// profile: verified | architecture: active

Mohsen Fathipour

Software Engineer & Technical Leader

Leveraging open-source LLMs combined with robust PHP and Node.js architectures to build high-traffic backend systems. Automating legacy operations and replacing traditional paperwork with scalable cognitive infrastructure.

> ./experience > ./ping
AI agents at work An animated map of AI agents. Engineering agents (development, code review, test, deployment) ship work to production. A shared AI layer provides an orchestrator, LLM, RAG, vector database, memory, MCP servers, skills and guardrails. Business agents cover customer support, finance, inventory, HR, sales, reports and documents. Two scenarios play in turn: a new feature going to production, and a customer support ticket. // engineering pipeline // AI layer // business agents Dev Team Development Code Review Test Deployment Production Orchestrator / Router LLM RAG Vector DB Memory MCP Servers Skills Guardrails Customer Support Financial Inventory HR & Recruiting Sales / CRM Reports Documents Teams & Customers agents.log # 1/2 new feature: request → production $ dev_team → orchestrator "add discount codes" $ orchestrator → dev_agent task assigned $ dev_agent → rag search "checkout": 5 files $ dev_agent → skills load laravel-conventions $ dev_agent → mcp.github open PR #418 $ dev_agent → review_agent review PR #418 $ review_agent → dev_agent 1 change requested $ dev_agent → review_agent fixed, approved ✓ $ test_agent → ci 142 tests passed ✓ $ guardrails → deploy_agent release checks ✓ $ deploy_agent → production v2.9.0 live ✓ # 2/2 support: customer ticket $ customer → orchestrator ticket #5120 "refund?" $ orchestrator → support_agent routed: billing $ support_agent → memory 2 past tickets found $ support_agent → rag refund policy: 3 docs $ support_agent → mcp.crm refund sent Oct 30 $ support_agent → llm drafting the reply $ guardrails → support_agent tone ✓ no PII ✓ $ support_agent → customer reply sent ✓ 1m 12s AI agents at work An animated map of AI agents. Engineering agents (development, code review, test, deployment) ship work to production. A shared AI layer provides an orchestrator, LLM, RAG, vector database, memory, MCP servers, skills and guardrails. Business agents cover customer support, finance, inventory, HR, sales, reports and documents. Two scenarios play in turn: a new feature going to production, and a customer support ticket. // engineering pipeline // AI layer // business agents Dev Team Development Code Review Test Deployment Production Orchestrator / Router LLM RAG Vector DB Memory MCP Servers Skills Guardrails Customer Support Financial Inventory HR & Recruiting Sales / CRM Reports Documents Teams & Customers agents.log # 1/2 new feature: request → production $ dev_team → orchestrator "add discount codes" $ orchestrator → dev_agent task assigned $ dev_agent → rag search "checkout": 5 files $ dev_agent → skills load laravel-conventions $ dev_agent → mcp.github open PR #418 $ dev_agent → review_agent review PR #418 $ review_agent → dev_agent 1 change requested $ dev_agent → review_agent fixed, approved ✓ $ test_agent → ci 142 tests passed ✓ $ guardrails → deploy_agent release checks ✓ $ deploy_agent → production v2.9.0 live ✓ # 2/2 support: customer ticket $ customer → orchestrator ticket #5120 "refund?" $ orchestrator → support_agent routed: billing $ support_agent → memory 2 past tickets found $ support_agent → rag refund policy: 3 docs $ support_agent → mcp.crm refund sent Oct 30 $ support_agent → llm drafting the reply $ guardrails → support_agent tone ✓ no PII ✓ $ support_agent → customer reply sent ✓ 1m 12s