Arkan Builder

About the project — Arkan‑Builder

A visual studio for agentic AI: sketch workflows on a canvas, convert voice or visual designs into validated agents, benchmark candidates automatically, then compile and deploy production‑ready Python with enterprise observability and safe, auditable model promotion.

Inspiration 🎯

We saw powerful agentic systems locked behind SDKs and config — LangChain and LangGraph made automation possible but inaccessible to most product teams. We imagined a world where anyone could sketch an intelligent workflow or speak an agent into existence and immediately validate, ship, and observe it. That vision became Arkan‑Builder: a visual, voice‑enabled studio that turns intent into production‑ready agentic workflows.

What it does ✅

• Visual canvas: drag‑and‑drop agents, tools and data flows and compile to readable, CI‑friendly Python.
  
• Voice → Agent: record or upload audio, transcribe, scaffold agent manifests and preview responses with high‑quality voices.
  
• Smart routing & cost control: context‑aware routing with predictive performance estimates for cost/latency tradeoffs.
  
• Enterprise MLOps: provenance, embeddings, vector search and model/evaluation tracking for reproducibility.
  
• Observability & integration: event‑mesh simulation, telemetry and live analytics dashboards.

How we built it 🔧

• Frontend: a responsive canvas with real‑time connection previews, collision detection and audio UI.
  
• Runtime: dynamic, sandboxed agent runtime with strict I/O validation and runtime tool loading.
  
• Routing & evaluation: rule‑based routing paired with an automated evaluator that benchmarks, scores and recommends/promotes models.
  
• Data & infra: durable storage and lineage for artifacts and evaluation results; MLflow-style tracking for experiments.
  
• Integrations: pluggable adapters for multimodal I/O, voice TTS/STT, and enterprise messaging with mock‑first demos.

Challenges we ran into ⚠️

• State parity across canvas, compiled code and runtime — solved by a centralized, serializable state model and deterministic compilation.
  
• Trustworthy model selection — solved by building an automated evaluator, statistical gates, and canary promotion workflows.
  
• UX at scale — performance and clarity for large flows required optimized redraws and interaction design.
  
• Audio fidelity & safety — required transcription confidence indicators, redaction, and explicit user review before promotion.

Accomplishments that we're proud of 🏆

• Visual→code compiler that outputs production Python and test scaffolding.
  
• Audio→Agent flow that turns spoken intent into validated agent manifests with voice previews.
  
• Automated evaluation → promotion pipeline enabling benchmarked, auditable canary rollouts.
  
• Enterprise readiness: provenance, reproducible RAG pipelines, event‑mesh simulation, and integrated telemetry.

What we learned 💡

• Democratization requires rigorous backend workflows (validation, observability, promotion) beneath a simple UI.
  
• Empirical evaluation is essential for reliable routing and to avoid regressions.
  
• Multimodal inputs dramatically accelerate ideation but require provenance, metrics and human review.

What's next for Arkan‑Builder ▶️

• Native multi‑modal agent builders (image/audio/video) with visual fine‑tuning and MLflow integration.
  
• Real‑time collaborative canvas with role‑based access and audit workflows.
  
• MCP generator and one‑click export/register to remote MCP servers with approval policies.
  
• Predictive optimization: automated cost/accuracy frontier and proactive retraining triggers.

Built With

• canvas-apis-backend:-fastapi
• chart.js
• ci?friendly-codegen-networking-/-http:-requests
• css
• demo
• demo-scripts
• electron
• embeddings-(rag)-data-&-storage:-delta-lake
• gemini-(adapters-+-mock)-vector-/-retrieval:-databricks-vector-search
• html
• image/audio
• json-artifacts-messaging-/-event-mesh:-solace-(adapter-+-simulator)-observability:-sentry
• langchain?style-tooling-routing-/-orchestration:-martian/deimos-router-(rules-based)-speech-/-multimodal:-elevenlabs-(tts/stt)
• languages:-javascript-(es)
• libs
• local-file-uploads
• opentelemetry-packaging-&-tooling:-node/npm
• processing
• pyproject.toml-testing-&-ci:-pytest
• python-frontend-/-ui:-electron
• python-multipart-misc-libraries:-chart.js
• python-venv-/-pip
• scikit?learn-(optional)
• type-hints-mlops-/-ml:-mlflow
• uvicorn-validation-/-typing:-pydantic
• whisper?style-fallbacks-llms-/-apis:-openai