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Paul Henkelman

Systems

Systems Built and Running

What's been built, and the scale it runs at.

Streaming Concierge

Charter's first customer-facing agentic AI service

Conceived the solution, built the fully functional prototype, and designed the production architecture (LangGraph, LangChain, custom and standardized MCP, Playwright-MCP) for an agentic system that executes service-activation steps on the customer's behalf, reducing roughly 2 hours of customer effort to about 2 minutes.

The demo was presented at every level of leadership, including to the CEO; guided the cross-team build to production launch serving thousands of customers per day. The design was adopted as Charter's reference architecture governing ongoing agentic development.

AIOps at National Scale

Detection, causal inference, prediction, and automated remediation · 30M+ subscribers

Designed the detection, causal-inference, prediction, and automated-remediation platform (Random Cut Forest, MAD-GAN, Bayesian networks) for customer-facing network issues across a 30M+ subscriber footprint; delivered the full PoC and implementation design and led multi-department development at enterprise scale.

Builds on pioneering Comcast's enterprise AIOps platform: ML alerting and event-correlation pipelines that cut false-positive alerts by 92%, petabyte-scale telemetry architecture, and automated anomaly response that materially reduced MTTR.

GPUaaS: AI Infrastructure as a Business

Idea-to-inference platform on edge GPU infrastructure

Lead inference and MLOps/LLMOps architecture for a GPU-leasing initiative: an idea-to-inference platform with continuous training and enhancement loops, built to serve external AI workloads at commercial scale.

Personally direct large-scale training on EKS and SageMaker: 100+ LLM fine-tuning runs across Qwen and other open-weight families, full LLM pretraining, and world-model training.

Noetix

Open source · sole author · MIT license

An open-source agentic memory system providing embedding-based semantic search and GraphRAG, wrapped in MCP as first-class agent memory.

Independently developed and in internal use at Charter. Agent memory is usually the weakest piece of an agentic architecture; Noetix gives it the same engineering weight as the agent itself.

View on GitHub

Agentic Security Architecture

Patent disclosure filed

Agent-safe sensitive-data handling: security architecture for agentic systems that must work with credentials and PII under guardrail and human-in-the-loop patterns built for dynamic workflows.

A patent disclosure has been filed; technical detail will follow when the application publishes. The underlying problem is general: agentic systems inherit every data-exposure risk of the tools they orchestrate, and closing that gap is an architecture problem.

Network Language World Models

Applied research · presented to GVP/SVP audiences

Research applying language world models (LWMs) to network management and automation, where agents require human-like operational intuition about how infrastructure actually behaves.

Presented at industry conference sessions, including the 2026 SCTE Rocky Mountain Symposium. World modeling is the likely next substrate for operational AI.