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Contents

1. Revenue systems

2. Builds

3. The operator

4. Writing

5. Journal

6. Resume

7. Appendix

229 wordsby CONNOR J. LAUGHLIN

Governed AI operating layer.

A multi-agent layer that stages GTM work behind human approval gates and audit trails.

Outcome

Compressed RFP/RFX triage from a day-scale workflow to a minutes-scale workflow. Material productivity lift from a governed AI operating layer.

Approval gate pathThe proposal triage workflow: intake, classification, retrieval, drafting, a human review gate that holds the work, and release. Every stage writes a row to the audit log drawn beneath the path.INTAKECLASSIFICATIONRETRIEVALDRAFTHUMAN REVIEW GATERELEASEAUDIT LOG
FIG_009 [ Approval gate path ]Every stage of proposal triage writes to the audit trail before it hands off.

I taught myself to code, then built the automation I kept asking someone else for. Proposal triage, research, competitive intelligence, content, and executive reporting all run through agents that stage the work. Humans approve it. Every run leaves a trail somebody can audit.

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. 04What it proves

    I wrote the code and led the function at the same time.

Proof

Chapter figures

governed AI operating layer
Multi-agent. governed AI operating layer. Agents stage GTM work behind human approval gates and audit trails. The agent count is gated..
productivity from the governed AI layer
Material lift. productivity from the governed AI layer. Team output against the pre-system baseline. The exact multiple is gated..
proposal triage and response
Days to minutes. proposal triage and response. Triage compressed from a day-scale workflow to a minutes-scale workflow. Percentages stay gated..

Systems built

  1. Agentic workflow design with human review gates
  2. RAG knowledge base for governed RFP and outbound drafting
  3. Governed LLM wiki on Karpathy's compile-once pattern, read by humans and agents
  4. Voice standards, source discipline, audit logs
  5. Prompt libraries and regression-check loops
  6. MCP-style tool integrations and n8n orchestration

Chapter 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

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.
Email Connor

Open to VP of Marketing & GTM, Head of GTM, VP Revenue Operations, and GTM engineering roles at AI-native B2B SaaS and PE-backed growth companies that build the tools of change. Chicago, hybrid or remote.