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RavaniRoshan/README.md

Hi 👋 I'm Ravani Roshan

AI Systems Builder | Autonomous Agents | Developer Tooling Building practical, reliable AI systems that can do more than look impressive in a demo.

I'm an AI-focused builder working on autonomous agents, computer-use automation, and the infrastructure that keeps ambitious systems dependable once they meet real tools, real users, and real-world constraints. My work spans multi-agent orchestration, sandboxed coding environments, LLM reliability, and developer products.

I care about useful systems, thoughtful product design, and safeguards that keep a swarm of agents from becoming a very confident expense report.


What I Work On

  • Autonomous and multi-agent systems for planning, coding, testing, and review
  • Agent reliability: budgets, backpressure, retries, circuit breakers, and observability
  • Sandboxed developer tooling and Docker-based execution environments
  • Computer-use automation and systems integrations
  • AI product engineering with Rust, Python, TypeScript, and modern web tooling

GitHub Activity

GitHub stats Top languages
GitHub streak

Tech Stack

Languages

Python Rust TypeScript JavaScript C++

AI, Agents & Reliability

OpenAI Anthropic LangChain Hugging Face

🧠 Multi-agent orchestration · LLM evaluation · Agent memory · Computer use · Observability

Cloud & Developer Infrastructure

Docker React Next.js Vite Tailwind CSS


Featured Work

Niki — Hermetic Multi-Agent Coding System

  • A Rust-based system where isolated agents plan, code, test, and review inside Docker sandboxes
  • Produces reviewable Git branches instead of giving every agent the keys to the production kingdom

Backstop — AI SDK Reliability Layer

  • In-process controls for backpressure, budgets, retries, circuit breaking, and metrics
  • Designed to put sensible brakes on AI SDK workflows before cost, rate limits, or chaos take the wheel

Phantom — Background Computer-Use Agent

  • A Rust-powered Windows automation agent built to work quietly in the background

policyctl — policyctl provides a deterministic policy runtime that sits between the agent and your codebase.

  • Provider-agnostic deterministic policy runtime for coding agents.

Highlights

  • Building agent systems with reliability and containment as first-class features
  • Shipping tools across Rust, Python, TypeScript, Docker, and LLM APIs
  • Exploring the bridge between agent research and practical developer workflows
  • Focused on products that make capable AI systems safer, clearer, and more useful

Let's Connect

agent status: awake
coffee level: suspiciously high
build target: useful systems, not shiny demos

Profile Views

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  1. niki niki Public

    Hermetic multi-agent coding system: isolated AI agents independently plan, code, test & review in Docker sandboxes, then hand you a reviewable git branch. Fire-and-forget, BYOK, built in Rust.

    Rust 2

  2. backstop backstop Public

    In-process AI SDK backpressure, budgets, retries, circuit breaking, and metrics. Testing whether transport-layer budget isolation reduces runaway-cost exposure in multi-agent coding architectures.

    Python

  3. phantom phantom Public

    Phantom — the invisible background-mode computer-use agent for Windows

    Rust

  4. winscript-lang winscript-lang Public

    The open scripting language for Windows automation.

    Python

  5. winscript-mcp winscript-mcp Public

    A Windows-native automation API, packaged as an MCP server, that gives AI agents the same system-level desktop control that AppleScript gives on macOS.

    Python 8

  6. openjck openjck Public

    An agent workflow visual debugging tool

    Python 1