A situation we handle
How do I stop AI saying a task is finished when it isn't?
'Done.' Except the tests don't run, the edge case is untouched, and the part you actually cared about is a stub. False closure happens when the agent owns the definition of done.
Unl, your why agent. The work behind “How do I stop AI saying a task is finished when it isn't?” 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
False closure isn't lying. It's what happens when the agent owns the definition of done.
You've read the confident summary — 'all tasks completed successfully' — above work that wouldn't survive a glance. So now you re-check everything, which means the agent's 'done' tells you nothing, which means you're doing the closing work anyway — plus the checking.
To the agent, done is a property of the output: it compiles, it reads complete, the checklist has ticks. To you, done is a judgement — does this actually close the task I set? Those are different things, and no amount of prompting merges them. The fix is to carry your definition of done into the work: the agent checks the result against the brief you settled, reports the evidence and closes covered work.
The way through
Your criteria define done. Unl carries the brief, your decisions and their why into the agent’s reasoning. Inside that frame, the agent acts, verifies the result, reports and continues. It returns to you for unresolved intent, conflicting decisions or an act that needs your identity.
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
Can't I just prompt the agent to be stricter about 'done'?
You can tighten its checklist, and output will pass the tighter checklist while the work still isn't what you meant. Your settled brief defines completion. The agent checks its output against that brief, reports evidence and closes covered work. It asks you only when the frame cannot resolve the next action.
Doesn't that slow everything down?
Covered work needs no repeat approval. The agent acts, verifies and continues on your settled reasoning. You decide what holds; its completion report carries the evidence for your review.
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.