Derious Vaughn

Agentic · MCP · Retrieval

So the queue moves without you staring at it.

Agent tooling, MCP servers, cite-first retrieval, and event-driven ops — with demos you can run below.

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Open to Agentic / MCP roles · Remote

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, internal tools, and MCP-based agent tooling. Designs workflows, prompts, and integrations in Claude, ChatGPT, and Cursor, and builds MCP servers that ground automations in live system data. 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 MCP
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.
  • Used MCP-based schema discovery to ground automation design in live Notion schema, and maintained living architecture docs and sandbox prompts for a multi-database workspace redesign.
  • 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 a personal agent control plane wiring ~21 MCP servers and ~29 agents (registry, sync, doctor) — private infrastructure, described not linked.
  • 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

About

Derious Vaughn

I started in design and moved into Python during the pandemic — building systems, automations, and internal tools when teams needed reliable ops more than slide decks. Today I focus on agent tooling, MCP servers, and cite-first retrieval for production workflows.

I use LLM-assisted coding the way I use any power tool: with judgment and verification. I own the tradeoffs, read the diffs, and ship only what I can explain and maintain.

Outside delivery work I'm a published poet; I also lift and play strategy games — both habits in patience, pattern recognition, and knowing when to stop pushing.

From the work itself: I build fail-closed, inspectable systems — triage gates, webhook routers with HMAC and structured logs, retrieval that stops on zero hits. On my own time I run a private multi-agent / MCP control plane (~21 servers / ~29 agents) with registry sync and health checks — not linked here, but it's how I stress-test the patterns I bring into production ops work.

Case study 01 · LangGraph · MCP

Triage the inbox before a human opens it.

~2 min · ~$0.10 vs 30–60m human. Plan, call tools, draft, then pass or fail a quality gate.

Private demo repo

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 · vs 30–60m human

Interactive

Run triage

01

Input

02

LangGraph workflow

Edit from / subject / body, then run. A narrated tool trace appears here.

03

Output

Idle
Latency
Tokens
Est. $
vs human
30–60m

Final draft and gate verdict show here when the run finishes.

Case study 02 · Webhooks · Event-driven

Don't wait on the next sync for the board to move.

Near-real-time workflow → reporting; stage tracking 8 → 12. Private production code.

Private production repo

Problem

A lagging scheduled sync meant leadership dashboards trailed the work. IT deployment stage visibility was too coarse — only 8 stages — so bottlenecks hid inside broad buckets and status changes showed up hours late.

How it was solved

A production Notion webhook router (HMAC verification, Secrets Manager, structured logs) plus event derivation so status changes hit reporting near real time. Expanded IT deployment stage tracking 8 → 12 for finer bottleneck visibility without waiting on the next batch sync.

TypeScript AWS Lambda API Gateway Notion webhooks HMAC DynamoDB Observability

Event-driven ops with verified webhooks — not a scheduled poll pretending to be live.

  • ProblemDashboards lagged; stage visibility too coarse (8 stages)
  • ConstraintVerified webhooks only; no unauthenticated writes
  • StackLambda router · HMAC · Secrets Manager · structured logs
  • ResultStage tracking 8 → 12 · near-real-time reporting vs lagging sync

Interactive demo · simulated

Scheduled sync vs webhook events

Left: what leadership sees under a lagging scheduled sync — stale stage, coarse buckets. Right: the same deployment after webhook events — finer stages light up as status changes land.

Scheduled sync stale

Last sync · 47 min ago

Webhook events live
Event router log (simulated) idle

Case study 03 · Vectorize · Workers AI

Cite the shelf,
or don’t answer.

~58 teaching chunks · 30 searches/IP/hr · fail-closed on empty hits. Retrieve first, then advise.

Live demo · teaching shelf

Problem

Contract literacy for a small shop signing a vendor or client master services agreement needs grounded sources — not a model inventing clause meaning. This demo packages a teaching shelf pattern (contract-literacy retrieval) I've run on a private corpus stack; it is not a signed client deliverable.

How it was solved

Build a subject-scoped teaching shelf the agent queries before it acts. Embed with bge-base-en-v1.5 (768-d), retrieve top-k ≈ 5 clause snippets from Vectorize, and constrain the answer to cite-only hits. If retrieval is empty, the agent stops (fail-closed) and escalates instead of guessing. This demo is the same retrieval step an agent would run at the edge.

Agent-grounded RAG Teaching shelf Pre-advice retrieval Cite-only answers Workers AI embeddings Vectorize (768-d) Pages Functions Empty-hit fail-closed

Grounded retrieval before the agent advises — public packaging of a pattern running on private infrastructure; the live demo is the proof.

  • Corpus~58 teaching chunks · 768-d Vectorize · bge-base-en-v1.5
  • Rate limit30 searches / IP / hour · top-k ≈ 5
  • ConstraintRetrieve → cite only; 0 hits → fail-closed stop
  • ResultClause-aware hits or an honest empty set; never invented legal text

Interactive demo · simulated agent

Watch the agent retrieve before it advises

Pick a clause the agent is asked about. It issues a few real searches against the contracts corpus one at a time, shows what comes back, then writes a cite-only answer built only from those hits — or stops and escalates if nothing grounds it. Simulated agent, real retrieval. Not legal advice.

Agent · retrieve → cite (simulated) idle

Pick a clause above. The agent searches the corpus, shows the hits, then answers using only what it retrieved.

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.