Hamid Jafari-Zadeh
I’m an AI systems and reliability engineer. I’ve been writing code since I was 12 and working in IT for over 20 years. For the last 13+ of them I’ve run production systems at national and global scale: payment infrastructure, incident command for a global platform across five datacenters, and now AI agents.
I specialize in turning complex operational problems into resilient, predictable systems. To me, AI is another systems layer, one that needs the same rigor, clear boundaries, and operational discipline as any other critical system.
My work connects systems thinking with hands-on implementation. I build agents and AI workflows that are designed to be inspectable, reliable, and useful under real-world conditions.
Featured Projects & Research
AI Agent Control Layer
SEED
A control layer for AI agents near production.
Problem: AI agents become risky near production when their behavior is hard to observe, constrain, or change safely.
Approach: Building a control layer focused on observability, explicit boundaries, and practical operator control for agent workflows.
Outcome: A path toward AI agents that teams can inspect, govern, and trust closer to real production use.
Controlled AI Execution
Architect
An AI system for technical task execution under strict human control.
Problem: Most autonomous agents act as black boxes, making it difficult to inspect their logic, stop their actions, or reverse their mistakes.
Approach: Built an execution loop that gates and records actions, requires testing before capability promotion, and keeps higher-risk operations under explicit human control.
Outcome: A dependable system that can perform technical tasks while remaining observable, reviewable, and accountable to the operator.
Behavioral Analytics
NeuroTrace
A framework for analyzing how AI behavior changes over time.
Problem: AI personalization is often judged through intuition, making it hard to measure how an assistant actually adapts across many sessions.
Approach: Created a deterministic pipeline that turns conversation logs into structured behavioral data, mapping patterns such as planning, memory, feedback, and reflection.
Outcome: A repeatable way to inspect behavioral change over time and discuss personalization with evidence instead of guesswork.
Content Infrastructure
MD Grid Engine
A content engine for building fast, file-first websites. It also serves as the UI foundation for Architect, where the same system is used to present plans, execution traces, and decision outcomes in a structured way.
Problem: Traditional CMS platforms are often too rigid or heavy for technical sites that need structured content and more complex system views.
Approach: Engineered a custom Next.js engine that treats content as code, transforming Markdown and MDX into responsive layouts through a centralized component registry.
Outcome: A fast, maintainable system that powers this portfolio today and provides a UI foundation for future Architect interfaces.
Need AI systems you can trust in production?
I help teams make AI agents and production systems observable, controllable and safe to change. Open to roles, contract work and consulting.