intheflow

Rust based IDP AI-Driven Developer Portal with Hybrid Execution Objective: Build an internal developer portal that automates the entire SDLC—from requirements to Day 2 operations—using AI for flexibility and Rust for determinism. The system integrates a knowledge graph, cross-language transpilation, and hybrid AI/Rust execution for business process orchestration.

1. Requirements

Functional

• AI-Driven Automation:

  • Generate code, tests, and docs from natural language requirements.

  • Self-healing workflows with AI-driven error correction.

• Cross-Language Transpilation:

  Convert projects between languages (e.g., COBOL → Rust) via a 3-step process:

  Source → Intermediate Representation (IR).

  Rearchitect IR for target language idioms.

  Generate Code (Rust/Java/Go) from IR.

• Business Process Orchestration:

  Map services to business processes/rules in user-friendly (BPMN) and LLM-friendly (JSON) formats.

  Execute workflows via AI decisions + Rust rule engine for determinism.

• GitOps & CI/CD:

  Argo Workflows/CD/Rollouts for pipelines and deployments.

Observability:

AI-powered incident triage with SigNoz (logs/traces) and Prometheus (metrics).

Non-Functional

Performance: <1s latency for IDE suggestions; 1M+ rules/sec for Rust engine. Security: OPA policies, Vault secrets, Semgrep scans. Scalability: Horizontal scaling for AI/Rust components. Reliability: 100% valid code generation via closed-loop testing.

2. Architecture

Core Components AI Layer: Models: DeepSeek-R1 (code gen/fixes), DeepSeek Coder (IDE). Orchestration: LangChain for multi-step workflows. Vector DB: Qdrant for RAG over docs/code.

Knowledge Graph: Nodes: Services, code components, business processes, rules. Edges: IMPLEMENTS, TRANSPILES_TO, HAS_RULE. Tools: Apache Age (PostgreSQL), Cypher queries.

Rust Backend: GraphQL API: Async-graphql + Axum for SDLC operations. Rule Engine: Enforce deterministic business rules (e.g., "validate before charge").

CI/CD: Argo Workflows: Self-healing pipelines with AI retry loops. Argo CD: Sync AI-generated K8s manifests.

Frontend: VS Code Extension: AI chat, code suggestions, Argo dashboards. Portal UI (SvelteKit): Business process visualizations (BPMN).

Hybrid Execution Model

mermaid graph

A[IR: Business Process] --> B[AI Orchestrator]
B --> C[Rust Rule Engine]
C --> D{Valid?}
D -->|Yes| E[Call Pre-Built Rust Function]
D -->|No| F[Block/Redirect]
E --> G[Update State]
G --> H{Next Step?}
H --> B

3. Development Plan

Phase 1: Core Backend & Knowledge Graph (Weeks 1-4) Rust GraphQL Server: Setup async-graphql + Axum with JWT auth. Integrate DeepSeek-R1 (vLLM/llama.cpp). Knowledge Graph: Model SDLC entities (services, code, processes) in PostgreSQL + Apache Age. Argo Workflows: Deploy Argo on Kubernetes; define base pipelines.

Phase 2: Transpilation & Business Mapping (Weeks 5-8) Transpiler Engine: Implement IR generator (Tree-sitter) and code synthesizer (DeepSeek-R1). Business Taxonomy: Define core terms (e.g., "Payment Processing") and map to code via static analysis. Closed-Loop Testing: Add pytest/Semgrep stages to Argo workflows.

Phase 3: UI & Observability (Weeks 9-12) VS Code Extension: Embed Monaco editor with DeepSeek Coder suggestions. Real-time Argo workflow status. Observability: Deploy SigNoz for traces/logs; integrate AI incident triage.

Phase 4: Day 2 Autofix & Compliance (Weeks 13-16) Post-Deployment Automation: Argo Rollouts + AI analysis of metrics for auto-rollback. Compliance Checks: OPA policies mapped to business taxonomy rules.

4. Toolchain Category Tools AI/ML DeepSeek-R1, LangChain, Qdrant, Hugging Face Backend Rust, Axum, Async-graphql, NATS, Redis CI/CD Argo Workflows/CD/Rollouts, Crossplane Knowledge Graph Apache Age, Cypher, Tree-sitter Security OPA, Vault, Semgrep, Kyverno Observability SigNoz, Prometheus, Loki

5. Challenges & Mitigations Challenge Mitigation AI Hallucinations RAG with Qdrant; validate outputs with Rust rule engine. State Management Redis for session state; CRDTs for distributed consistency. Legacy Code Gaps Prioritize common transpilation paths (COBOL → Rust). Rule-AI Conflicts Hierarchy: Rust rules override AI decisions.

6. Outcomes For Developers: Zero boilerplate; AI handles coding, testing, and fixes. IDE-integrated SDLC automation.

For Business: Real-time mapping of code to business processes. Self-healing deployments with AI-optimized rollouts.

Next Steps Start with Phase 1 (Rust backend + knowledge graph). Define initial business taxonomy (e.g., "Payment Processing"). Prototype a COBOL→Rust transpiler using Tree-sitter and DeepSeek-R1.