Derious Vaughn

AI Automations · Operations

So the queue moves without you staring at it.

I build agent tooling, retrieval pipelines, and overnight jobs. The case studies below include demos you can run here.

Case studies

Resumé

Derious Vaughn

Los Angeles, CA · 310-350-3717 · deriousvaughn@gmail.com

linkedin.com/in/deriousvaughn · github.com/theHaruspex

Solutions delivery specialist who turns business problems into production automations and internal tools. Designs workflows, prompts, and integrations in Claude, ChatGPT, and Cursor, then writes runbooks teammates can operate. Strong with APIs, webhooks, JSON, and Notion. Remote-ready with stakeholder-facing delivery experience.

Portrait of Derious Vaughn

Skills

Claude AI Agents Prompt Engineering Workflow Automation APIs & Webhooks Notion Python Documentation
AWS SQL Docker CI/CD Linux GitHub Actions

Experience

Automations and Operations Specialist

04/2025 – present

eCustomSolutions · Remote

  • Delivered AI-assisted automations from discovery through production: scoped needs with operations stakeholders, built solutions in Claude and Cursor, and documented runbooks teammates could run without me in the loop.
  • Built no-code Notion automations for an IT deployment pipeline and helpdesk intake (triage, assignment rollups, requester visibility), replacing manual coordination with repeatable workflows.
  • Integrated systems with event-driven webhooks and REST APIs so workflow changes reached reporting in near real time instead of waiting on a lagging scheduled sync.
  • Maintained living architecture docs and sandbox prompts for workspace redesign, including MCP-based schema discovery so automation design reflected live system data.
  • Partnered with engineering, IT, and leadership to find bottlenecks, capture decisions, and simplify a multi-database operations workspace.

Operations & Tech Solutions

05/2023 – 04/2025

Shades of Color · Carson, CA

  • Automated e-commerce and accounting integrations across WooCommerce, Amazon, and QuickBooks APIs to cut manual steps and improve reporting accuracy.
  • Debugged production integration failures when external systems changed and explained fixes in plain language to business owners.
  • Built ingestion and reporting workflows from messy sales and advertising data so leadership could rely on recurring operational outputs.

Independent engineering projects

2024 – present

Personal · Remote

  • Built AI-assisted outreach and scraping tools for personalized email generation, contact extraction, demographic enrichment, and structured reporting (AIAgentOutreach, CASchoolScraper).
  • Built an idempotent AWS Lambda ETL with Docker and GitHub Actions for batched ingestion (reddit-to-supabase-etl), plus a Python SQL Server hydration toolkit for local and CSV-backed loading (hydrate-sql-server).
  • Built a local MCP server (macos-mcp) that exposes Mail, Calendar, Reminders, Notes, Messages, and Contacts to agent clients, with draft-only email so nothing sends without review.
  • Built a Notion schedule-driven automation trigger that parses board schedules and flips automation flags on a timer (EC2 cron + Notion API; notion-notification-central-trigger).
  • Built a daily environmental justice ELT joining EPA air quality with Census income data for LA County (la-air-equity-elt), plus a cite-first LA City Council research assistant (la-council).

Education

Coursework in Communication Studies and Honors Humanities
Azusa Pacific University · Azusa, CA

Case study 01 · LangGraph · MCP

Triage the inbox before a human opens it.

Plan, call tools, draft, then pass or fail a quality gate. Try it below.

Problem

Inbound triage is expensive busywork. Someone burns 30–60 minutes gathering context, drafting a reply, and guessing blast radius, with no structured quality gate before a message looks ready to send.

How it was solved

A LangGraph-shaped pipeline (plan → retrieve → tool calls → draft → eval) with MCP-style tools, live latency/token/$ strips, and a hard pass/fail gate. High-blast actions stay human-gated. The point is guardrails, not unsupervised send.

Python LangGraph Tool use / function calling MCP Agent evals Observability (latency · tokens · $) Guardrails TypeScript / Node (demo UI)

Stack matches how ops agents ship in practice: Python, LangGraph, tool use, MCP, evals, and cost/latency observability.

  • ProblemSlow inbound triage with no quality gate
  • ConstraintNo silent send; high-blast paths stay human-gated
  • StackLangGraph-shaped agent · MCP tools · eval scorecard · cost/latency strip
  • Result~2 min demo path · ~$0.10 · PASS/SKIP explicit

Interactive

Run triage

Idle
Latency
Tokens
Est. $
vs human
30–60m
Pick a sample, then run.

Case study 02 · Cron · Cloud Agents

Wake a cloud agent only when the task says so.

Scheduled pickup with eligibility gates, not always-on autonomy.

Problem

Work marked “in progress” still sat idle overnight. There was no safe way for an agent to pick something up without risking double-runs, a wide credentialed blast radius, or a start that happened only because a task happened to be open.

How it was solved

A scheduled job checks a task board for a narrow eligibility rule: the task must be in progress and explicitly opted into overnight automation. Only then does it start a cloud agent with the right tools attached, clear the overnight flag so the job will not re-fire, and write a short work log. If nothing qualifies, the job stays idle.

Python MCP Multi-agent / control plane Scheduled jobs Guardrails (eligibility gates) Task board API Cloud Agents API Local scheduler

Built around MCP, multi-agent ops, Python automation, and guardrails: an agent control plane, not a classical ML training platform.

  • ProblemIn-progress work never auto-starts overnight
  • ConstraintNever start without an explicit overnight opt-in; idle when none qualify
  • StackScheduled check · task query · cloud agent + tools · work-log writeback
  • ResultOne eligible task → one run; empty set → idle

Interactive

Simulate an overnight check

Idle until a task is both in progress and opted into overnight automation.

Case study 03 · Vectorize · Workers AI

Cite the shelf, or don’t answer.

Retrieve clause context first, then let the model speak. Empty hits mean stop. No invented legal text.

Problem

A small shop is about to sign a vendor or client master services agreement. An LLM that “knows contracts” will invent confident clause explanations unless it is informed first. Retrieve grounded sources before it recommends, drafts, or calls tools.

How it was solved

Build a subject-scoped contracts corpus the agent queries before it acts. Embed the question, retrieve top clause snippets from a curated Nolo / CA contracts shelf, and constrain the answer to cite-only hits. If retrieval is empty, the agent stops and escalates to a human (or counsel) instead of guessing. This demo is the same retrieval step an agent would run at the edge, not a person browsing a public index sample.

Agent-grounded RAG Legal / contracts shelf Pre-advice retrieval Cite-only answers Workers AI embeddings Vectorize Pages Functions Empty-hit fail-closed

Grounded retrieval before the agent advises is the core proof: the same pattern as production RAG, scoped so a model can explain MSA clauses from sources instead of inventing them.

  • ProblemLLM advice on MSAs without sources invents clause meaning
  • ConstraintRetrieve → cite only; empty hits → agent stops / escalates
  • DemoAgent retrieval step · edge embed → Vectorize → cites
  • ResultClause-aware hits or an honest empty set; never invented legal text

Interactive

Run the agent’s retrieval step

What an agent would pull before advising: Nolo / CA desk references via Vectorize. Not legal advice. Zero hits means stop; do not invent a clause.