CJLConnor J. Laughlin
IndexFile
22-AGENT AI GTM OS

22-Agent AI GTM Operating System

A 4-person team needed the operating capacity of a much larger department.

  • AI GTM systems
  • Executive marketing leadership
1970s mainframe terminal with code on the CRT and a continuous-form printer extruding a log.
Fig. 35AI operating system · Terminal under load

TLDR · 90 seconds

22-agent
AI marketing operating system
400%
productivity uplift
96%
RFP response-time reduction
What it proves

I can lead the function and build the technical layer myself.

The case

  1. 01The problem

    A small team had to support content, demand generation, proposals, sales enablement, reporting, and brand work across 5 regulated verticals.

  2. 02What I built

    I self-taught Python, TypeScript, prompt engineering, and agentic workflow design to build a governed AI operating layer for GTM work: research, campaign briefing, content adaptation, RFP and RFX support, competitive intelligence, and executive reporting.

  3. 03What changed

    The team got a repeatable production system with review gates, voice standards, source discipline, and faster output across high-context work.

  4. 04Why it mattered

    AI became an operating system with quality control, not a shortcut around judgment.

  5. 05What it proves

    I can lead the function and build the technical layer myself.

Proof

  • 22-agentAI marketing operating systemresearch, content, RFP, intel, executive reporting
  • 400%productivity upliftoutput measured against pre-system baseline
  • 96%RFP response-time reductiongoverned RFP workflow with human approval
  • 24.5 hrs/wkteam time recapturedweekly hours redirected from manual work to strategy
  • 115+strategic deliverables in 60 dayscontent, RFP, intel, enablement

Systems built

  • Agentic workflow design with human review gates
  • RAG knowledge base for governed RFP and outbound drafting
  • Voice standards, source discipline, audit logs
  • Prompt libraries and regression-check loops
  • MCP-style tool integrations and n8n orchestration

Quick details

Scope

Research → brief → draft → approve → publish → audit.

Stack

Python • TypeScript • RAG • n8n • LLMs • MCP-style tools

Governance

Human approval gates • audit logs • drift and regression reviews

Artifacts

Pages from the work. Redacted where it has to be.

Library card catalog as a RAG knowledge base index with retrieval paths and a vintage retrieval console.
Fig. 36RAG knowledge base index · KB-INDEX
AI workflow audit log tracking run IDs, prompt versions, exceptions, and model drift.
Fig. 37AI workflow audit log · Fig. 22
Governed RFP approval gate checklist with audit trail and approval-required stamp.
Fig. 38RFP approval gate · Fig. 20

Governance notes

  • Source packet and workflow map shared before exact architecture images go public
  • Every AI artifact has an approval gate and an audit trail
  • Drift reviews and regression checks log error tags and prompt updates

In the interview

I built an AI operating system for a small team that needed the output of a much larger department.

In a working session

A walk-through is the better unit. I will show redacted artifacts: process maps, KPI dictionaries, reporting packs, automation logs.

CONFIDENTIAL
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