One purpose, many agents

Unl, the next generation
of AI-native.

The complexity doesn’t disappear. You stop having to carry it.

AI inside software

You operate the product.

The assistant helps inside a tool. You still carry the direction between tools, agents and sessions.

Your purpose inside AI

You direct the undertaking.

Unl keeps what you decided and why. Your agents inherit what matters, act within that direction and report what changed. Your AI already knows how to reason. Unl gives it your reason.

Unl is built with Unl.

A judgement in ChatGPT. A different page on this site.
Watch the reasoning reach the work.

The Unl portal's terminal: each turn, the AI that asked, what Unl handed it and why, and the decisions it lit.

The Unl terminal, replaying four turns from two different AIs. In each, Unl answers the AI in its own words: which of your decisions bears on the turn and why, or that nothing does. The decisions it handed over fold beneath. On the right, the latest turn's decisions with their reasons.

Run it in any terminalnpm install -g unlimitlessthenunl

Your own stream runs in your terminal and your Unlimitless portal. See where every why came from, and watch your agents stay true to it.

The missing role in the emerging fleet.

Loops

Six real decisions,
carried into the work.

Each Loop opens in turn.

Loop 001
A sentence said in ChatGPT at 10:21 was live on the platforms page by 14:51, built by Codex.
Loop 003
Asked once in ChatGPT, the connect page put every tool in one place the same evening.
Loop 004
Said in ChatGPT that a homepage picture said too little. It was gone 82 minutes later.
Loop 007
A yes in Claude, and 42 minutes later no agent could throw away work it did not make.
Loop 008
Asked in Claude if Unl was ready for new people. That evening a new person's decision began carrying into their next AI.
Loop 009
Said in Claude that the homepage should show the real terminal and six Loops, not one. Built 22 minutes later.

The work adds up

One purpose.
Many agents.
Real output.

Individual Loops show how a decision becomes work. The operating cadence shows what happens when that reasoning keeps carrying across agents.

Agents started: 794
Cumulative agent work: 6d 4h
Pull requests: 142

Snapshot reported 8 October 2026, for work since 4 October 2026. Cumulative agent time can overlap. These are figures we report ourselves, not independently verified counts or a throughput promise.

The human sets the direction and makes the new decisions. Agents take work, carry it through checks and return the evidence so later agents can continue.

Who carries what

The conversation
is where you direct.

You carry the purpose

Set the target. Make the decisions and give their reasons. Change direction when the evidence calls for it.

Agents carry the work

Each agent gets the why that bears on its task. It reasons, acts and reports the result. A later agent can continue from the same direction.

Software carries the machinery

Your orchestrator, analytics and infrastructure still do their jobs. Unl adds the shared reasoning that lets changing agents work toward the same purpose.

Your agents can change.
The undertaking carries on.

Models change. Sessions reset. Your decisions shouldn’t.

This is how we build Unl with Unl. The terminal shows real, sourced work on Unl itself. It shows how the work runs, not a promise that every control we use is available to every customer.

Start with the decisions and reasons your agents can inherit today. Keep your tools. Let the reasoning carry between them.

How the why reaches the work ↗Build with Unl ↗Read as Markdown ↗

Unl, the next generationof AI-native.