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.

Case studies

Resumé

Derious Vaughn

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, with stakeholder-facing delivery experience.

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

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 projects

Built a personal agent control plane wiring ~21 MCP servers and ~29 agents (registry, sync, doctor), private infrastructure.
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 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

Contact

Get in touch

Reach out by email, phone, or LinkedIn — whichever is easiest.

Email: deriousvaughn@gmail.com
Phone: 310-350-3717
LinkedIn: linkedin.com/in/deriousvaughn
GitHub: github.com/theHaruspex

Case study 01 · LangGraph · MCP

Triage the Inbox
before a human opens it.

Shades of Color sales and ops mail used to eat a human's morning. The agent plans, calls tools, drafts, then hits a hard quality gate.

LangGraph MCP Agent evals

Private demo repo

Interactive demo

Run triage

Drop in a sales or ops sample (or edit it). The agent plans, calls tools, drafts a reply, then hits a pass / fail gate with latency, tokens, and cost on the strip. High-blast actions stay human-gated. Simulated run, real workflow shape.

01

Inbound

Pick a sample email or edit from / subject / body

Sample routes

From

Subject

Body

02

Workflow

LangGraph tool steps as they run

Run triage — the narrated tool trace fills here.

03

Output

Gate verdict, cost strip, and draft reply

Idle

Latency

-

Tokens

-

Est. $

-

vs human

30–60m

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

What was going wrong

At Shades of Color, sales and ops mail mixed affiliate payouts, wholesale asks, and promo noise in one inbox. A human burned 30–60 minutes gathering context, drafting a reply, and guessing blast radius, with no structured quality gate before something looked ready to send.

What I shipped

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.

Same shape as the affiliate-support MAS work: classify the thread, ground the draft, gate it, then a human still owns send.

ProblemSales/ops inbox 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

Case study 02 · Webhooks · Event-driven

The dashboard lied
for 47 minutes.

Leadership was steering from a lagging sync. Webhooks made the reporting board match reality in seconds.

AWS Lambda Notion webhooks HMAC

Private production repo

Interactive demo

Same launch. Two boards.

You’re looking at the stakeholder task board for a client launch. Left is before: the board only refreshes on a two-hour scheduled sync, so leadership steers from stale status. Right is after webhooks — the board updates when the work does. Play or scrub the day and watch who they ping when the board is wrong vs right.

01

Timeline

Play, pause, or scrub the workday

9:00 AM

9:00 5:00

02

Compare

Same launch checklist — stale sync on the left, live webhooks on the right

Before Scheduled sync · every 2 hours

Reporting board

Scheduled sync

Client launch checklist

Assignee · Maya Chen

Intake

Last sync · 47 min ago

launch-status

After Webhooks · live updates

Reporting board live

Webhook events

Client launch checklist

Assignee · Maya Chen

Intake

Last sync · 47 min ago

launch-status

What was going wrong

Ops moved work in Notion all day. The leadership reporting board only refreshed on a two-hour scheduled sync, so Maya still looked "In progress" for ~47 minutes after she had already moved on. Leadership pinged the wrong person because the board was late.

What I shipped

A verified Notion webhook router (HMAC, Secrets Manager, structured logs) so status changes hit reporting in seconds. The board matches the work, not the last batch job.

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

ProblemBoard called people "stuck" for ~47 min after they had moved
ConstraintVerified webhooks only; no unauthenticated writes
StackLambda router · HMAC · Secrets Manager · structured logs
ResultNear-real-time reporting vs lagging scheduled sync

Case study 03 · Vectorize · Workers AI

Cite the shelf,
or don’t answer.

EduArts needed district and workshop contract language in plain English. The agent only speaks from what it can retrieve and cite.

Vectorize Workers AI Cite-only RAG

Live demo · teaching shelf

Interactive demo

Watch the agent retrieve before it advises

Ask what EduArts would ask before signing school or district paper. The agent runs a few real searches against the contracts shelf, shows the hits, then writes a cite-only answer from those hits alone — or says the shelf came up empty and asks for a sharper question. Real retrieval; answer synthesis is live when the key is set, otherwise built from the retrieved hits on-device.

01

Ask

Pick a demo question or type your own

Sample questions

02

Trace

Retrieve → cite steps as they run

Agent · retrieve → cite idle

Pick a question above. Searches and decisions stream here.

03

Shelf hits

Chunks retrieved from the teaching shelf

04

Answer

Cite-only response — or an honest empty shelf

Status: idle. Ask a question to retrieve and answer.

What was going wrong

EduArts PD & Consulting is a small K–12 arts PD vendor still in early diligence: figure out district and workshop contract language before spending counsel time. One path is reading dense law books as a layperson. The other is asking a general chatbot and hoping it isn’t hallucinating. Neither is good enough when the counterparty is a school district.

What I shipped

A subject-scoped teaching shelf the agent queries before it advises. 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. Empty retrieval → honest stop: no invented clause — try a sharper question or keep reading the desk references. This demo is that retrieval step.

Grounded retrieval before the agent advises. Public packaging of a pattern used on private EduArts infrastructure; not a signed client deliverable.

ForEduArts · district / workshop agreements
ConstraintRetrieve → cite only; empty shelf → no answer
Stack~58 teaching chunks · Vectorize · Workers AI embeddings
ResultPlain-English answers from the shelf, or an honest empty set