I built a tiny AI company that runs on my laptop (multi-agent application)

Published: (April 27, 2026 at 08:18 PM EDT)
2 min read
Source: Dev.to

Source: Dev.to

Introduction

Lately I’ve been thinking about what “work” might look like in a world of AI agents. I was talking with my wife about it — maybe in the future, instead of replacing people one by one, it becomes one person managing a small team of AI workers. Tools like Claude Code or OpenClaw felt way too technical for her, so I started wondering: what if this actually felt like working with a team? Not writing code, not wiring workflows, but hiring, managing, and collaborating.

Holons

I built Holons, a desktop‑first system (Tauri + Flask + React) where:

  • agents have roles and identities
  • a “lead” agent assigns tasks and builds workflows
  • multiple agents can collaborate in group chats
  • everything runs locally (or self‑hosted)

You can say something like:

“Create a pitch for a B2B AI accountant”

and Holons will:

  1. propose a workflow
  2. assign tasks to different agents
  3. estimate cost
  4. let you run the whole thing

What surprised me

Visibility

The hardest part wasn’t the agents; it was visibility. Once multiple agents are running, you need to understand:

  • what they’re doing
  • how much they cost
  • how they behave over time

Unified ledger

I made an early design decision: every LLM call writes to a unified ledger. Each call tracks:

  • model + provider
  • tokens (prompt / completion)
  • cost
  • latency
  • agent + user

That single table powers:

  • cost dashboards
  • usage quotas
  • reports
  • audit logs

Key features

  • Multi‑provider support: Bedrock, OpenAI, Anthropic, Gemini, MiniMax
  • Per‑agent model binding (different agents can use different models)
  • pgvector‑based RAG + external knowledge integrations
  • MCP‑style tool integration
  • IM channels (Telegram / Slack / LINE)

Still early

This is very much an experiment. I’m not sure yet if this is the “right” abstraction for multi‑agent systems—more like managing a team, less like calling an API.

I’m curious how others are thinking about multi‑agent systems. Are people actually using them in real workflows, or are we still figuring out the right model?

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