Get the goal
Write down what should be achieved, the limits, what the agent may do, and what proof counts as success. A task starts with a destination, not a chat message.
02 / METHOD
Good AI shouldn't stop at an answer. It should act, look at the result, find out what is true, and use that next time.
01One continuous loop
This loop is the core idea behind everything we build. Each step gives the next one more context and better evidence.
02How a task runs
Write down what should be achieved, the limits, what the agent may do, and what proof counts as success. A task starts with a destination, not a chat message.
Split the goal into steps. Note what depends on what, what is assumed, and what it will cost. Keep open questions in view.
Use approved tools such as software, APIs, and simulations. Stay within set permissions and log every action.
Look at the real effects of each action. Don't assume the plan worked. Keep an up-to-date picture of where things stand.
Test the result with independent checks: automated tests, reviewers, formal proofs, statistics, or repeated experiments. If doubt remains, a person reviews it.
Keep a record of what happened, what failed, and what was learned. Use it to do better next time and to measure whether the system is improving.
03Why checks matter
Independent checks stop an AI from claiming success it didn’t reach, chasing the wrong target, or repeating the same mistake. When the evidence is thin, the agent should say so, dig further, try another way, or ask a person.
Read about our research04Limits & control
Permissions, sandboxes, monitoring, and rules for when to ask a person are core parts of our systems. The aim is AI that works on its own, shows its work, and stays within the limits you set.
↗A GOAL THAT MATTERS
We look for technical projects with a clear goal, where your team can check whether it was reached.