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Autonomous AI agents increasingly act across organizational boundaries on behalf of human operators: they invoke third-party services, delegate subtasks to other agents, and pay for metered resources. Deploying such agents safely requires five capabilities that today live in separate systems: persistent identity, scope...
Enterprise AI agents often succeed in a demonstration and then stall once they must operate day after day. An industry report estimates that most pilots never reach production and that deployed systems rarely retain feedback or improve over time, while agent benchmarks show single-run successes masking unreliable repet...
ARISMA treats AI as an inspected, benchmarked, logged, and reversible assistant rather than an autonomous reviewer, built around one governing principle: every consequential scientific decision must remain human-interpretable, human-auditable, and human-accountable.
Three building blocks toward a complete, privacy-preserving browser agent, a verified repair instrument pairing a trilingual seeded-violation benchmark with an audit-inject-verify loop, and a dual-condition protocol that measures harm as carefully as benefit are contributed.
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