My AI agents.

My AI team, its targets and its results.

9 Agents/28 Sub-agents

Hours saved
20.9 h

Since 24 October 2026

Euros saved
€1,333

At €63.8 per hour

Today
8.8 h

€561, 40 actions

How the savings are calculatedEstimates awaiting validation

Each agent action is valued at the time it would have taken me. Calculation basis: €150,000 in annual revenue, 50 hours per week, 47 weeks, or €63.8 per hour.

The time I spend reviewing their work is deducted: 0.4 h so far.

Time estimates, awaiting validation

  • Outreach emails20 min
  • Outreach follow-ups5 min
  • CRM records updated3 min
  • Email replies drafted8 min
  • WhatsApp or LinkedIn messages sent3 min
  • Meeting reminders sent2 min
  • Tasks added from meetings3 min
  • Research tasks completed30 min
  • Bookings and purchases prepared15 min
  • Documents created60 min
  • Deliverables reviewed and improved15 min
  • Deliverables filed in iCloud2 min
  • Presentations and one-pagers120 min
  • LinkedIn posts written45 min
  • Reel scripts and captions45 min
  • Accounting receipts attached4 min
  • Expense reports prepared30 min
  • Digests and summaries sent15 min
  • Weekly plans prepared45 min
  • AI News editions published60 min
  • F feedback applied to agents10 min
  • Wiki consolidations30 min
  • Lines of code added2 min

The cockpit

Select an agent to explore their targets, tools and team.

  • Targets met7 of 28
  • Behind target21
  • No target2
  • Agents up to date9 of 9
Report from 27 October

Ola

Secretary

Everything is planned and organised in the calendar, and every participant is informed.

Joined on 25 October 2026

Ola, male character holding a notebook

KPIs

  • Behind target
    74 %

    Complete meetings (location or link, attendees, no clashes)

    Target: at least 90 %

  • Behind target
    2

    Calendar conflicts detected

    Target: at most 0

  • Behind target
    50 %

    Meetings with a reminder the day before or 1 hour before

    Target: at least 100 %

Tools

  • Google Calendar
  • WhatsApp
  • Gmail
  • Apple Mail
  • LinkedIn
  • Claude Code

Focus

No meeting without a location, attendees or a reminder.

Team3
  • rappels

    Sends a reminder the day before and 1 hour before each meeting.

    Active
  • revue-agenda

    Reviews the next 7 days of the calendar and flags missing details.

    Trial
  • creneaux-convenus

    Schedules the meeting once everyone agrees on a time.

    Trial

The organisation chart

I'm at the centre. Each agent has a team of sub-agents, and they hand work over to one another.

Nathan
Organisation chart: Nathan at the centre, 9 agents around him, their sub-agents and handoffs.Ola : rappels (active)Ola : revue-agenda (in trial)Ola : creneaux-convenus (in trial)Juo : todo-executeur (active)Juo : todo-reunions (active)Juo : reponses (active)Juo : todo-recap (active)Juo : week-prep (active)Nala : crm (active)Nala : radar (active)Nala : sourcing (in trial)Chop : content-friday (active)Chop : analyste (in trial)Chop : reseau (in trial)Chop : chop-invitations (in trial)Gry : collecte (active)Gry : pilotage (active)Mar : sante (active)Mar : testeur (in trial)Mar : veille (in trial)Mar : feedback (active)Mar : wiki-gardener (active)Mar : ai-news (active)Maya : compta (active)Maya : virements (active)Maya : maya (in trial)Teachy : teachy (in trial)Tex : mesure (active)NathanOla, SecretaryOlaJuo, Operations leadJuoNala, Sales leadNalaChop, Brand and content leadChopGry, EvaluatorGryMar, System coordinatorMarMaya, Accounting leadMayaTeachy, Agent trainerTeachyTex, Software engineerTex
Active sub-agentIn trialHandoffs

Hover over an agent to see their handoffs.

The handoffs

  • ChopNala

    opportunities from social media

  • NalaOla

    accepted meetings

  • OlaJuo

    bookings and logistics to handle

  • JuoNala

    sales opportunities found in the to-do list

  • Gryall agents

    measurement of time and money saved

  • Marall agents

    health, tests, coordination

  • Teachyall agents

    lessons of the week

  • JuoTex

    coding tasks

  • JuoMaya

    invoices and receipts received

The system

31 scheduled routines. Agent model: Claude Opus 5.5. Evaluator: a second Claude reviews every sensitive deliverable, without seeing the agent's work.

Each agent reports its KPIs. Gry compares them with the targets I set. Mar follows up with any agent that stops reporting.

The last 7 days

  • Juo

    Digests and summaries sent

    4
  • Juo

    Documents created

    3
  • Juo

    Deliverables filed in iCloud

    6
  • Juo

    WhatsApp or LinkedIn messages sent

    10
  • Juo

    LinkedIn posts written

    2
  • Juo

    Research tasks completed

    1
  • Juo

    Email replies drafted

    2
  • Juo

    Bookings and purchases prepared

    1
  • Mar

    F feedback applied to agents

    2
  • Mar

    AI News editions published

    1
  • Nala

    Digests and summaries sent

    1
  • Nala

    CRM records updated

    3
  • Ola

    WhatsApp or LinkedIn messages sent

    2
  • Ola

    Meeting reminders sent

    2

  • Juo

    Digests and summaries sent

    5
  • Juo

    Deliverables filed in iCloud

    11
  • Juo

    Deliverables reviewed and improved

    1
  • Juo

    Weekly plans prepared

    1
  • Juo

    Bookings and purchases prepared

    1
  • Mar

    Wiki consolidations

    1
  • Mar

    F feedback applied to agents

    1
  • Mar

    AI News editions published

    1
  • Nala

    Digests and summaries sent

    1
  • Nala

    CRM records updated

    4
  • Ola

    Meeting reminders sent

    1

  • Juo

    Documents created

    3
  • Juo

    WhatsApp or LinkedIn messages sent

    5
  • Juo

    LinkedIn posts written

    1
  • Juo

    Research tasks completed

    1
  • Juo

    Email replies drafted

    2
  • Juo

    Bookings and purchases prepared

    4
  • Juo

    Tasks added from meetings

    1
  • Mar

    AI News editions published

    1
  • Nala

    Digests and summaries sent

    1
  • Nala

    CRM records updated

    3

System growth

One measurement per day.

Team size
AgentsSub-agents
0255025 Oct27 Oct25 Oct : 6 (agents)26 Oct : 9 (agents)27 Oct : 9 (agents)925 Oct : 21 (sub-agents)26 Oct : 28 (sub-agents)27 Oct : 28 (sub-agents)28
View data
DateAgentsSub-agents
25 Oct621
26 Oct928
27 Oct928
Cumulative hours saved
012.52525 Oct27 Oct25 Oct : 12.4 h (hours saved)26 Oct : 20.9 h (hours saved)27 Oct : 20.9 h (hours saved)20.9 h
View data
DateHours saved
25 Oct12.4 h
26 Oct20.9 h
27 Oct20.9 h