Artificial intelligence alignment studies how to make model behavior match intended goals, values, and safety boundaries. It includes training methods, evaluations, oversight, interpretability, access controls, and governance. The challenge is not only stating a goal but ensuring the system generalizes it rather than exploiting an imperfect proxy.
Agentic and multi-agent systems add persistence, tool use, communication, and strategic adaptation. Alignment work must therefore evaluate behavior over long tasks and interactions, including reward hacking, hidden coordination, attempts to alter oversight, and situations where immediate incentives conflict with broader intent.
