Engineering notes on production AI systems.
AI infrastructure releases, MCP, agents, retrieval, memory, and the operational lessons from building Hermes.
The piece that anchors the week.
LangGraph Deep Dive: How Production Agents Actually Run
A production-focused guide to checkpointers, interrupts, TimeoutPolicy, and the runtime decisions that matter once agents hit real traffic.
Agent orchestration is no longer a demo concern.
As workflows become longer-running and more autonomous, checkpointing, resumption, human approval, and failure boundaries become the architecture — not implementation details.
News, guides, and field notes.
The feed is built for senior engineers: what changed, why it matters, and how it affects production systems.
What’s new this week in AI engineering
OpenAI, Anthropic, LangGraph, MCP, and infrastructure updates worth reading.
MCP Architecture: Protocol, Primitives, and Security
A production guide to lifecycle, transports, primitive design, and implementation boundaries.
How Hermes uses Forge to turn research into WordPress drafts
A practical look at planning, writing, reviewing, and publishing through an agent workflow.
Real implementation notes, not generic agent advice.
Tool execution boundaries
Where agents should stop, ask, or checkpoint before side effects.
Durable context vs session state
What belongs in memory, what belongs in the transcript, and what should expire.
Forge as content pipeline
Research, drafting, review, preview, and WordPress publication as one controlled workflow.
AI engineering notes for production systems.
Release signal, deep technical guides, and Hermes case studies — organized for engineers making architecture decisions.
LangGraph Deep Dive: How Production Agents Actually Run
Checkpointers, interrupts, TimeoutPolicy, and what changes when agent workflows become operational systems.
Production agents require resumable control flow.
This guide explains the design decisions behind durable agent orchestration without turning into API documentation.
MCP Architecture: Protocol, Primitives, and Security
JSON-RPC lifecycle, transports, security boundaries, and implementation patterns.
What changed this week in AI engineering
A weekly digest across OpenAI, Anthropic, LangGraph, MCP, and open-source infra.
How Hermes uses human approval to control publishing side effects
Approval boundaries, preview URLs, draft-only automation, and safe publication paths.
I build production AI and data systems.
My work sits at the intersection of AI agents, retrieval infrastructure, and production data platforms. Sieon Labs is where I turn that work into source-backed engineering notes for people building real AI systems.
The through-line is operational reliability: data pipelines, vector search, intent routing, observability, and workflows that keep working after the demo.
Intent routing, tool execution, autonomous workflows.
Vector search, indexing strategy, hybrid data access.
ETL, cloud databases, Terraform, monitoring.
AI infrastructure news, deep guides, Hermes cases.
AI & Agent Development Intern
Contributing to enterprise AI agent platform features across integrations, execution workflows, productivity surfaces, and intent routing.
- Built routing logic for Collection Chat, Ask Data, and AI agent workflows.
- Supported Pinecone migration architecture and document indexing strategy.
- Contributed to analytics and reporting workflows for operational insight.
Data Engineer
Built production data infrastructure for analytics, recommendations, and large-scale game operations.
- Designed a 500GB+ AWS OpenSearch k-NN vector embedding store.
- Built Python ETL and SQL Server auditing workflows for production analytics.
- Provisioned Aurora MySQL, Azure SQL, and Redis with Terraform.
- Built Grafana dashboards and anomaly-detection pipelines for operational reliability.
IT MES Systems Engineer
Worked on manufacturing systems where query performance and reporting reliability directly affected operations.
- Refactored SQL procedures behind real-time manufacturing dashboards.
- Built C# automation tools for reporting integrations.
University of Chicago · Stony Brook University
M.S. Applied Data Science at the University of Chicago; B.S. Information Systems from Stony Brook. Current capstone work focuses on healthcare AI retrieval systems using clinical datasets.
Capabilities instead of a resume keyword dump.
- Python, SQL, R, C#
- Pandas, NumPy, PySpark
- Scikit-learn, TensorFlow, PyTorch
- ETL/ELT pipeline design
- Batch and streaming workflows
- Data modeling and quality monitoring
- AWS, Azure, GCP
- OpenSearch, Aurora, Redis, BigQuery
- Terraform, Docker, Kubernetes, Grafana