OpenAI introduced LifeVal, an internal evaluation of how well models complete everyday tasks such as finding childcare, budgeting, shopping and accessing government services. It drew on time-use surveys and a demographically representative study of about 4,000 people to pick tasks where people most want help. The task set and grading rubrics are not released as a public leaderboard. OpenAI says it used the measure over the past year to improve models on browser use, computer use and connectors.

What it means: Classic academic tests measure knowledge; LifeVal tries to measure whether an assistant finishes practical workflows with actions, not only advice. That matters for products sold as personal agents: without such measures, progress stays anecdotal. Keeping the task set internal limits independent scrutiny and makes head-to-head comparison with rivals harder. For businesses and public bodies in Greece evaluating automation tools, the lesson is to demand scenarios close to their own processes — enrolments, appointments, form filing — with clear success criteria and human confirmation. High scores on curated examples do not prove safety against bad inputs or deceptive websites. Organisations should pair capability scores with agent-security tests, tool limits and action logs. Regulators and consumer groups will also want clearer performance claims when vendors market a “personal assistant.” LifeVal is a step toward measuring what people actually ask for help with, but buyers should still run their own acceptance tests before wiring agents into money movement, identity or health workflows.

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