Federico Jan

Enterprise AI Agent Architect · GTM Engineer

Federico Jan

I design and deploy AI agent systems for real business operations.

Most recently, I was the first engineer and sole agent architect behind REMI, Marketing.MBA's AI GTM operating system—taking agents from architecture into employee workflows, production operations, and real business constraints.

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Portrait of Federico Jan
Federico JanAgent Architect · GTM Engineer
Tokens in production
10B+
Frameworks in production
5+
Agents developed
20+
Agent runs traced
Thousands

Selected work

One flagship platform. Two focused systems.

All work →
  1. Flagship systemREMI: Building an Enterprise Multi-Agent GTM Operating System

    The agent platform behind production GTM work: an internal agent factory, structured company context, long-running specialist teams, versioned skills, observable execution, approval gates, and business integrations.

    • 10B+ tokens
    • 5+ frameworks
    • 1h+ runs
    Read case study
  2. Building an Agent-Native SEO Department

    A manager agent and three specialists turned Airtable briefs into human-approved, production-ready Framer articles — including research, long-form copy, SVG infographics, video embeds, and internal links.

    • Agent-native SEO
    • 4-agent system
    • ~90 min/article
    Read case study
  3. Ask Your Org: A GitHub-Native Company Brain

    A governed organizational knowledge system that turned scattered company context into a navigable GitHub structure with explicit sources, ownership, freshness, and escalation rules.

    • GitHub-native
    • Provenance-aware
    • Org intelligence
    Read case study

Public blueprint

Make your organization legible to AI agents.

The public Ask Your Org Blueprint turns company context, source ownership, permissions, and escalation into a practical three-layer architecture.

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Operating standard

Enterprise-grade means surviving the business.

The hard part starts after the demo: observability, long-run reliability, systems integration, and adoption by the people doing the work.

Production Agent Operating LoopSense → decide → act → measure
Production Agent Operating LoopMarket and customer signals move through manager and specialist agents, cross a human approval gate, become operational action, and return as traced outcomes for the next run.PRODUCTIONLOOPTRACED, NOT ASSUMEDSenseMarket & customer signalsDecideManager + specialist agentsActHuman-gated executionMeasureTraced outcomesProduction Agent Operating LoopA compact mobile view of the sense, decide, act, and measure production loop.PRODUCTIONLOOPTRACED, NOT ASSUMEDSenseMarket signalsDecideAgent teamsActHuman-gatedMeasureTraced outcomes
Signals enter the system, agents coordinate, humans retain authority at consequential edges, and traced outcomes improve the next decision.
  1. 01

    Instrumented

    Thousands of runs traced from the beginning, so failures became engineering evidence.

  2. 02

    Long-running

    Agent workflows designed to stay coherent and stable for more than an hour.

  3. 03

    Integrated

    CLIs, APIs, knowledge systems, approval gates, and production GTM workflows.

  4. 04

    Forward-deployed

    Built alongside employees and tested against real incentives, friction, and constraints.

See the operating system behind the work →

Field notes

Latest writing

  1. How to Build a Minimal AI Publishing Pipeline as a Founder

    Build a minimal AI publishing pipeline: use local markdown, add a CLI approval gate, separate prompts, add fallbacks, and only publish verbatim quotes.

    Read article

Building—or hiring for—production AI?

Open to AI leadership roles and select technical advisory work on systems that need to survive real business operations.

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