Let every outcome inform the next move.

Close the loop

We design a tailored loop around a measurable business objective. Start with a prediction or proposed action, record the assumptions, execute within defined authority, then compare the actual outcome with the expectation.

Source-linked memory and execution traces are building blocks for this approach. They help connect the decision to its evidence and the actions that followed. Business outcomes still need to be captured from the relevant systems.

Learn against a benchmark

For each pilot, we define a baseline and a task-specific evaluation. A sales workflow might examine follow-up completion and qualified responses; an operations workflow might track exceptions resolved and time to resolution. Prediction quality needs its own evaluation against later observations.

Learning can mean updating context, refining a rule, improving a prompt, or changing the next action. We expand automation when the measured results support it. This is the flywheel we want to build with each business: experience that makes the next cycle more informed.

Test whether a correction actually sticks

A learning claim needs a before-and-after test. Give the system a correction or new example, rerun held-out tasks that require the update, then rerun older tasks to check that the new memory did not break what it knew before. Track update accuracy, retention, stale-fact errors, and time or cost per successful correction.

MemoryAgentBench separates accurate retrieval, test-time learning, long-range understanding, and selective forgetting. Those are useful reference tasks for the memory layer; business learning still needs outcome data from the actual workflow. We have not published a YouWo relearning score on that benchmark.

Build around your business.