husamettinarabaci
Husamettin ARABACI
Principal AI Systems Architect · AI-Native Systems Engineering
I design and build production-grade systems where AI agents, models, distributed software, cloud, and edge infrastructure operate as one engineered system.
My background spans 20+ years of software and systems engineering, with deep experience in distributed systems, backend architecture, high-performance systems, reliability, security, IoT, embedded systems, and edge computing.
Today, my work is increasingly focused on engineering reliable systems around probabilistic intelligence.
Engineering Focus
- AI-Native Systems Architecture
- Agentic & Multi-Agent Systems
- Distributed Systems
- AI Platform & Control Plane Architecture
- Durable Agent Execution
- Evaluation & Verification
- AI Security & Authority Delegation
- Reliability & Failure Engineering
- Observability & Forensics
- Context, Memory, RAG & MCP
- AI-Native Software Development
- Hybrid Cloud & Edge Systems
How I Think About AI Systems
A model is only one component of a production AI system.
The harder engineering problems emerge around it:
state · authority · context · verification · failure · observability · reliability · cost
My focus is on designing those boundaries explicitly and turning probabilistic intelligence into systems that can operate safely and predictably in production environments.
Current Engineering Work
I’m currently exploring and building across:
- Agent execution and orchestration
- Tool and MCP architectures
- Context and memory systems
- Verification and evaluation pipelines
- Agent security and permission boundaries
- Durable AI workflows
- AI platform architecture
- AI control planes
- Distributed and hybrid AI execution
- Reliability patterns for nondeterministic systems
The emphasis is on working implementations, architecture decisions, experiments, failure analysis, and measurable evidence.
Selected Systems Background
My engineering experience includes:
- Large-scale distributed and concurrent systems
- High-throughput telemetry and streaming architectures
- Backend and platform engineering
- Reliability and fault-tolerant system design
- Security-sensitive systems
- High-performance data processing
- IoT, RF, embedded and edge systems
- Cloud infrastructure and CI/CD
- Technical architecture and engineering leadership
Open Source
My repositories increasingly serve as an engineering notebook for AI-Native Systems Engineering — implementations, experiments, benchmarks, architectural patterns, and failure investigations.
As this work develops, the strongest artifacts will be documented and pinned here.