You will get an AI agent architecture review and written technical plan
Project details
You will get a written technical review of your AI agent architecture and a clear improvement plan — async, no meetings needed.
Building with LangGraph, MCP, or RAG is straightforward until it isn't. State management issues, wrong retrieval strategy, MCP server bottlenecks, poor graph design — these are hard to spot from the inside.
I review production AI systems daily. My open-source project EasyOref runs a multi-node LangGraph pipeline with MCP tool integration and RAG-based extraction in production.
What you get:
– Written assessment of your current stack
– Risk analysis: what will break at scale
– Specific recommendations for MCP, RAG, and LangGraph patterns
– Architecture diagram (Mermaid)
– Prioritized implementation roadmap
Delivered as a structured document you can act on immediately.
Building with LangGraph, MCP, or RAG is straightforward until it isn't. State management issues, wrong retrieval strategy, MCP server bottlenecks, poor graph design — these are hard to spot from the inside.
I review production AI systems daily. My open-source project EasyOref runs a multi-node LangGraph pipeline with MCP tool integration and RAG-based extraction in production.
What you get:
– Written assessment of your current stack
– Risk analysis: what will break at scale
– Specific recommendations for MCP, RAG, and LangGraph patterns
– Architecture diagram (Mermaid)
– Prioritized implementation roadmap
Delivered as a structured document you can act on immediately.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AIAI Tools
Azure OpenAI, Bing AI, GitHub Copilot, Microsoft 365 CopilotAI Models
ChatGPT, GPT-4, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$25
|
Standard
$50
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | - | - | - |
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | |||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code | - | - | - |
About Mikhail
Agentic AI Engineer | MCP | LangGraph | LLM Infrastructure
Tel Aviv, Israel - 10:03 am local time
10+ years in production engineering. Led a platform team from 1 to 13 engineers. Shipped products to 5M+ users while keeping 99.5–100% crash-free rate in production.
My flagship project is CyberMem — a self-hosted MCP memory platform I designed and shipped end-to-end, used by Claude, GPT, Cursor, Gemini, and Perplexity. It runs on Docker, Kubernetes, and Raspberry Pi with Prometheus/Grafana observability, Traefik zero-trust auth, automated versioned releases via GitHub Actions, and Vitest test suite. 550+ commits, 25 versioned releases. Live at cybermem.dev.
I also built EasyOref: a LangGraph-based multi-step alert agent with MCP tool use, BullMQ queues, RAG enrichment, and Telegram delivery — running live in production.
What I work on:
- MCP server development (memory, API bridges, file gateways, custom tools) — Docker/K8s deployed, schema-driven, with auth, observability, and docs
- LangGraph / LangChain workflows — stateful agents with tool use, conditional branching, human-in-the-loop, rollback
- RAG pipelines — document ingestion, embeddings, vector store, retrieval tuning, clean Q&A API
- MCP client setup — Claude Desktop, Cursor, Windsurf, Perplexity — wired correctly with working auth
- Architecture reviews — for teams building agent systems before committing to a design
Positioning: AI Infra Engineer who ships production systems, not demos.
Stack: TypeScript/Node.js, Python, LangGraph, LangChain, FastMCP, Docker, Kubernetes, Redis, BullMQ, Prometheus, Vitest, GitHub Actions.
Send me a short message with your use case, and I'll tell you the fastest way to ship it.
Portfolio: mikhailkogan.dev
Steps for completing your project
After purchasing the project, send requirements so Mikhail can start the project.
Delivery time starts when Mikhail receives requirements from you.
Mikhail works on your project following the steps below.
Revisions may occur after the delivery date.
Analyze your architecture
Review your stack, identify bottlenecks, anti-patterns, and missing pieces
Write findings & recommendations
Document risks and provide MCP, RAG, and LangGraph-specific recommendations