for everything—APIs, data logic, database connections, etc. A Component's specific job is defined by how it's connected, making it a truly universal building block.Example: To process an order, you might use three Components: one to receive the request from the customer, one to process the payment, and a third to update the inventory.Customer Request → Payment Processor → Inventory Updater2. The Smart Connector: Ports & SchemasComponents connect using ports, which only accept the correct type of data "plug." Each Port enforces a schema (a strict data contract), guaranteeing that bad data can never crash a downstream part of your system.Example: A Payment Processor's input Port requires data with a valid credit card number and amount. It will simply reject any data that doesn't perfectly match this structure, preventing errors before they can happen.3. Upgrades: Automatic CapabilitiesCapabilities are instant, expert-level features you can add to any Component. A non-bypassable capability kernel automatically gives every component foundational security and monitoring from day one.Example: If your Payment Processor needs to be more reliable, you add the retry capability. It instantly learns how to automatically retry on a temporary failure, making your system more resilient, and no new code is required.4. The System Blueprint: BindingsBindings are the explicit wiring diagram for your entire system, defined in a clear blueprint. They draw a line from an output Port on one Component to an input Port on another, creating a verifiable and transparent map of your data's journey.Example: Your blueprint contains a Binding that declares: "Connect the 'payment successful' port from the payment processor to the 'update stock ' port of the Inventory Updater." This creates a guaranteed and observable data path.


Why JUXX22 Scales
Iterative Development – Each component tested independently with LLM-generated data enables rapid debugging and focused validation through multiple feedback loops
Clear Boundaries – State, logic, routing, and aggregation separated into distinct components with predictable behavior, reducing complexity
Composable Design – Processes, data sources, interfaces, and process steps swap without breaking the system
Blueprint Control – All logic and connections originate from a centralized declarative blueprint, making workflows easy to inspect, modify, and evolve


Type-Safe Communication Through Ports and Schemas
All components communicate through strictly typed input and output ports, preserving structure and validation throughout the pipeline.Runtime validation is enforced via Pyadantic schemas attached to each DataObject.Both Routers and Reducers are composable, meaning they can be nested to support more complex or recursive workflows without breaking modularity.


Advanced Workflow With Routers and Reducers
Router and Reducer components enable complex conditional paths and result aggregation, unlocking sophisticated flows without breaking modularity.

-Router (FormatRouter) splits flow between HTML/Markdown processors.
-Reducer (FormatCollector) merges their outputs.


Iterative Development With Focused Testing
Each component is tested independently using LLM-generated test data. This modularity allows:
-Rapid debugging
-Focused validation
-Multiple feedback loops for system improvement


What Differentiates Us

  • Goal-driven generation – Natural language objectives to complete Python pipelines

  • Production-ready outputs – Deployable systems with Docker/Kubernetes, not just code

  • Self-healing validation – Every check coupled with automatic LLM remediation

  • Agent-native infrastructure – Autonomous agents can plan, coordinate, and execute structured workflows

  • LLM-optimized – Each step fits within optimal context windows for reliable generation

  • Blueprint-driven control – Centralized declarative architecture with formal ADRs

  • Enterprise-grade by default – Mandatory security, rate limiting, and observability in every component

  • Machine-readable – Structured for parsing by AI agents and developers

  • Isolated side effects – State changes contained within specific modules

  • Performance guarantees – Published latency/memory budgets with build-time enforcement

  • Visual transparency – Directed graph visualization for inspection and version control

  • Evolvable – Swap backends, add libraries, support MCP without rewiring



Explanation Of Our Vision



Abstract Thinking: The Art Of Pruning And Knowledge Graphs (Scale-Free)



Contact Me - [email protected]
https://www.linkedin.com/in/-tyler-mills/