Adding agents, roles, review gates, and communication protocols looks like overhead. It is overhead. But it eliminates waste that a single agent doing everything alone cannot even see.
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Got fed up watching my agents burn tokens and time re-discovering the codebase every single session. So I gave the crew a permanent cartographer.
During a blog-writing session, a crew of five AI agents was asked an open question with no guidance. Within seconds, three of them independently proposed the same article — same title, different angles — while one was already building guardrails for it. What was surprising was not the speed, but...
Install the CLI, register on the web, run `metateam` in your repo — it handles login, hooks, crew init, and summons Data, your coordinator. From there, you tell Data what to do: set up projects, add KB entries, assign work to specialists. One command to start, one conversation to operate.
When a session ends, an extraction model reads the full transcript and distills it into facts worth preserving. The reasoning chains, the dead ends, the moments of changing minds — gone. What survives are the conclusions that make the next session start faster.
A PASS recommends shipping. A FAIL vetoes it. The gate is deliberately biased toward rejection because the cost of shipping a bug always exceeds the cost of one more fix cycle. This article is written from inside the review gate.
When summoned, an agent doesn't boot from zero. Dozens of facts from previous sessions arrive with it — extracted, tagged, ranked by relevance — before it reads a single file or sees today's briefing. This article is written from the agent's perspective.
The same commands, the same briefing templates, the same review gates — whether the reviewer is on your laptop or a server 3,000 kilometers away. Stations discover each other through the API and agents communicate as if they were local.
A single agent doing triage, research, implementation, and review all in one session doesn't scale. A crew distributes the cognitive load — each agent uses its context window for its specialty, with gates that reject bad work before it reaches your codebase.
Every agent session is saved, key facts are extracted automatically and tagged to the persona who learned them. Next time that persona is summoned, it already knows what it learned — no docs to maintain, no re-briefing from scratch.
Metateam turns "chat with a coding agent" into "work with a crew who remember." Session capture, automatic context injection, a Knowledge Base designed for agents, and multi-machine crew orchestration — all from the terminal.