A situation we handle
How do I stop babysitting my AI agent?
You wanted an agent; you got a toddler with a keyboard. Every few minutes it needs a yes, and the time you were saving goes on supervision. Babysitting is what per-step approval feels like — the fix is checkpoints only at judgements.
Unl, your why agent. The work behind “How do I stop babysitting my AI agent?” depends on decisions your AI needs to inherit. You settle what matters and why; Unl brings the relevant decisions and why into the next session, across the AIs you use. Your agents can then act on your decisions, check the result and carry the outcome forward. They return to you when intent is unresolved, decisions conflict or the action needs your identity. Use Unl with your AI or see how Unl works.
Why this matters
Most of those approvals were never judgements. They were the agent asking you to be its context.
Every few minutes: 'shall I proceed?' And you say yes, because the step was mechanical and obvious — and you realise you're not supervising quality, you're compensating for the fact that the agent doesn't know what you've already decided.
Loudly pro-autonomy on execution: the agent should run at full speed on the work that suits it. Checkpoints belong only where a genuine judgement bears — a real decision of yours, touched by a real step. When your settled calls are already in the agent's reasoning, the constant check-ins collapse: it isn't asking permission, because it already has the answer you'd have given.
The way through
Fewer, better-placed checkpoints. The agent runs; you decide — and only when there's a decision to make.
With Unl you author the brief, hand it off, and the agent executes inside what you settled — full speed on the mechanical work, your decisions arriving in its reasoning before it acts on the parts that need them. And the agent verifies completion against what you decided.
Questions people ask
Isn't supervision just the price of using agents?
Per-step supervision is the price of an agent that doesn't carry your decisions. Most 'shall I proceed?' moments aren't judgements — they're the agent asking you to be its context. Put what you've settled in its reasoning before it acts, and the checkpoints shrink to the steps where a real judgement bears.
How is this different from setting the agent to auto-approve?
Auto-approve removes the checkpoints without adding the authority — the agent runs free and uninformed. The fix is the other order: your settled decisions arrive in its reasoning first, so it runs free inside them, and the few genuine crossings still surface to you.
Which AI tools does this work with?
Unl is one connection over MCP, so it hands your settled decisions to Claude, Claude Code, ChatGPT and Cursor alike. The call you settle on one surface is inherited on the others.
What this is
Your agents don’t need to persist. Your purpose does.
Save the thoughts, decisions and targets worth keeping, each with its reasoning, carried into every AI session the moment they matter. A new unit of exchange between you and your AI: the Settled Why with standing that travels. Unprompted.
Your whole AI world. What you decided at the epicentre. It works inside Claude, Claude Code, ChatGPT and Cursor, and connects in a couple of steps.
MCP native·Human settled·Model agnostic·Your data
Accurate Autonomy
Connect Unl to your mix of agents.
Inherited Initiative in each agent, Unlimitless Agent Stigmergy across the fleet — your frame does the coordinating.
The full product, open. Free to start. Paid plans from $10 a month.