Custom AI Development IDE & Agent Runtime
Oct 1, 2025
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2 min read
Custom AI Development IDE & Agent Runtime
Overview
Architected a modular, extensible AI development environment and distributed agent runtime designed to orchestrate complex, multi-turn tool-calling workflows across heterogeneous local and cloud LLMs. The platform integrates Anthropic’s Model Context Protocol (MCP), providing unified, secure context and tool integration for autonomous developer agents.
flowchart TD
IDE[AI Developer IDE Frontend\nTypeScript / Electron] <-->|JSON-RPC / SSE| MCP[Model Context Protocol\nServer & Registry]
MCP <--> LocalSLM[Edge SLM Engine\nOllama / Custom Kernels]
MCP <--> Runtime[Distributed Agent Runtime\nAWS EC2 / FastAPI]
Runtime --> Memory[Persistent Session Memory\n& Workflow State Hub]
Runtime --> Tools[External Tooling\nSlurm, Git, Compilers, DBs]
System Architecture & Features
1. Model Context Protocol (MCP) Orchestration
- Implemented full MCP client and server specifications, enabling autonomous agent delegation across filesystem navigation, terminal executions, git workflows, and remote database queries.
- Standardized tool schema generation and validation for low-latency function calling.
2. Scalable Cloud Agent Runtime & Workflow Hub
- Engineered a distributed agent execution backend on AWS EC2 using FastAPI and Docker, supporting persistent conversation history, checkpointing, and real-time execution streaming.
- Built a collaborative workflow hub enabling developers to publish, share, and dynamically compose multi-agent task recipes.
3. Edge SLM Inference Optimization
- Engineered an optimized local edge inference pipeline tailored for short-context Small Language Models (SLMs).
- Minimized invocation latency and token consumption, allowing reactive agent decision loops to execute with near-zero overhead.
