Architecting Autonomous Intelligence
& Production AI Swarms.
I am William Volodarsky — an Agentic AI Engineer & Systems Orchestrator (Fordham CS, Magna Cum Laude). I build local-first multi-agent systems using LangGraph, deterministic context injection, and human-in-the-loop governance to replace fragile AI demos with reliable production infrastructure.
LangGraph Router, Librarian, Career & Email subgraphs
Zero-cost DPFH directory injection vs chunk fragmentation
HL7 CDA XML, ADB Scraper, Playwright PDF, IMAP OAuth
Multi-axial alignment, LLM-as-a-Judge test harnesses
Nexus: The Multi-Agent Cognitive OS
Replacing single-prompt toy demos with a stateful, local-first multi-agent swarm. Nexus orchestrates health, career, and research workflows across 500+ structured markdown notes with zero hallucinated file structures.
DPFH vs. Traditional Vector RAGZero LLM Cost
Side-by-side operational architecture for structured knowledge retrieval.
- •Chunk Fragmentation: Rips markdown tables, frontmatter, and headings into arbitrary 500-token slices.
- •Lost in the Middle: Semantic search retrieves out-of-context chunks without hierarchical awareness.
- •Hallucinated Paths: Generates plausible but nonexistent markdown note links.
- •Zero-Cost Injection: Deterministic Python pre-node injects live disk directory tree into prompt.
- •Whole-Document Reading: Librarian traverses exact notes and structured wiki-links natively.
- •100% Grounded Links: Every citation is a verified, live Obsidian deep link.
def hydrate_agent_context(state: SwarmState) -> dict:
# 0 LLM tokens: Pure Python tree mapping & frontmatter injection
live_tree = vault_reader.get_domain_structure(domain=state["domain"])
curated_prompt = build_system_prompt(
subconscious_rules=RULES_CONFIG,
live_taxonomy=live_tree,
conversation_summary=state["summary"]
)
return {"hydrated_prompt": curated_prompt}Production Domain Tool Suite
12 purpose-built automation pipelines powering Nexus agents.
Longitudinal clinical record synthesis & lab trend tracking.
Android screen capture with multi-scroll OCR deduplication.
Headless Playwright PDF & python-docx with page-fill metric audits.
Asynchronous email ingestion & priority triage pipeline.
12-case golden dataset test runner with JSON scoring reports.
Pre-commit side-by-side file write review in SQLite.
Deterministic article cleaning & atomic Zettelkasten synthesis.
Automated daily briefing audio synthesis via voice models.
Local asynchronous audio capture & thought triage.
Mobile prompt execution with user-ID crypt-security.
Zero-cost directory tree & frontmatter metadata traversal.
Zero-knowledge client-side encryption for personal records.
End-to-End Execution Architecture
Click any node in the execution DAG below to inspect how Nexus ingests, classifies, hydrates, evaluates, and safely commits autonomous agent actions.
LangGraph Content Router
Deterministic zero-shot classifier matches user intent against domain agents (Health, Career, Email, Librarian). High-confidence matches route directly; complex cross-domain queries dispatch to the Librarian subgraph.
def route_query(state: SwarmState) -> str:
domain = classify_intent(state["messages"][-1].content)
# Returns "career", "email", "librarian", or "general"
return f"enter_{domain}_subgraph"Professional Experience & Execution
Combining rigorous foundation model evaluation with 8 years of operational leadership, revenue scaling, and mission-critical compliance.
AI Training Fellow
Foundation Model Alignment- Performed iterative resampling with corrective feedback across LLMs and latent diffusion models.
- Evaluated model outputs across multi-axial quality rubrics: factual accuracy, instruction following, safety, and non-deterministic code correctness.
- Identified systematic failure modes (hallucination patterns, lost-in-the-middle context degradation), translating edge cases into actionable fine-tuning constraints.
Co-Owner & Operations Lead
2x Revenue Growth- Identified high-margin product opportunities through sales velocity and competitive data analysis, deploying new product lines that doubled private brand revenue over 18 months.
- Engineered multi-channel international distribution (Amazon, Wholesale, D2C) across US, UK, and EU markets.
- Managed regulatory compliance, international customs logistics, product design, marketing campaigns, and multi-tier supplier negotiations.
Medical Records & Patient Services
100% HIPAA Compliance- Co-managed clinical records for a premier high-volume ophthalmology practice, maintaining 100% HIPAA compliance across document handling, retrieval, and clinical transfers.
- Built interactive tracking spreadsheets and scheduling databases for patient service quality, driving measurable improvements in clinical throughput.
Education & Academic Honors
Bachelor of Science in Computer Science
Dean's List. Focus on Algorithms, Operating Systems, Database Systems, Artificial Intelligence, and Software Engineering.
Associate of Science in Computer Science
Rigorous foundation in Object-Oriented Programming (Java/C++), Data Structures, and Discrete Mathematics.
Skills & Systems Competencies
Strictly production-tested competencies across agent orchestration, backend services, evaluation testbenches, and data pipelines.
Master Resume & Evidence Locker
Synchronized directly with production engineering and operations track record.
William Volodarsky
Agentic AI Engineer & Systems Orchestrator
Fort Lee, NJ 07024 • bill@williamvolodarsky.com • github.com/brolodarsky • linkedin.com/in/williamvolodarsky
01 // Professional Summary
Agentic AI Engineer (CS, Magna Cum Laude) and creator of Nexus, a production-grade Agentic OS featuring a multi-agent LangGraph pipeline with Deterministic Pre-flight Hydration, a full-stack Next.js + FastAPI control panel with HITL governance, an LLM-as-a-Judgeevaluation framework, and 12 deployed automation tools. Eight years of business operations leadership provides the systems judgment to ship robust, compliant AI systems that solve real-world problems.
02 // Flagship Project: Nexus (Agentic OS)
read_toc, read_note, search_vault, get_vault_structure) with tree-based directory injection and frontmatter filtering.03 // Professional Experience & Leadership
Performed iterative resampling with corrective feedback across LLMs and latent diffusion models; evaluated outputs across multi-axial quality rubrics, identifying systematic failure modes (hallucination patterns, context degradation) and translating edge cases into actionable fine-tuning constraints.
Doubled private brand revenue over 18 months through sales velocity analysis and high-margin product expansion. Managed multi-channel international distribution (Amazon, Wholesale, D2C) across US/UK/EU markets, overseeing regulatory compliance, customs logistics, and supplier negotiations.
04 // Education & Honors
Let's Build Production AI Together.
I am currently evaluating high-impact Agentic AI Engineering and Systems Orchestration roles (NYC, Hybrid, or Remote). Whether you are building autonomous agents, tackling LLM alignment, or scaling complex operations, let's connect.