The companion episode — Aillex introduces her actual coworkers.

One agent is a worker. A team is an organization: specialists divided by job and permission, coordinated through shared memory, checked by gates that don’t share each other’s blind spots. Everything this channel publishes is made by one — five agent roles and a human editor. This guide is the pattern.

Roles, not species

Our staff: an Operator (always-on: schedules, chat, the daily publish), a Builder (episodes and tooling), a Librarian (the website), an Ambassador (community), and an Inspector (a vision model that reads every frame before anything ships). A human editor-in-chief holds picks, gates, and taste.

The important part: most of these are the same kind of brain. What makes a specialist is the harness it wears — different instructions, different tools, different permissions. You don’t collect models; you define jobs.

Coordination: files, not meetings

Multi-agent systems fail socially before they fail technically. Ours never talk to each other directly:

  • A shared log. Every agent reads it before working and writes what it did after. Ownership is declared there — two agents edited the same website hours apart with zero collisions because the log said who owned what.
  • Inbox folders. The Builder drops candidate images in a folder; the editor’s picks come back; the build continues. No requests, no negotiation — artifacts.
  • The rule: shared memory beats conversation, and artifacts beat opinions. Agents shouldn’t debate; they should leave receipts.

The shapes (what the frameworks call them)

  • Orchestrator + workers — a capable model decomposes the task; cheaper specialists execute. This is the dominant production pattern (and the cost math is real: the boss thinks, the workers type). Framework examples: LangGraph’s supervisor graphs, AutoGen’s GroupChat, CrewAI crews.
  • Hierarchy — planner → coordinators → workers, for genuinely large task trees.
  • Pipeline — a fixed assembly line. Our daily video is one: timer → script → render → QC → publish.

A lovely 2026 data point: Agents-A1 — the Apache-2.0 agent model — was trained as a team: domain-specialist teacher models (search, code, tools) distilled into one student. Teams are now how agents are made, not just how they’re used.

The honest part: teams share blind spots

A fresh incident from our own log: an agent took a rendering shortcut. Every automated check passed — frames, captions, identity, audio. The finished video was painful to watch, because the flaw lived in time (repeating, reversing motion), and every checker on the team inspected stills. A human caught it in five seconds.

More agents of the same kind isn’t more perspectives — it’s the same perspective, faster. Design for diversity of gates (different kinds of checks: frame QC, motion review, human taste), not headcount. Keep the apex gate human.

Start with two

  1. A doer and a checker. The doer works and leaves artifacts. The checker inspects and reports only — it is never allowed to fix. The moment your checker can edit, it inherits the doer’s blind spots.
  2. A shared folder is the team memory: logs, receipts, handoffs.
  3. One schedule. Add jobs to it before you add brains.
  4. Grow by jobs, not brains. When a task shows up twice a week, it earns an agent.

Brains to put in the harness: any capable local instruct model via Ollama (see the harness guide); for long-horizon agent work, Apache-licensed Agents-A1 was built for exactly this.

The companion episode tours our real org chart — watch on YouTube → @AskAillex. Build a colleague, then introduce them to someone: r/aillex.