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AI/ML & LLM Agents Internal Platform • 12 Multi-Agent Workflows • ⏱
Enterprise Model Context Protocol (MCP) Server Infrastructure for Multi-Agent AI
Lead Architect: Samuel Mofrad • AI Systems Architect
Executive Architecture Summary
Architected and deployed enterprise-grade Model Context Protocol (MCP) servers enabling secure, bi-directional tool execution between LLM agents (Claude, GPT, Ollama) and internal engineering backends.
• Core Architectural Implementation & Deliverables:
- Engineered JSON-RPC 2.0 transport layers over stdio and SSE with strict mutual TLS (mTLS) and token authentication.
- Integrated semantic vector indexing (Qdrant & pgvector) allowing autonomous agents to query Jira, Confluence, and internal Git repositories.
- Reduced repetitive DevOps incident remediation cycle time by 42% via self-healing multi-agent diagnostic runbooks.
- Established sandboxed code execution environments preventing untrusted LLM prompt injection and unauthorized API lateral traversal.
Need similar architectural transformation for your enterprise?
Consult with Samuel Mofrad on legacy decoupling, high-throughput microservices, or custom MCP servers.