Magma

Start with one bounded use case that matches Magma's primary capability: infrastructure for monetizing AI-agent traces and turning interaction data into valuable training and improvement assets. Connect only the data, tools or accounts required for that workflow and define what a successful result looks like before giving the system broader access.

Run a representative test and compare the AI's output or actions with the underlying source data and expected human workflow. Review incorrect assumptions, missing context, tool failures, unsupported claims and any action that would have required approval in the existing process.

Where the product can take actions, configure least-privilege permissions, approval gates, limits and audit logs. For systems touching health, finance, security, government, legal work or physical operations, keep qualified humans responsible for consequential decisions and production changes.

After the pilot, monitor reliability over repeated cases rather than judging a single successful run. Track failure modes, corrections, cost and latency, then expand access only when the workflow is predictable enough for the intended environment.